Daniel Rittel

H Maigre

A study

come

Introduction

The effect of mode-mix on crack initiation (toughness and trajectory) is well documented both theoretically and experimentally for quasi-static fracture [ 1]. Dynamic fracture (single or mixed-mode) analysis is generally more complicated since inertial effects cannot be neglected [START_REF] Freund | Dynamic Fracture Mechanics[END_REF].

Recent (mode II) shear impact experiments in steel and polycarbonate have shown the existence of fracture mode transitions (brittle to ductile. including shear band formation) as a function of the impact velocity [START_REF] Kalthoff | Shadow optical analysis of dynamic fracture[END_REF][START_REF] Chandar | On the failure mode transitions in polycarbonate under dynamic mixed mode loading[END_REF][START_REF] Zhou | Dynamically propagating shear bands in impact-loaded prenotched plates[END_REF].

Here we report experimental results for mixed-mode dynamic crack initiation in commercial PMMA as a model for brittle materials. The two-dimensional combination of a major and a minor mode (1-11 or 11-1 respectively) was systematically achieved using two distinct specimen geometries impacted at various velocities. The results show that, regardless of the dominant loading mode, the initial kink angle between the crack and the original notch line increase with the impact velocity. These results are described and discussed in terms of a simple criterion.

General framework

The history of the dynamic stress intensity factor(s) (SIF) can be determined by hybrid experimental-numerical techniques relying on the use of path-independent integrals or dynamic weight functions [START_REF] Freund | Dynamic Fracture Mechanics[END_REF][START_REF] Bui | Facteur d'intensite des contraintes tire des grandeurs mecaniques globales[END_REF][START_REF] Bui | A new approach to the experimental determination of the dynamic stress intensity factor[END_REF]. In these reports, the dynamic SIPs are determined either from the experimental forces or from the experimental displacements.

Experimental investigation of dynamic crack initiation under single loading mode (e.g. mode I) can be achieved by keeping both the specimen's geometry and the loading I LMS Ecole Polytechnique, 91128 Palaiseau, Cedex France symmetrical (see, e.g .• [START_REF] Giovanola | Investigation and application of the one-point-bend impact test[END_REF]). The lack of symmetry will cause (often unwanted) mixed mode to occur [START_REF] Kalthoff | On the nonsymmetry of the loading condition in lzod-type impact test configurations[END_REF]. On the other hand, systematic investigation of mixed-mode dynamic crack initiation can be achieved provided the crack-tip fields can be accurately determined ( 1 0].

Fracture time, is a key parameter which can be determined from fracture gage readings. For linear-elastic solids (brittle fracture), it has recently been shown that additional information is gained by using the reciprocity between the force and displacement based formulations From Fig. 2a it can be noted, as expected, that the mode II is minor although it may reach significant values. Fig. 2b shows that the mode I component is essentially negative in accordance with previous reports [START_REF] Chandar | On the failure mode transitions in polycarbonate under dynamic mixed mode loading[END_REF][START_REF] Lee | Fracture initiation due to asymmetric impact loading of an edge cracked plate[END_REF].

The initial kink angle was measured locally from magnified pictures of the notch-tip. This measurement was delicate in several cases where the initial kink curves continuously. Yet it was observed that the macroscopic crack angle values can be considered as upper bound values with respect those obtained by local measurements. In all cases the initial kink angle increases with the impact velocity regardless of the dominant mode, as summarized in Table I. The same trend was observed for the absolute values of the SIFs.

The reported results represent a systematic attempt to explore the two extremes of I-II mode mix using the same experimental and theoretical methodology. To explain the observed kink angle-velocity relationship, one can invoke the maximum energy release rate criterion.

For positive mode I this criterion is a maximum normal stress ( creel criterion [START_REF] Nuismer | An energy release rate criterion for mixed mode fracture[END_REF].

Yet when the mode I component becomes negative, it can no longer be applied and one can reasonably assumed that the crack will dissipate energy through a shear mechanism [START_REF] Broberg | Workshop on Dynamic Fracture[END_REF]. This amounts to searching for the angle at which the shear stress (cr re ) is maximal.

The crack will ultimately select the initial kink angle corresponding to the maximum energy release rate, be it by combined opening + shear, or by shear alone. The stresses are determined from the experimental values of the SIF's [START_REF] Freund | Dynamic Fracture Mechanics[END_REF]. A detailed description of the criterion will be presented in a subsequent paper. Next, the experimental results are analyzed using the proposed guidelines. In this case, we do not look for a prediction of the initial kink angle but rather use observed values in conjunction with the criterion's predictions. Fig. 4 shows a sharp transition in the kink angle values from low values (typically I 0°) corresponding to a shear mechanism to high values (typically 65°). By comparing the bulk fracture times and the observed kink angles, it appears that at lower impact velocities, the shear specimens fail by a shear mechanism (mode II). By contrast mode I becomes energetically favorable at higher impact velocities and the initial kink angle is thus larger [START_REF] Chandar | On the failure mode transitions in polycarbonate under dynamic mixed mode loading[END_REF]. Therefore, the maximum energy release rate will dictate which of mode I or II crack initiation mechanism will operate.

Despite a certain simplification, as we deal with notches rather than sharp cracks (without frictional effects [START_REF] Broberg | Workshop on Dynamic Fracture[END_REF]), it seems that the present approach can predict the effect of the impact velocity on the initial kink angle. I). I).

Conclusions

[

  II]. Bulk fracture is thus defined as the time at which the evolutions of the SIF. determined using once the experimental forces and once the experimental displacements, diverge. Bulk fracture time is a useful complement to gage readings which generally precedes them by a few microseconds. Experimental We use an instrumented bar to apply and measure the transient forces and displacements applied to the specimen. As the striker impacts the bar a compressive pulse propagates towards the specimen. The pulse is then partly reflected-partly transmitted at the specimen bar interface [ 12]. Since the applied loading is unsymmetrical, mode mixity develops [I 0]. The setup for dominant I (CCS-Compact Compression Specimen) or II mode (shear) experiments is depicted schematically in Fig. I. Results & Discussion In this series of experiments, direct calculation of the SIFs was carried out using the dynamic weight function method. For each mode, we calculated the SIF from the convolution of the experimental force (or displacement) with the calculated response of the specimen subjected to a unit (dirac) force (or displacement) pulse. Typical evolutions of the I and II mode SIFs are shown in Figs. 2a and b for CCS and shear experiments respectively.

Fig. 3

 3 Fig. 3 describes the evolution of the kink angle as predicted from the criterion for two different velocities for mode I experiments. It can be noted that at times corresponding to bulk fracture, the predicted and observed angles are in good agreement. Here the operative mechanism is clearly mode I (opening) fracture.

Fig. 4

 4 Fig.4describes the evolution of the kink angle for mode II experiments. Fracture gage readings yield information of little accuracy as net separation of the two specimen halves occurs much after the actual fracture. Reciprocity-based estimates yield more reasonable values. In this case, we do not look for a prediction of the initial kink angle but rather use

  Dynamic crack initiation experiments were carried out on PMMA, as a model of brittle material.The mode mixity was systematically varied between dominant mode I and dominant mode II and a range of velocities was applied.The initial kink angle increases with the impact velocity independently of the dominant mode.The initial kink angle is reasonably well predicted in terms of a maximum stress criterion, i.e. maximum normal stress for combined opening +shear, and maximum shear stress for shear + negative opening (closure).

Table I :Figure2:

 I Figure I: Experimental setup

Figure 4

 4 Figure 4:

Acknowled�ment

Daniel Rittel gratefully acknowledges financial support from the Israel Science Foundation and from the Technion VPR New York Metropolitan Research Fund.


Karine Lebaron

Lilia Mechiri

Simone Richard

Annabelle Austruy

Jean-Luc Boudenne

Stéphane Coupé

Assessment of individual and mixed toxicity

Keywords: Bromoform, Tribromoacetic acid, Tribromophenol, Disinfection by-products, Sea urchin, Paracentrotus lividus, Ecotoxicology, Genotoxicity

Introduction

Chlorination has been used worldwide, for decades, and is one of the most effective treatment for water disinfection in treatment plants for the production of tap water. Chlorination is also employed in the management and upkeep of industrial installations, where water is often used for cooling or heating purposes, as well as to prevent and control biofouling and corrosion of pipelines.

Hence, in some industrialized embayment areas, huge volumes of coastal seawater are continuously being pumped and chlorinated before being re-released into the original embayment [START_REF] Allonier | Determination of dihaloacetonitriles and halophenols in chlorinated sea water[END_REF][START_REF] Boudjellaba | Chlorination by-product concentration levels in seawater and fish of an industrialised bay (Gulf of Fos, France) exposed to multiple chlorinated effluents[END_REF][START_REF] Manasfi | Characterization of chlorination byproducts in marine waters and sediments in a semi-enclosed bay exposed to multiple industrial chlorinated effluents[END_REF]). As chlorine is very reactive in the presence of natural organic matter, seawater chlorination generates a complex set of brominated and chloro-brominated disinfection-by-products (DBPs). Finally, benthic organism, resident in the embayment, are exposed to these DBPs [START_REF] Singer | Humic substances as precursors for potentially harmful disinfection by-products[END_REF][START_REF] Westerhoff | Reactivity of natural organic matter with aqueous chlorine and bromine[END_REF][START_REF] Halpern | A Global Map of Human Impact on Marine Ecosystems[END_REF].

Although the diversity and occurrence of brominated DBPs in contaminated coastal seawater is relatively undocumented [START_REF] Richardson | Occurrence, genotoxicity, and carcinogenicity of regulated and emerging disinfection by-products in drinking water: A review and roadmap for research[END_REF][START_REF] Manasfi | Assessing the genotoxicity of two commonly occurring byproducts of water disinfection: Chloral hydrate and bromal hydrate[END_REF], bromoform (halomethane), tribromoacetic acid (haloacetic acid) and tribromophenol (halophenol) are among the most prevalent molecules, often measured at relatively high concentrations (i.e. µg/L) [START_REF] Manasfi | Characterization of chlorination byproducts in marine waters and sediments in a semi-enclosed bay exposed to multiple industrial chlorinated effluents[END_REF].

Most of the DBPs so far tested have been found to be toxic and genotoxic in diverse model systems, such as bacteria, mammalian cells or zebrafish embryos. Furthermore, brominated DBPs have generally been shown to be more toxic than their chlorinated analogues [START_REF] Richardson | Occurrence, genotoxicity, and carcinogenicity of regulated and emerging disinfection by-products in drinking water: A review and roadmap for research[END_REF][START_REF] Richardson | What's in the Pool? A Comprehensive Identification of Disinfection Byproducts and Assessment of Mutagenicity of Chlorinated and Brominated Swimming Pool Water[END_REF][START_REF] Yang | Comparative Developmental Toxicity of New Aromatic Halogenated DBPs in a Chlorinated Saline Sewage Effluent to the Marine Polychaete Platynereis dumerilii[END_REF][START_REF] Hanigan | Zebrafish embryo toxicity of 15 chlorinated, brominated, and iodinated disinfection by-products[END_REF]. It is worth noting that at the single molecule level, visible effects have always been following exposure to high concentrations of DBPs, suggesting that toxic and genotoxic risks are limited [START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF]. Nevertheless, recent epidemiological research has suggested that lifetime exposure to DBPs, mainly through ingestion, would significantly increase the risk of bladder cancer in humans [START_REF] Villanueva | Disinfection Byproducts and Bladder Cancer: A Pooled Analysis[END_REF][START_REF] Villanueva | Bladder Cancer and Exposure to Water Disinfection By-Products through Ingestion, Bathing, Showering, and Swimming in Pools[END_REF].

Hence, there are great concerns about the level of human exposure, causing health and sanitary surveillance agencies to enact maximum concentration limits (MCL). For instance, the US EPA recommends MCLs for total halomethane and haloacetic acid of 80 µM and 60 µM, respectively, in safe drinking water. It is notable that these values are relatively consistent with the maximum concentrations measured in the effluents of water treatment plants from the USA, Canada or France (CAREX Canada 2009;[START_REF] Mouly | Les sous-produits de chloration dans l'eau destinée à la consommation humaine en France[END_REF].

There are currently no MCLs for environmental waters that harbor functional ecosystems, and which are potentially chronically exposed to DBPs. Studies that assess the toxicity of DBPs in aquatic animals remain scarce and to our knowledge, only two have tested brominated DBPs in recent years, using a zebrafish embryo model [START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF][START_REF] Hanigan | Zebrafish embryo toxicity of 15 chlorinated, brominated, and iodinated disinfection by-products[END_REF]. These two studies tested a total of 11 brominated DBPs, including bromoform (BMF) and tribromoacetic acid (TBAA). Most of the brominated DBPs that were compared, proved toxic for larval development, though only at high concentration (i.e. >100 µM). Moreover, short-term exposure to chlorinated wastewater did not have any significant toxic effect. Finally, a genotoxic effect was only detected for chlorodibromomethane and sodium borate at their EC50 levels, 160 µM and 0.049 µM, respectively.

In this study, we were interested in broadening the understanding of the toxicological impact of DBPs on coastal marine organisms, using the sea urchin, Paracentrotus lividus, as a proxy for the echinoderm class. Paracentrotus lividus lives in the first few meters of coastal areas. The species is widely distributed along the north-eastern Atlantic coast and on all Mediterranean coasts. Furthermore, they can be encountered in numerous contrasted habitats that include differences in temperature, salinity and chemical contamination [START_REF] Bellas | Integrative assessment of coastal pollution in a Ría coastal system (Galicia, NW Spain): Correspondence between sediment chemistry and toxicity[END_REF]. While adults are benthic and relatively sedentary, the larvae produced at each reproductive event are pelagic for up to 4 weeks, thus ensuring species dispersion. The sea urchin is a relevant model organism to assess the effect of potentially harmful molecules. It is more closely related to human than the mussel [START_REF] Tu | Sea urchin Forkhead gene family: Phylogeny and embryonic expression[END_REF], with two species having been fully sequenced. Reproduction is easily performed in vitro, resulting in high numbers of translucid larvae. Sea urchin larvae have been extensively used to assess the embryo-toxicity and embryo genotoxicity of molecules (Hose 1985;McGibbon and Moldan 1986;[START_REF] Gharred | Assessment of the individual and mixture toxicity of cadmium, copper and oxytetracycline, on the embryo-larval development of the sea urchin Paracentrotus lividus[END_REF]Morroni et al. 2016;Trifuoggi et al., 2017;Neves et al., 2018;Messinetti et al., 2018;Periera et al., 2018;Dorey et al., 2018).

In this study, our objective was to generate additional data in a model marine organism, of the toxic influence of three major brominated contaminants found in coastal seawater that is subjected to massive anthropogenic disturbance. With this aim in mind, we used sea urchin embryos to assess the toxicity and genotoxic potential of bromoform (halomethane), tribromoacetic acid (haloacetic acid) and tribromophenol (halophenol), tested either alone or in combination, on two populations of P. lividus, either chronically exposed or unexposed.

Materiel and methods

Sea urchins

Adult Paracentrotus lividus sea urchins were harvested by scuba diving, on the morning of each experiment, at a non-polluted site (NPS) near the Toulon (Var, France) embayment (Garonne Bay: 43.098503-6.018430)and at a polluted site (PS) where sweater is chronically chlorinated [START_REF] Manasfi | Characterization of chlorination byproducts in marine waters and sediments in a semi-enclosed bay exposed to multiple industrial chlorinated effluents[END_REF]. P. lividus were induced to spawn by gentle shaking.

Sperms and eggs were individually collected, respectively dried with a micropipette and kept on ice and in 100mL of filtered seawater (FSW). Eggs were microscopically observed to verify maturity before adding dry sperm.

Ten independent (i.e. unrelated) larval populations were produced by arbitrarily mixing one sperm with one egg suspension, in 50mL of FSW at 20°C at a concentration of 500 eggs/mL, under agitation for 45 min, then the fertilization rates were assessed.

Observed fertilization rates were 100% in every experiment.

Chemicals

Bromoform (CAS 75-25-2), tribromoacetic acid (CAS 75-96-7) and tribromophenol were purchased from SimgaAldrich. Stock solutions of bromoform and tribromoacetic acid were prepared by direct dissolution in filtered sea water (FSW). Tribromophenol was first dissolved in dimethylsulfoxide solvent (DMSO) to compensate for its very low water solubility, then it was dissolved in FSW, with a final DMSO concentration of 0.1%.

Experimental design

Toxicological assessments

Ten unrelated larval populations were used and tested in triplicates, as follows: in 24-wells microplates, suspensions of 500 fertilized eggs in 2 mL FSW were exposed for 48 hours to 7 concentrations of each chemical (Table 1). At the end of the exposure time, larvae were fixed by adding ethanol at 15% final concentration and kept at 4°C until microscopic observations wre carried out. Normal and abnormal (i.e. delayed growth, developmental anomalies) Pluteus larvae were recorded.

Percentages of normal Pluteus larvae were plotted against chemical concentrations. We then determined the dose-response curves using R software and deduced the half maximal effective concentrations, EC50, here considered as the concentration of chemical at which we could observe only 50% of normal and viable larvae among the pool. The no and lowest observed effective concentrations (respectively NOEC and LOEC) were deduced from the chemical concentrations used in the experiments.

We then assessed the toxicity of the chemicals combined in ten other independent experiments. Specifically, we tested three mixtures named mix NOECs, mix LOECs and mix EC50, that is each chemical within a mixture is at its specific endpoints (NOEC, LOEC or EC50). Depending on the chemical tested, controls were either exposed to FSW alone or FSW containing 0.1% DMSO.

Genotoxicity test

The genotoxicity of the mix NOECs, mix LOECs and twice mix LOECs was assessed using the comet assays, based on the [START_REF] Tice | Single cell gel/comet assay: Guidelines for in vitro and in vivo genetic toxicology testing[END_REF] procedure.

Five thousand, 48-h, Pluteus were exposed as described above for 1 h to the mixtures. After exposure, larvae were collected by centrifugation at 1500g for 10 min at 4°C. The pellet was resuspended in 1mL of FSW, then gently mixed with 1 mL of glycine 1M and incubated on ice for 5 min to allow for complete dissociation of cells. Fifty thousand cells were then collected and placed in 1 mL of phosphate buffer saline (PBS) 1× then centrifuged at 3000g for 5 min at 4°C. Pelleted cells were then collected and gently mixed with 50 µL of low melting point agarose at 37°C, then plated onto a pre-coated laboratory microscope slide [START_REF] Tice | Single cell gel/comet assay: Guidelines for in vitro and in vivo genetic toxicology testing[END_REF].

Once prepared, slides were immersed in a lysis buffer (2.5 M NaCl, 100 mM Na2EDTA,2H2O, 10 mM Tris pH10, 10 g N-Lauroylsarcosine, 10% DMSO and 1% Triton X100) for 90 min at 4°C. Slides were then placed in an electrophoresis tank containing an alkaline solution (200 mM Na2EDTA 2H2O, 10 N NaOH) for 20 min at room temperature, to allow DNA to denature. This was followed by electrophoresis which was conducted for 20 min at 25V and 350mA. Thereafter, slides were plunged in a neutralization buffer (0.4 M Tris-HCl pH 7.5) for 20 min. Finally, slides were fixed with 100% methanol and dried at room temperature overnight.

DNA was stained with a solution of SyberGreen (Sigma-Aldrich) deposited onto each slide and left to stand for 20 min in the dark. Slides were then read using an epifluorescence microscope equipped with a digital camera and dedicated software. Whole cells and comets observed within several randomly selected microscopic fields were captured, irrespective of the length and the shape of the comet [START_REF] Gyori | OpenComet: An automated tool for comet assay image analysis[END_REF]. Pictures were then analysed using Open Comet v1.3.1 (cometbio.org) implemented in ImageJ (SciJava) program [START_REF] Gyori | OpenComet: An automated tool for comet assay image analysis[END_REF].

DNA damage was assessed considering the olive tail moment (OTM) that consider both the length and the distribution of DNA within the comet's tail (Olive et al. 1991).

The control experiment consisted in larval suspensions exposed to FSW containing 700µM of H202.

This concentration has been chosen after a preliminary experiment in which we tested the genotoxicity of H202 for a range of concentrations (R² = 0.9967), as previously described [START_REF] Nahon | Improved Comet assay for the assessment of UV genotoxicity in Mediterranean sea urchin eggs[END_REF].

DNA damage was assessed via the olive tail moment (OTM), which takes into consideration both the length and the distribution of DNA within the comet's tail (Olive et al;, 1991).

The positive control experiment consisted of larval suspensions to FSW containing 500 µM of H2O2.

This concentration was chosen after a preliminary experiment in which we tested the genotoxicity of H2O2 for a range of concentrations (R=0.9983), as previously performed [START_REF] Nahon | Improved Comet assay for the assessment of UV genotoxicity in Mediterranean sea urchin eggs[END_REF].

Statistical analysis

Statistics were performed with Rstudio (version 1.1.442) and XLSTAT (Addinsoft, 2017). Doseresponse curves were produced using regression models. Effective concentrations (i.e. EC10, EC50 and EC90) values were generated with the "estimating effective doses" (ED.drc) from R package.

Prior to Kruskal-Wallis test, ecotoxicological and comet assay data were tested for normality. The Mann-Whitney U test was proceeded to compare data from non-polluted site (NPS) to polluted site (PS). Differences between each condition were considered significant when p-value < 0.05, data were expressed as mean ±S.E.M (standard error of the mean).

Results and discussion

Assessment of BMF, TBAA and TBP toxicity on early embryo development

In this study, embryos were exposed to a range of bromoform (BMF), tribromoacetic acid (TBAA) and tribromophenol (TBP) concentrations for 48 h (Table 1), from fertilization to the Pluteus stage.

The control with 0.1% DMSO was not toxic for larvae, and we found typical dose-response curves, from which EC50 and EC90 were extrapolated (Figure 1 and Table 2). 2). BMF was found more toxic than TBAA. This order of toxicity has regularly been reported in several aquatic organisms living in fresh or marine water [START_REF] Yoshioka | Testing for the toxicity of chemicals with Tetrahymena pyriformis[END_REF][START_REF] Yang | Comparative Developmental Toxicity of New Aromatic Halogenated DBPs in a Chlorinated Saline Sewage Effluent to the Marine Polychaete Platynereis dumerilii[END_REF][START_REF] Liu | Comparative toxicity of new halophenolic DBPs in chlorinated saline wastewater effluents against a marine alga: Halophenolic DBPs are generally more toxic than haloaliphatic ones[END_REF][START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF][START_REF] Hanigan | Zebrafish embryo toxicity of 15 chlorinated, brominated, and iodinated disinfection by-products[END_REF].

Table 1: Different endpoints (µM) for three DBPs, tribromophenol, bromoform and tribromoacetic acid obtain on pluteus larva after 48 hours of exposure Like other aquatic organisms [START_REF] Yoshioka | Testing for the toxicity of chemicals with Tetrahymena pyriformis[END_REF][START_REF] Delacroix | Disinfection by-products and ecotoxicity of ballast water after oxidative treatment -Results and experiences from seven years of full-scale testing of ballast water management systems[END_REF][START_REF] Liu | Comparative toxicity of new halophenolic DBPs in chlorinated saline wastewater effluents against a marine alga: Halophenolic DBPs are generally more toxic than haloaliphatic ones[END_REF][START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF][START_REF] Hanigan | Zebrafish embryo toxicity of 15 chlorinated, brominated, and iodinated disinfection by-products[END_REF], P. lividus embryos were sensitive to relatively high concentrations of the tested molecules. The EC50 was 14.5 µM for TBP, 526.31 µM for BMF and 923.5 µM for TBAA, and the lowest LOEC was 3 µM withTBP (Table 2). Except for bromoform, P. lividus appears to be more sensitive to TBP and TBAA, than the embryos of the marine polychaete, Platynereis dumerilii [START_REF] Yang | Comparative Developmental Toxicity of New Aromatic Halogenated DBPs in a Chlorinated Saline Sewage Effluent to the Marine Polychaete Platynereis dumerilii[END_REF], or the phytoplanktonic green algae, Tetraselmis marina [START_REF] Liu | Comparative toxicity of new halophenolic DBPs in chlorinated saline wastewater effluents against a marine alga: Halophenolic DBPs are generally more toxic than haloaliphatic ones[END_REF]. Indeed, according to the three previous studies, the EC50 for bromoform in P.lividus is situated between the EC50 in zebrafish (200 µM [START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF]) and the EC50 in polychaete embryos (730 µM [START_REF] Yang | Comparative Developmental Toxicity of New Aromatic Halogenated DBPs in a Chlorinated Saline Sewage Effluent to the Marine Polychaete Platynereis dumerilii[END_REF]). With regard to TBP and TBAA, our model seems 4 to 14 times more sensitive to TBP and 4 to 6 times more sensitive to TBAA, when compared with other, different, biological systems [START_REF] Yang | Comparative Developmental Toxicity of New Aromatic Halogenated DBPs in a Chlorinated Saline Sewage Effluent to the Marine Polychaete Platynereis dumerilii[END_REF][START_REF] Liu | Comparative toxicity of new halophenolic DBPs in chlorinated saline wastewater effluents against a marine alga: Halophenolic DBPs are generally more toxic than haloaliphatic ones[END_REF][START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF].

The first observable effect (LOEC) of the three chemicals in our model was the abnormal development of a fraction of Plutei. At higher concentrations, we observed both abnormal and delayed development, mostly between the morula and gastrula stages, although in different relative proportion, depending upon the molecule and the concentration. Of the developmental anomalies triggered by all three chemicals, we mainly observed crossed spicules, which are among the most regularly reported malformations in sea urchin larvae [START_REF] Gharred | Assessment of the individual and mixture toxicity of cadmium, copper and oxytetracycline, on the embryo-larval development of the sea urchin Paracentrotus lividus[END_REF]. Interestingly, TBAA-induced mortality was detected early, since we observed a black-coloured dead eggs (unpublished data).

Table 2: Percentage of normal pluteus larva growth when exposed to NOEC and LOEC of bromoform, tribromophenol and tribromoacetic acid singly and combined Exposure for 48h to mix NOECs and mix LOECs critically reduced the proportion of normal Pluteus, to 63% ± 2 and 18% ±1, respectively, while none survived exposure to mix EC50 condition, demonstrating the combined effect of these chemicals (Table 2). The results obtained with a mixture of chemicals were entirely expected. However, in zebrafish embryos, the influence of chlorinated water had no detectable phenotypic impact after 5 days of exposure, suggesting that a complex water environment, containing a wide diversity of DBPs at much lower concentrations than those tested, may not be deleterious for the population [START_REF] Hanigan | Zebrafish embryo toxicity of 15 chlorinated, brominated, and iodinated disinfection by-products[END_REF]). Nevertheless, chronic exposure of adult fish to 0.9 nM of TBP could potentiallyhave a significant effect over a number of generations, on survival, larval development and the malformation rate [START_REF] Deng | Chronic exposure to environmental levels of tribromophenol impairs zebrafish reproduction[END_REF].

Table 2: Percentage of normal pluteus larva growth when exposed to NOEC and LOEC of bromoform, tribromophenol and tribromoacetic acid singly and combined

We further tested the impact of embryo exposure at polluted site (PS), with embryos generated by P. lividus adults chronically exposed to chlorinated sea water. These PS embryos were exposed to TBP, BMF and TBAA at their respective LOEC, EC50 and EC90, and to mix LOECs condition. They were found slighly less sensitive to TBP and TBAA than NPS embryos, while very resistant to BMF, which is the most prevalent molecule found in seawater where genitors were harvested (Figure 2) [START_REF] Manasfi | Characterization of chlorination byproducts in marine waters and sediments in a semi-enclosed bay exposed to multiple industrial chlorinated effluents[END_REF]. Moreover, exposure to mix LOECs reduced the proportion of normal Pluteus by 40% instead of 80% for the NPS embryos. This increased PS embryo resistance could be explained by either natural selection of larva, with higher fitness for the polluted site, or a parental effect (Ross et al., 2016).

Evaluation of mixtures' genotoxicity

The alkaline comet assay has been widely used to test potential genotoxic effects of environmental pollutants, in several model organisms such as animal embryos and plant roots, as it is an efficient method for detecting single-and double-strand DNA breaks [START_REF] Yıldız | Determination of genotoxic effects of copper sulphate and cobalt chloride in Allium cepa root cells by chromosome aberration and comet assays[END_REF][START_REF] Liman | Determination of genotoxicity of Fenaminosulf by Allium and Comet tests[END_REF].

The results obtained from the comet assay are summarized in Figure 3. It is likely that given the short-term exposures to the mixtures, we observed the obvious potential of our mixtures to induce DNA strand breakages, prior to any activation of DNA repair mechanisms that would counteract the induced DNA damage. The level of genotoxicity was assessed in positive and negative controls and exposed groups according to the OTM and the percentage of DNA (%DNA) measured in the comet tail. Furthermore, we only assessed the proportion of cells within each group that exceeded a threshold value, corresponding to the 95 th percentile of the control group for the OTM and %DNA. Under controlled conditions, the 95 th percentile for OTM and %DNA were 2.6 and 16.5%, respectively.

For the two parameters considered, we found normal data distributions within the negative control groups (Shapiro-Wilk, p=0.1) but not in the tested groups or in the positive control (Shapiro-Wilk, p<0.0001). The non-parametric test did not reveal any influence of exposure level based on the OTM but demonstrated that the group exposed to the mixture at twice the LOEC concentration (mix LOEC_ X 2), had significantly more DNA within the comet tail, compared to all other groups (Kruskal-Wallis, p<0.033). This result would suggest that this last condition only had a genotoxic effect. However, the discrepancy in the results between controls and tested groups for normality of data distribution indicates exposure had an influence on a fraction of the whole cells within each group, as previously reported [START_REF] Taban | Detection of DNA damage in mussels and sea urchins exposed to crude oil using comet assay[END_REF]). Indeed, the proportion of cells with an OTM higher than 2.6 regularly increases with the concentration and gives higher OTM values than observed in the negative control group (Figure 3,A). This increase of the proportion of cells has also been observed for the %DNA parameter, although it is clearly noticeable at the mix LOEC_X2 condition (Figure 3,B). Hence, overall, our results suggest a dose response when taking into consideration both the effect on DNA and the occurrence of responding cells. Similar results have already been reported in short-term exposure experiments, such as those performed on CHO (Chinese hamster ovary) cells exposed to TBA for 4h [START_REF] Plewa | Comparative Mammalian Cell Toxicity of N-DBPs and C-DBPs[END_REF]) and occurrence [START_REF] Taban | Detection of DNA damage in mussels and sea urchins exposed to crude oil using comet assay[END_REF].

It is interesting to note that at the mix LOEC_X2 condition, we found that 7% of the cells were distributed with more than 90 % of their DNA within the comet tail. Among these, a few cells also displayed a high OTM value, which relates to the length of the comet. A similar result was obtained with some cells from the positive control group (Figure 3,B). This could be indicative of ongoing apoptosis [START_REF] Rı ́os | Tribromophenol induces the differentiation of SH-SY5Y human neuroblastoma cells in vitro[END_REF]Collins et al., 2004). Evidence for this has already been demonstrated in other studies. It has been shown that hydrogen peroxide can induce apoptosis, causing cell death in less than 2 h in 293T cells exposed to more than 400 µM of H2O2 [START_REF] Xiang | Is Hydrogen Peroxide a Suitable Apoptosis Inducer for All Cell Types[END_REF]. A recent study has also suggested that halogenated DBPs might trigger apoptosis at high concentration [START_REF] Rı ́os | Tribromophenol induces the differentiation of SH-SY5Y human neuroblastoma cells in vitro[END_REF]Collins, 2004). Thus, in our study, the mixtures might have the potential to also trigger apoptosis in a fraction of the larval cells.

This distribution pattern is difficult to explain at this stage, and it would be interesting to assess whether a certain cell type would be more sensitive to mixtures than others, or if the external cells are more impacted than those embedded within the larval body, for instance. This has already been reported in zebrafish exposed for a much longer time (i.e. 72 h) to BMF [START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF].

Finally, we have highlighted with our experimental conditions that a significant genotoxic effect, observed with high doses of a cocktail of DBPs, are generally consistent with previous findings [START_REF] Teixidó | Developmental effects and genotoxicity of 10 water disinfection by-products in zebrafish[END_REF][START_REF] Hanigan | Zebrafish embryo toxicity of 15 chlorinated, brominated, and iodinated disinfection by-products[END_REF].

Conclusion

This study has furthered our knowledge of the impact of bromoform, tribromophenol and tribromoacetic acid, tested alone and in combination, on sea urchin, Paracentrotus lividus, development. We have revealed that all three molecules could impair embryo development with the order of toxicity TBP>BMF>TBAA. TBP is 10 to 30 times more toxic compared to the two others molecules. The mixture of the three DBPs also significantly impaired development and were proved weakly genotoxic at low doses (i.e. mix NOECs). Increasing the concentration of the mixture to mix LOECs significantly increases cytotoxicity, as well as increasing the level of DNA damage and the proportion of cells affected.

In this study, we have highlighted a potential genome by environment interaction, particularly regarding BMF. Further experiments will be required in order to assess the relative influence of a potential parental effect, as well as local genetic adaptation in the observed resistance.

Figure 1 :

 1 Figure 1: Effect of tribromophenol, bromoform and tribromoacetic acid singly, on the percentage of normal pluteus growth after 48h

Figure 2 :

 2 Figure 2: Effect of bromoform (pink), tribromophenol (blue), tribromoacetic acid (orange), singly and in combination (grey), on the percentage of normal pluteus larva growth from a polluted site, after 48h of exposure. *** represent statistically significant differences at p < 0.05 from Dunnet's test, between the combine molecules toxicity on larva from a non-polluted site and from a DBPs polluted site.

Figure 3 :

 3 Figure 3: Distribution of OTM (A) and % of DNA within the comet tail (B) according to the level of exposure of Pluteus larvae to the chemicals' mixture. Boxplots encompass the 1st and the 3rd quartile. The black line within the boxplot represents the median. Tukey's whisker extents are presented (defined as 1.5 times the 1st and 3rd quartiles). The red horizontal line indicates the 95% percentile of the control condition used as a reference, and the numbers in red indicate the proportion of comets presenting a higher value than the reference

Acknowledgment

This work was co-funded by the French National Research Agency (ANR) within the project FOS-SEA (ANR-16-CE34-0009) and by Conseil regional Provence Alpes Côte d'Azur (Reginal Council SUD).


B Y Simon Lau

Cuong Pham-Nguyen

C S Lee
email: cslee@mmu.edu.my



Abstract-With the proliferation of mobile devices, pervasive learning has become a new wave in technology-enhanced learning (TEL). One of the key problems to solve in this area is to adapt learning content and services according to a learner's needs and wants to different learning contexts at the workplace. We propose using semantic web services as a solution to context -adaptive pervasive learning. Another problem lies in web service search, which may return many services that do not match the requirements and context of a learner. This wastes resources and poses more problems to the formulation of just-in-time learning activities. In this paper, we define a service requirement specification to model a service request and a semantic description metadata schema to sufficiently annotate web service functionalities and behavior. On top of that, we propose an adaptation model to match and adapt relevant web services. The technique and algorithm presented in this paper are aimed at improving the efficiency and accuracy in selecting the right web service for a scenario at the workplace.

I. INTRODUCTION

obile or pervasive learning is the new wave of technology-enhanced learning (TEL) with the proliferation of mobile device ownership such as PDAs and mobile phones. Truly -pervasive‖ learning envisages the combination of formal (at school) and informal (outdoor, at home, at workplace etc.) learning, integration of mobile devices in broader educational scenarios, context-as-construct and seamless learning across different contexts [START_REF] Sharples | Big Issues in Mobile Learning: Report of a workshop by the Kaleidoscope Network of Excellence Mobile Learning Initiative[END_REF], [START_REF] Balacheff | 10 issues to think about the future of research on TEL[END_REF]. As such, learning activities conducted through this new learning paradigm requires the delivery of learning content and service tailored to the learner's current situation. Two other compelling reasons for adapting appropriate content and services are limited computing resources of most mobile devices in terms of processing and displays as well as the complex requirement of engaging the learner with more fruitful learning experiences and outcomes [START_REF] Beale | Mobile Context Aware Systems: the intelligence to support tasks and effectively utilise resources[END_REF].

Once a learning activity is determined based on the previous learning history and the learner's current situation, the adaptation mechanism of learning content, navigation and services [START_REF] Bomsdorf | Adaptation of Learning Spaces: Supporting Ubiquitous Learning in Higher Distance Education[END_REF] for a particular learning context may be applied at two levels of granularities, i.e. 1) adaptation of the learning scenarios and 2) adaptation of services as resources to fulfill the requirements of a learning task.

With regards to the first type of adaptation, substantial work has been done such as adaptations through hierarchical tasks/the method model [START_REF] Pham-Nguyen | Pervasive Learning System based on a Context-Aware Hierarchical Task Model‖[END_REF]. Generally, adaptation is aimed at achieving an activity according to the current situation. On other words, the learning system has to select dynamically the relevant way to achieve the different tasks included in a scenario. The system thus needs to carry out just-in-time searching and matching of available web services that may provide such desired functionalities.

Our paper addresses the second type of adaptation, i.e. service-adaptation. We propose a solution to manage context -adaptive semantic web services. The main contribution of this paper is a service matching and adaptation model that serves as a solid foundation for service discovery, matching and adaptation. The model is built with the support of the service requirement specification identified by a given task and the service descriptor indexing web services. All of the above will be illustrated with a context-aware learning scenario for the workplace.

The organization of this paper is as follows: First, we illustrate our context modeling technique on a simulated example of context-aware learning scenario for the workplace. Second, we present a service requirement specification and a service description metadata model that facilitate service retrieval and adaptation. Third, we describe a detailed adaptation process to rank services retrieved for a learning task in a scenario. Finally, we conclude with a summary, lessons learned and item for future work.

II. RELATED WORK

At present, there have been numerous efforts to annotate web services with semantic information, such as WSMO [START_REF]Web Service Modeling Ontology (WSMO) [W3C Member Submission[END_REF], WSDL-S [START_REF] Akkiraju | Web Service Semantics -WSDL-S[END_REF], METEOR-S [START_REF] Patil | METEOR-S Web service Annotation Framework[END_REF] and OWL-S [START_REF]OWL-S: Semantic Markup for Web Services[END_REF]. However, much of the efforts are on the definition and implementation of semantic web services in e-commerce and business process integration domains. Even though in recent times, there are some efforts to explore the implementation techniques for context-aware pervasive learning [START_REF] Pham-Nguyen | Pervasive Learning System based on a Context-Aware Hierarchical Task Model‖[END_REF], [START_REF] Zhao | Learning Resource Adaptation and Delivery Framework for Mobile Learning[END_REF], [START_REF] Wang | Context Awareness and Adaptation in Mobile Learning[END_REF], efforts to adapt semantically annotated web services [START_REF] Qusay | Relevancy Ranking of Web Services‖[END_REF]- [START_REF] Lu | Ranking Web services based on ontology semantics[END_REF] Serge Garlatti, is with the Computer Science Department, Telecom-Bretagne, CS 83818, 29238 Brest Cedex3, France (e-mail: serge.garlatti@telecom-bretagne.eu).

Semantic Web Service Adaptation Model for a Pervasive Learning Scenario

CONTEXT-AWARE LEARNING SCENARIO FOR WORKPLACE

We will take a portion of an e-Retail learning scenario at the workplace shown in Fig. 1 below as the basis of our service adaptation model. This learning scenario is adapted from the p-LearNet project done in [START_REF] Pham-Nguyen | Pervasive Learning System based on a Context-Aware Hierarchical Task Model‖[END_REF]. A learning situation at the workplace is composed of the physical environment, learning and commercial setting of the user's current work situation. Pervasive learning scenarios at the workplace are modeled by a hierarchical structure of tasks/methods representing learning and working activities. For a complete presentation and execution of the scenario, another category is needed: mixed activities. Thus, working and learning activities are represented by working, learning and mixed tasks. In Fig. 1, the context descriptor and the control structure of some methods are represented for different categories of tasks. The context descriptor of certain methods of a task describes the current situation of the learner. Adaptation modeling based on context is to be articulated in the sections that follow.

As mentioned in Section I, our focus is on service adaptation, an adaptation mechanism dedicated to refine the service retrieval process to realize an atomic task: how to discover available services and adapt them according to the current situation for realizing a learning task. In [START_REF] Pham-Nguyen | Pervasive Learning System based on a Context-Aware Hierarchical Task Model‖[END_REF], a learning situation can be sufficiently described by a set of context features belonging to various context dimensions such as the scenario (a hierarchal task model having a task/method paradigm), the user (a user can be a learner, a teacher, a salesman, a customer, etc.) with sub-dimensions: the role, previous knowledge, know-how, preferences, loyalty card, purchase intentions, intention of use), the retail shop, the device, the location, the time, the pedagogical tools, the network, the physical environment and the resource (learning object, services, media resource, system resource, etc.). Specific metadata are associated to situation properties and context dimensions to enable adaptation. Context features are either permanent or transitory. Permanent context features are a priori knowledge about the scenario and the learner and consistent across different situations. Transitory context features are dynamic, and their values can only be gathered during runtime. It is necessary to analyse each context feature which contributes to service adaptation.

We extend the work in [START_REF] Pham-Nguyen | Pervasive Learning System based on a Context-Aware Hierarchical Task Model‖[END_REF] by creating context views to define different viewpoints about the context usage. It is done by re-grouping context features to form a subset of all the context features of the context space. The context model is not prescriptive in nature as we do not want to restrict it to certain scenarios only. The context viewpoint can be easily tailored to suit different domain types. At present, we have identified the pedagogical and professional context views to cater to the learning scenario for the workplace as illustrated in Fig. 1 below. They are shown in Table I in the previous page.

IV. SERVICE REQUIREMENTS SPECIFICATION

In a learning scenario, an activity may be realized by either one or more web services. Web services can be internal or external. The problem to solve is to search for a service or a set of services that may be composed in a certain order to fulfill the requirements of the current task. In the context of this paper, the task is an atomic task on a task/method hierarchy.

In systems without context adaptation, web services are statically bound to the achievement descriptor of a method [START_REF] Pham-Nguyen | Pervasive Learning System based on a Context-Aware Hierarchical Task Model‖[END_REF] during design time. In this regard, the problem is just to bind, invoke and execute these web services in a known order. Take for instance Task -S.3_1.4 -Search and deliver information of products to the client‖ in Fig. 1 in the previous page (web service A can be bound to this task at design time for searching product information and content objects (images, sounds, documents, etc.). At runtime, logics are built into web service A to search for relevant content objects annotated with description metadata from repositories or from the database and deliver them to the client which made the request.

Even though the ultimate long term vision of semantic web services is fully automatic composition of web services for a user-defined requirement, we argue that it is not practicable yet at this moment in time due to problems such as non-determinism [START_REF] Online Source | Web Services Composition (An AI-based Semantic Approach)[END_REF]- [START_REF] Srivastava | Web Service Composition -Current Solutions and Open Problems[END_REF], partial observability [START_REF] Pistore | ‖Planning and Monitoring Web Service Composition‖[END_REF], [START_REF] Charif | An Overview of Semantic Web Services Composition Approaches[END_REF] and semantic heterogeneity [START_REF] Nagarajan | Semantic Interoperability of Web Services -Challenges and Experiences[END_REF].

The solution that we propose in this paper is semi dynamic service adaptation (discovery and matching). In our proposition, the relevant services can be a single matched service or a set of matched services. For a set of matched services, the system searches for all the relevant services provided by service providers according to the current context descriptor and the service requirement specification (to be presented in this section) in the achievement descriptor. This is done based on an adaptation process (to be presented later) applied for services. For example, taking Task -S.3_1.4‖ above again, our solution consists of four phases. i) Phase 1: a search of web services, which provide information of a selected product or a product type required by the Service Requirement Specification of the method -M1322‖, on a service repository is first carried out according to the current situation.

ii) Phase 2: All relevant web services of all the suppliers are organized, invoked and executed for searching of product information and resources.

iii) Phase 3: The found resources are classified according to the current situation. They are filtered and ranked according to their degree of relevance. iv) Phase 4: Learning/working activities are delivered with the learning content to the targeted peripheral devices.

Characteristics and functionalities of a Service required by a relevant task or activity have to be specified semantically as a signature to facilitate accurate and efficient discovery and matching of the right services. The primary goal of a Service Requirement Specification is to describe a service is to be -desired‖. It is a request issued by the system wishing to interact with a Service provider in order that a task is performed on behalf of the learner in the current context. By our definition, a service satisfies a service requirement by providing a set of desired output parameters for a desired goal (or -effect‖) with a set of input parameters and situational context features.

Our proposed service requirement specification is summarized in as follows:

1) Functional requirement: describes the capabilities of web services desired by a user. It is characterized by input parameters (i.e. a set of triples of name, type and value), target output parameters (i.e. a set of triples of name, type and value), pre-conditions (i.e. a set of declarative predicates using the context features taken from context view) and expected post-conditions (i.e. a set of declarative predicates with another set of features describing the learner and the scenario after execution);

2) Non-functional requirement: includes the identity to annotate a service (e.g. name, owner, type etc.) as well as performance related parameters, such as Quality of Service (QoS), security, availability etc;

3) Content requirement: this specifies a list of domain concepts or a query identifying the content objects (e.g. Price, ProductModel, learning objects etc.).

For easy transfer and processing between different systems, a service requirement specification is manifested in OWL. The OWL representations are discussed in another paper.

Table II above shows an example of a service requirement specification -SRS_M1322‖ for the method -M1322‖ which realizes the task -S.3_T.1.4 -Search and delivery information of products to clients‖. This specification consists of three main parts. In the functional requirement part, the input is composed of three parameters (ProductType ?pt, ProductInfoType ?pi and DeliveredResourceType ?rt) describing desired capabilities of the service for retrieving the desired resource (e.g. document, media, voice file, pages, database, etc.). The pre-condition is a predicate that verifies, for example a mandatory presence of a ProductType. The output describes a list of relevant resources to be delivered to the client. The non-functional requirements portion represents QoS related context features that can be used for ranking purpose (see the section IV, subsection: ranking). Finally, the content requirement presents a list of domain concepts that covers a sub-domain semantic (ontology of products) for query refinement. These concepts are used to -compare‖ with the DomainContent category of a service (see Table III above) through the domain ontology.

V. SEMANTIC SERVICE DESCRIPTION

Towards semantic discovery and matching of the right services, web services have to be semantically annotated with a semantic service descriptor. The fundamental consideration in describing a web service to support accurate and efficient service discovery and matching is to fulfill a three-part ontology [START_REF] Milanovic | Current Solutions for Web Service Composition[END_REF]: function, behavior and interface. The interface dictates how the service can be invoked for a task/method by a calling learning system. The syntax aspect (message format and protocol details) of the Service has been specified in UDDI and WSDL [START_REF] Online Source | UDDI Features List (3)[END_REF]- [START_REF] Online Source | Web Services Description Language (WSDL) 1.1[END_REF].

In this paper, we are interested in specifying a Service's functionality and behavior to facilitate easy matching with the service requirement. It is contained in a signature-like Service descriptor feature set that can be specified in the ModelReference extended elements/attributes of WSDL. Table III above enlists features which are generally applicable to most web services.

The features in Table III above can be extended to include features which describe more specific characteristics of web services such as pedagogy. The features on a feature list can be either mandatory or optional. For example, features such as name, description, language etc. in the General category are compulsory for all web services while features in the meta-metadata, life-cycle, context etc. can be optional depending on the scenario. They serve to index a service for either filtering or ranking purposes. For instance, the location feature when tested using an adaptation rule using the location context feature of the service requirement specification can be used to determine whether this service is to be relevant or not. A Quality feature such as qualityRating is used to rate and rank the service in relation to other service provisions in the web service search result list.

VI. SERVICE MATCHING AND ADAPTATION

Service discovery, matching and adaptation involve the problem of matching the service requirement specification to the service descriptor. At present, we are investigating and designing a schema mapping mechanism to map features on the service description specification to the service descriptor for filtering and ranking purposes.

In this section, we elaborate on the adaptation principle carried out by the Service Matching and Adaptation Framework as shown in Fig. 2 below. The inputs to the matching subsystem are the Service requirement specification identified by a given task and the service descriptor indexing web services. The global process consists of two main phases executed sequentially: Service retrieval and Service adaptation. The former is aimed at searching relevant services in the repository based on the Service description to fulfill the Service request for a task. This is done by querying the Service descriptor metadata repository (matching particularly the ServiceRequirement and DomainContent of Services with functional, non-functional and content requirements of a request). The relevant Services serve as the input for the adaptation phase. The latter is aimed at refining the relevant Services according to the current situation and user's interest. The result of the adaptation will be utilized by the Service Invocation, Orchestration, Choreography and Execution Subsystem which are parts of a global Pervasive Learning System.

The adaptation process consists of three stages: evaluation/classification, filtering and ranking (see Fig. 2). It is specified by a function a f :

} , , { r f c a f f f f  (1)
i) Classification: input services are classified according to the current situation in several equivalence classes: two classes {-Good‖, -Bad‖} for each transitory feature and up to five equivalence classes for all permanent situation features, together. Services belong to an equivalent class if they satisfy its membership rules. In a formal way, the classification function c f is defined as follows:

c n c c c f f f f ... 2 1    ( 2
)
where n is the number of context features for evaluation. The function c i f is the classification function for each feature i and is specified by:

i c i EC Sr Sr f   : (3)
where

       } , { , } { } { , ) ( ) ( ) )( ( j i j i c j c i c j c i ec ec s ec ec s s f s f s f f Let ) (s f s c c 
, by applying the functions ( 2) and (3), we achieve the classified service s:   } ,..., , { , f c (srv 1 )= <srv 1 , {"Good", "Good", "Bad", "Bad"}> f c (srv 2 )= <srv 2 , {"Bad", "Good", "Good", "Good"}> f c (srv 3 )= <srv 3 , {"Good", "Good", "Good", null}> Note in the example above, service srv 3 does not have information for the evaluation of the performance feature.

ii) Filtering: all services belonging to -Bad‖ classes according to a mandatory feature are filtered out (mandatory features are subset of transitory features). In other words, these services are considered irrelevant and discarded. For example, with the deviceModel feature, the class -Good‖ is considered as relevant while the class -Bad‖ is not. So, the system will eliminate all services that belong to the class -Bad‖. The filtering function f f is formally specified as: ) eliminates the service s c from Sr c .

) ( ) } ,..., , { ( , } ,..., , { ,
For instance, taking the set of three services in the example above, the mandatory features for filtering are deviceModel and location, thus the irrelevant equivalent classes EC ir is {-Bad‖, -Bad‖}. By applying the filtering algorithm (4) for

Sr c

= {srv 1 , srv 2 , srv 3 }, the service srv 2 will be eliminate because it belongs to the class -Bad‖ of the deviceModel feature. Thus, the services f Sr remain after filtering step are {srv 1 , srv 3 }. iii) Ranking: some ranking algorithms have been proposed for web services [START_REF] Qusay | Relevancy Ranking of Web Services‖[END_REF]- [START_REF] Lu | Ranking Web services based on ontology semantics[END_REF]. In WSR [START_REF] Lu | Semantic Web Services Discovery and Ranking[END_REF], a ranking process will reduce the burden of finding the most relevant web services from the returned result set based on domain ontology. The ranking algorithm considers the semantics of each Service, and only recommends the most crucial ones to a request. In a pervasive environment, the ranking process is mainly to refine matched services according to the current context. Some context features from the Context, Quality, Financial, etc. categories are taken into account for the service ranking process. In this regard, the user can decide intentionally what context features are more important than others from a feature list proposed by the system. For example, users can choose the qualityRating of services as with high priority while the others are more interested in performance. Moreover, ranking requirements also depend on the type of services. With services for voice communications, the performance is very important while with services for financial transactions, the security must be highly assured. Therefore, the user can build an -interested‖ feature priority list by annotating each feature with an -interested‖ level (from very low to very high). This information will be memorized in the user's profile for later reuses and be part of the current situation information for this user. Based on the list of -interested‖ features, the system can rank all matched services. The ranking process r f is presented formally by a quadruple as follows:

) , , , ( The ranking algorithm ranks relevant services according to their degree of importance as determined by the weightage assigned to each equivalent class. The degree of importance is calculated by a function described below: . We obtain Sr r ={<srv 1 ,{"Good", "Bad", "Bad"}>, <srv 3 ,{"Good", "Good", null}>}. The important point of each relevant service calculated by ( 6) and ( 7) is:

     u I j ij weight i i imp ec f c sr f 1 ) ( ) , ( (6)
f imp (srv 1 ) = 2 + 0 + 0 = 2 f imp (srv 3 ) = 2 + 3 + 0 = 5
Because f imp (srv 3 )  f imp t(srv 1 ), consequently, the ranking order is specified as: srv 1  relevant srv [START_REF] Beale | Mobile Context Aware Systems: the intelligence to support tasks and effectively utilise resources[END_REF] . In other words, the service srv 3 is the most relevant according to the current context.

VII. CONCLUSION

In this paper, we have described a context-aware learning scenario for the workplace based on a hierarchical task/method model, a service requirement specification, a service description metadata model and a service adaptation model. They serve as a solution to manage dynamic content and learning activity delivery in a dynamic pervasive learning system using context adaptive semantic web services. The proof of concept system is currently very much a work in progress with specific learning scenarios rigorously being defined and refined. The results will be presented in our future publication.

Fig. 1 A

 1 Fig. 1 A small portion of the e-Retail scenario

3 }

 3 are matched for a service request of a task. In this case, there are four context features that are taken into account for the classification: deviceModel, Location, deliveryChannel and performance. The equivalent classes correspondent are EC dev = EC loc = EC del = EC per = {-Bad‖, -Good‖}. The result by applying the classification function for Sr is described as follows:

where

  Sr c denotes a set of classified services, EC ir is the set of irrelevant equivalent classes (-Bad‖) for all transitory features. The function Filter(s c

the 2008 IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications Multimedia University, Cyberjaya, Malaysia, 12-13 July 2008

	Legend:	S.1.3
		Sale assistance in situation
	Learning task	Context descriptor:
	Professional task	-Role = {Saleman, Customer} -Location = {Shop}
	Mixed task	M13
	S.3_T.1	S.3_T.2
	Preparing for the sale	Finalizing the sale

B-Y-S. Lau, C. Pham-Nguyen, C-S. Lee, S. Garlatti M Proceedings of

M132 Context descriptor: (the sale with a customer)

	-Role = {Saleman}
	-CollaboratorRole = {Customer}
	-DeviceType ={PTA, Smartphone}
	-NetworkType =

{Wifi, Bluetooth} M131 Context descriptor: (a saleman without customer)

	-Role = {Saleman}
	-DeviceType = {PTA, Smartphone}
	-NetworkType = {Wifi, Bluetooth, GMS
	Location = {Shelve}}

M133 Context descriptor : (a customer is founded nearly a screen LCD with the presence of a saleman)

		-Role = {Customer}
		-CollaboratorRole = {Salemen}
		-DeviceType ={Screen LCD}
	VM1321	-NetworkType = {Wifi, Bluetooth}

Context descriptor: (a customer is detected nearly a screen LCD)

					Method
							{SEQ(S.3_T.1, S.3_T.2)}
									-Role = {Saleman}
									-CollaboratorDeviceType = {Screen LCD}
					Context
					VM1322
			{SEQ(S.3_T.1.3, PAR(S.3_T.1.4, S.3_T.1.2,
				OPT(S.3_T.1.5)))}
	S.3_T.1.1 Verify the information about the labeling of products	S.3_T.1.2 Revise the knowledge of products	S.3_T.1.3 Identify the client M1321	S.3_T.1.4 Search and deliver information of products to the client	S.3_T.1.5 Install the chosen products	S.3_T.1.7 Switch to the supervisory mode (join PTA with LCD)	S.3_T.1.8 Visualize the summarization table of selected products	S.3_T.1.9 Dialog with client on a screen LCD	S.3_T.1.10 Present personalizing messages of the client	S.3_T.1.11 Ask for an interaction of the screen LCD	services S.3_T.1.12 Ask questions about products and
		M1312			M1323					M1331
	M1311			M1322							M1332

descriptor: (a colleague is on line)

	Supplement method
	-CollaboratorRole = {Saleman, Expert}
					M134
	{PAR(S.3_T.1.1, S.3_T.1.2)}
				{PAR(SEQ(S.3_T.1.11, S.3_T.1.12),
					S.3_T1.14}
		{SEQ(S.3_T.1.7, PAR(S.3_T.1.8, S.3_T.1.9, S.3_T.1.10))}
	S.3_T.1.6
	Ask for
	helps or
	learn from
	colleagues
	VM13221	VM13211	VM13212	VM13213	VM13214

Context descriptor: (a customer is near a screen LCD without saleman)

	-Role = {Customer}
	-DeviceType ={Screen LCD}
	-NetworkType = {Wifi, Bluetooth}
	{PAR(S.3_T.1.13, S.3_T.1.14)}
	S.3_T.1.13	S.3_T.1.14
	Scroll	Consult
	promotional	information
	messages and	of products
	advices about
	products
		M1342
	M1341

TABLE I DESCRIPTION

 I OF PEDAGOGICAL AND PROFESSIONAL CONTEXT VIEW

	Context views	Dimensions or features
	Pedagogical	{user(role, knowledge, how-know); learning
		collaborator; scenario (learning activity);
		pedagogical tool; learning objects; learning
		location (coordinate, place); time; device
		(device type: PTA, RFID tag reader, large LCD
		screen); network (network type, bandwidth);
		system resource; physical environment
		(temperature, noise level)}
	Professional	{user(role, preference, loyalty, purchase
		intentions, intentions for use); organisation
		unit; professional collaborator; scenario
		(working activity); resources; working location
		(coordinate, place); time; device (device type:
		PTA, RFID tag reader, large LCD screen);
		network (network type, bandwidth); system
		resource; physical environment (temperature,
		noise level)}

  Sr denotes a set of services, EC i is a set of equivalent classes of the feature i. At present, all transitory context features have two equivalent classes {-Good‖, -Bad‖}.

	Let	(	s	,	ec	) i 	f	c	(	s	)	. This means that the classification
							i
	function c f classifies the Services into an equivalent class ec i
					i
	 EC

i . The * operator is specified as follows:

   relevant is the total relevant order defined on Sr r For example, take the aforementioned example again. The interested features I u for ranking are {location, deliveryChanel, performance} and the corresponding mapped weights W u are {2, 3, 4} relating to {average, high, very high}. After applying a projection of Sr f on I u

	f	Metadata/ ) ( j weight ec returns a correspondent interested weight for Metadata/ Adaptation
	Service description Matched the equivalent class ec j . It is specified as follows: services Service descriptionClassified services Relevant
	Service requirement specification weight ( ec ij f	Matching     )	services j 0 w if	Evaluation/ Classification if ec ij	" 	Filtrage ij " ec  " or Good Ranking " ec Bad ij		null	(7)
									Service Invocation,
	Domain ontology purpose, sr i  relevant sr j implies the service srv Current situation Current situation (interested features) j is more relevant Orchestration, Choreography for ranking and Execution Subsystem Fig. 2 Service Matching and Adaptation Framework than the service srv i if f imp (sr j )  f imp t(sr i ).


Andrzej Kabat

Lida Sadeghioon

Annie Gravey

Hazar Sayed Darwish
email: hazarsd@gmail.com

An Enhanced MAC for supporting Guaranteed and Best effort traffic in a WDM Optical Packet Ring

In this paper we present an aproach for supporting best effort traffic on an ECOFRAME ring network which has been dimensioned primarily for Guaranteed Traffic. This approach is reservation based, and is combined within the ECOFRAME MAC with the opportunistic access method used for Guaranteed traffic. The reservation method is intended to both protect the Guaranteed traffic and allocate fair shares of the excess resources to the competing Best Effort traffic flows. We analyze the performance of the proposed approach on 2 simple scenarios, i.e. a "concentration" scenario where all stations send the same amount of traffic to a Hub on the ring, and an "any-to-any" scenario where each station sends the same amount of traffic to each other station on the ring. The maximum amount of BE traffic that can be supported without degrading the QoS offered to Guaranteed traffic is derived using simple queueing models. The simple scenarios are then simulated, with the modified MAC, and compared to the theoretical results. The obtained simulation results show that Guaranteed traffic is very well protected, and that spare resources are indeed fairly shared. However, the reservation approach fails to meet the maximum utilization in the any-to-any scenario due to the fact that it forbids spatial reuse for BE traffic.

I. INTRODUCTION

With emerging new applications, metropolitan area networks are used for aggregating and distributing of various client traffic types with different requests for services. WDM optical packet rings have been proposed to bring fine granularity along with reconfigurable flexibility for next generation Metro networks.

The present paper continues our work from [START_REF] Uscumlic | Impact of peerto-peer traffic on the efficiency of optical packet rings[END_REF], [START_REF] Uscumlic | Traffic grooming in WDM optical packet rings[END_REF], [START_REF] Uscumlic | The impact of network design on packet scheduling in slotted WDM packet rings[END_REF] and [START_REF] Sadeghioon | A label based MAC for OPS multi-rings[END_REF]. The performance of a single-wavelength, unidirectional optical packet ring is studied in [START_REF] Uscumlic | Impact of peerto-peer traffic on the efficiency of optical packet rings[END_REF]. The impact of WDM on the performance of optical packet rings is studied in [START_REF] Uscumlic | Traffic grooming in WDM optical packet rings[END_REF] while different design methods of the optical packet ring together with the impact of schedulers on the dimensioning process is studied in [START_REF] Uscumlic | The impact of network design on packet scheduling in slotted WDM packet rings[END_REF]. A MAC for ECOFRAME WDM multi-ring optical packet networks that supports only Guaranteed (G) traffic is presented in [START_REF] Sadeghioon | A label based MAC for OPS multi-rings[END_REF].

In our previous works, only G traffic is considered: the network is dimensioned to support all G traffic with no loss and controlled delay. The present work considers both G traffic as before, but also Best Effort (BE) traffic, and addresses methods for supporting BE traffic on top of G traffic, while still respecting committed QoS for G traffic.

We do not consider here the creation of optical packets from electronic packets, and we assume that each optical packet is classified as G or BE traffic. Algorithms for creating optical packets in ECOFRAME have been proposed in [START_REF] Eido | Packet Filling Optimization in Multiservice Slotted Optical Packet Switching MAN Networks[END_REF] and [START_REF] Nguyen | Simutools '09[END_REF].

We assume here that once a packet is inserted on the ring, whether it is G or BE traffic, it is transparently carried to its destination. In particular, resources used by a BE packet in transit cannot be preempted by a G packet. This means that packet loss cannot occur on the ring. On the other hand, buffers used in each station for inserting and extracting packets can possibly get congested, leading to increased transit delays and possibly to packet loss if the buffers are too small.

After analyzing the possible causes of QoS degradations due to BE traffic, we propose a distributed reservation protocol that is used for supporting BE traffic. We show that the reservation protocol impacts on the fairness of BE support.

Fairness for BE traffic is a well studied topic, and in particular a well known, but quite complicated, distributed fairness algorithm is proposed by the Resilient Packet Ring protocol [7]. However, ECOFRAME differs from RPR in the sense that optical packets are not converted into electronic packets in transit stations; the RPR methods cannot then be applied to ECOFRAME. A simple distributed method of reservation of time slots in non-WDM optical packet rings, called SWING, is proposed in [START_REF] Bonald | Swing: Traffic capacity of a simple WDM ring network[END_REF], while a centralized approach of reservation of the free time slots is studied in [START_REF] Cadéré | Virtual circuit allocation with QoS guarantees in the ECOFRAME optical ring[END_REF]. Our approach differs from these works by first explicitly considering both G traffic (quantified by a known traffic matrix) and BE traffic, and second by considering a WDM ring where each station can send and receive on several wavelengths in order to benefit from statistical multiplexing. This paper is organized as follows : Section II explains the ECOFRAME principles ; theoritical stability conditions for the ECOFRAME ring for G and BE traffic are described in Section III ; Section IV details the Access protocol for BE traffic and in Section V performance results are presented ; a last section conclude the paper .

II. ECOFRAME ARCHITECTURE

The ECOFRAME ring is a unidirectional WDM optical packet ring. All operations are synchronized and occur at discrete time instants [START_REF] Chiaroni | Demonstration of the Interconnection of Two Optical Packet Rings with a Hybrid Optoelectronic Packet Router[END_REF]. The interval between successive instants is fixed and is called "time slot". There are up to 40 data channel wavelengths and one out of band control channel carried by a separate wavelength. All data channels support the same rate, which is considered as rate unit in the present paper.

We assume here that each station is equipped with a tunable transmitter and as many fixed WDM receivers as there are data channels.

Each station can thus transmit traffic on all data channels, but at most one packet per slot, which effectively limits a station's ingress rate to 1.

A station can receive traffic on each data channels. Although each station can receive several packets per slot, there may be a limit on the egress rate from each station.

The status of the data channel slots are carried in the same slot of the control channel ; control packets are used to operate the MAC and carry, either explicitly or implicitly, all relevant control information such as slot occupancy on each data channel, traffic type of the packet carried in a slot (if any), destination address and reservation status, plus any other necessary traffic control, fault management or performance monitoring information [START_REF] Sadeghioon | A label based MAC for OPS multi-rings[END_REF].

Traffic travels transparently in the optical domain between its source and its destination which means that there is no Optical/Electonic/Optical (OEO) conversion in transit stations. This characteristic both limits power consumption in stations and minimizes latency. This means that no packets are lost in the optical domain (neglecting physical layer impairments). On the other hand, insertion delay is impacted by this policy since the transit traffic always has priority over packets to be inserted, even if the packet in transit is BE whereas the packet to be inserted is G. According to this policy, and under the assumption that sufficient buffers are available in the electronic domain, the QoS delivered to packets by the ECOFRAME network is expressed in terms of delay (e.g. insertion delay, extraction delay, end-to-end latency, etc).

III. ECOFRAME STABILITY CONDITIONS

We assume here that planning the ECOFRAME network is done according to a traffic matrix obtained e.g. thanks to the set of negotiated Service level agreements (SLA). We thus assume that the network operator knows (an upper bound for) the amount of G traffic to be supported on the ring. This upper bound is usually a busy hour estimate, which implies that the network is usually over-provisioned for G traffic by the network providers in order to operate within safe margins both for delivering the contracted SLAs and for future expansions.

Let the G traffic matrix be A = [a ij ], where a ij represents the traffic flow between stations i and j for a ring with N s stations and N w data channels. Different design methods for ECOFRAME ring with G traffic only are derived in [START_REF] Uscumlic | The impact of network design on packet scheduling in slotted WDM packet rings[END_REF].

For a given network characterised by N s , N w and A, deriving stability conditions for BE traffic consists in identifying the maximum amount of BE traffic that can be supported on the ring, while maintaining the QoS delivered to the G traffic within specific bounds.

The 3 conditions sets to be considered are the following :

1) The load carried by each link is limited by N s ;

2) The insertion delay for G traffic is controlled ; for example, an upper quantile could be specified for this insertion delay;

3) The extraction delay (i.e. the time it takes for a station to extract the optical packet on the link, and to send it on its egress link) is controlled ; for example, an upper quantile could be specified for this extraction delay. Simple queueing models have been derived in [START_REF] Uscumlic | Traffic grooming in WDM optical packet rings[END_REF] in order to assess both insertion delay and extraction delay in an ECOFRAME ring. Inserted traffic flows are modelled as Bernoulli processes, which may seem as overly simplifying since Bernoulli processes are memoryless, and thus not bursty. However, consider that ECOFRAME is assumed to support the aggregated traffic from a large number of individual application flows. Indeed, ECOFRAME is designed to operate metropolitan area networks. It is well known that such aggregated traffics are rather smooth [START_REF] Cao | Internet traffic tends toward Poisson and independent as the load increase[END_REF].

We also show in [START_REF] Uscumlic | Traffic grooming in WDM optical packet rings[END_REF] that extraction delays can easily be upper bounded, as long as the egress rate is limited to a given percentage of the capacity of the egress link. We shall therefore concentrate on the second set of stability condition, and show that this set forbids saturating the links in the ring.

In the present section, we derive stability conditions for simple traffic scenarios, in which we assume that all stations behave exactly in the same way (i.e. send the same amount of traffic).

A. Concentration Scenario

In the "concentration scenario" we assume that there is a specific station (a Hub) which receives traffic from N s other stations. The capacity of the egress link from the Hub is equal to N w . Let a iHub (respectively b iHub ) denote the rate of G traffic (respectively of BE traffic) sent by one station to the Hub.

If there is no G traffic (A = 0), there is a single constraint which consists in avoiding saturation on the most saturated link (i.e. the link to the Hub):

b iHub ≤ N w N s (1)
In the general, the two stability conditions now read :

a iHub + b iHub ≤ N w N s (2)
which avoids the congestion of the most saturated link.

a iHub ≤ β[1 -((N s -1)(a iHub + b iHub )/N w ) Nw ] (3)
which ensures that G traffic to be inserted in the last station before the hub is still delivered an acceptable QoS in terms of insertion delay. Expression (3) is obtained by modeling the insertion queue in station N s (the last station before the hub) with a Geo/Geo/1 queue where the ingress rate is a iHub and the service time is geometric with parameter [1 -((N s -1)(a iHub + b iHub )/N w ) Nw ]. Indeed, the total rate of transit traffic in competition with the G traffic to be inserted in station

N s is [(N s -1)(a iHub + b iHub )].
This rate is balanced on N w data channels and the slots on all data channels are considered to be independent. Therefore, the probability that a G packet can be inserted in a given time slot is

[1 -((N s -1)(a iHub + b iHub )/N w ) Nw ].
β is a parameter (e.g. 0.9) chosen to limit an upper quantile of the waiting time in the above Geo/Geo/1 queue.

For the concentration case, the maximum amount of acceptable BE traffic is thus given by :

b iHub ≤ N w N s -a iHub (4) b iHub ≤ N w (1 -a iHub /β) 1/Nw )/(N s -1) -a iHub (5)
Depending on the value for a iHub , either condition (4) or condition ( 5) is the most constraining, which indeed means that protecting G traffic may forbid using all spare resources on the ring.

B. Any-to-any Scenario

In the "any-to-any" scenario, each station sends the same amount of traffic to each other station. The egress link from each station is of capacity 1. Let a a2a be the amount of G traffic sent by one station to any other station; the amount of G traffic entering (and exiting) each station is thus (N s -1)a a2a . Let b a2a be the amount of BE traffic sent by one station to any other station.

Since the system is completely symetric, the (single) link saturation condition reads :

(N s -1)(a a2a + b a2a ) ≤ 2N w /N s (6)
Each ingress queue is modelled by a Geo/Geo/1 queue with parameters a a2a and [1 -

((N s -2)(N s -1)(a a2a + b a2a )/2N W ) Nw ].
The upper quantile of the insertion time is then limited by the following constraint :

(N s -1)a a2a ≤ β[1-((N s -2)(N s -1)(a a2a +b a2a )/2N W ) Nw ]
(7) As in the concentration case, it is straightforward to derive from these two conditions an upper bound for b a2a . In this scenario also, depending on the value for a a2a , it may be possible, or not, to fully use spare resources.

IV. BE TRAFFIC ACCESS METHOD MAC operation for G traffic is explained in [START_REF] Sadeghioon | A label based MAC for OPS multi-rings[END_REF]. In ECOFRAME, G traffic is transmitted in an opportunistic manner, i.e. G packets can be sent at any free time slot by any station in the network as the network is dimensioned for a known amount of G traffic.

However the amount of BE traffic offered by the stations is potentially unknown, which precludes the use of the same opportunistic access mode. We thus propose an approach to insert BE traffic while ensuring the following properties:

• the QoS of the G traffic should still be within the design limits; • no station should be able to starve the others (i.e. some fairness should be enforced). The last condition specifies that the enhanced MAC should e.g. enforce that, in symetrical scenarios such as described in III all stations can send the same amount of BE traffic.

The main design options selected for the enhanced MAC to support BE traffic are listed below :

• a transit packet has always the priority over the packet to be inserted at any priority level; • in a given station, G packets always have the priority to be transmitted over BE packets; • BE packet can only be transmitted using a reservation made for the BE flow characterised by the same source and destination. • A reservation on a time slot can be erased by a any other station for the benefit of putting a new reservation if the previous reservation has a lower "relative priority" than the new reservation (the notion of "relative priority", which is central to our reservation based scheme, is explained later in this section). Sending a reservation and inserting BE packets are both based on computing the relative priority for each BE flow at every station. The relative priority is only calculated for BE flows, and does not apply to G traffic.

Consider BE flows as f k , let b k be the load per flow and d k be the exact fair share of the rest of the flow. Defining α as a positive variable, each flow is defined as followed:

x k = (α + b k )/(α + d k ) (8)
x k is always larger than b k then it is possible to classify the BE flows according to their respective eligibility and select the flow that has the higher relative priority.

The fair rate for b k can be calculated according to the G traffic matrix used to dimension the network. Additionally it is possible to implement dynamic policy calculation of G traffic matrix based on the actual G traffic inserted by each station. Where a D ij is the G flow sent by station i and received by j, and respectively a M ij is the observed flow rate during measurements then a E ij is the calculated fair shared flow.

a E ij = min(a D ij , A M ij ) (9)
The above equation actually allows us to measure how much G traffic the ring can support according to negotiated SLAs.

Moreover the network can be updated to recalculate the fair share resources available to offer to BE traffic. Knowing how to calculate the fair share , traffic exchange process within each station follow these steps:

1) receive any G packets on one or more channels and release corresponding slots ; 2) insert a G packet if possible ; 3) insert a BE packet if possible ; 4) check all reservations carried in the time slot and insert a single new reservation if it is both necessary, and possible.

Step 1 is straightforward. In step 2, a G packet is transmitted even if the slot is reserved as was previously discussed. While the G packet is inserted, the station can optimize the data channel to insert its packet.

Step 3 only occurs if there is no G packet to transmit (a station can only send 1 packet per slot time). Also, a BE packet can be inserted only on a slot that carries a reservation for its own flow (same source and same destination). If there is a reservation but there is no BE packet to insert, the reservation is just erased by the station.

Step 4 is central in protecting G traffic, and enforcing fairness. It first protects the QoS offered to the G traffic to insert in this station by estimating the future amount of transit traffic, by continuously monitoring reservations. If the amount of future transit traffic is considered excessive, a reservation is dropped. Secondly, G traffic to be received at the station is also protected by estimating the future amount of traffic to receive in the station. If this amount is considered to be excessive, a reservation is dropped. If the station has BE traffic to insert, and one slot is unreserved after these operations, the station can insert a single reservation corresponding to its flow which has the highest relative priority. Otherwise, if no slot is unreserved, the relative priorities of all reservations are compared and the station can overwrite an existing reservation if it has a flow with a higher relative priority.

V. PERFORMANCE RESULTS

To evaluate the performance of the proposed reservation mechanisms, we have implemented them in our NS2 based ECOFRAME simulator, which has been augmented to support a reservation based insertion process for BE traffic while using a purely opportunistic insertion process for G traffic.

In the experiments reported below, we consider both the concentration scenario and the any-to-any scenario. The ring is constructed with six stations and three data channels. The G traffic arrival process is Bernoulli. We assume greedy BE sources, which means that each station always has a BE packet to transmit.

The QoS metric for G traffic is the sojourn time in the ingress which includes both the waiting time till a G packet arrives at the head of the queue, and its the insertion time on the ring. The QoS metric for BE traffic is the insertion time (since there is no actual queue for greedy traffic). We first assess the performance of the reservation based scheme for the concentration scenario. Using relations ( 2) and (3) for b iHub = 0 and β = 0.9, we observe that the system with only G traffic is unstable for a iHub > 0.55. Therefore, relevant areas in Fig. 1 and Fig. 2 are on the left hand side, for a load offered per station (a iHub ) smaller than 0.55.

Fig. 1 shows how the egress traffic from the hub varies versus offered G traffic per station comparing the system without and with BE traffic. It clearly shows that the reservation scheme succeeds indeed in using most of the spare resources to support BE traffic. It is also necessary to check on the one hand that supporting BE traffic does not degrade the QoS offered to G traffic, and on the other hand that the reservation scheme fairly shares the spare resources between stations. This is assessed in Fig. 2 which represents the throughput per station, for both G and BE traffic in the cases without and with BE traffic.

We first see on Fig. 2, that for the flow of traffic from station 5 to the Hub (station 3), G traffic is insensitive to BE traffic. We do not report what happens for the G traffic sent by the other stations, but the result is similar. These experiments clearly show that G traffic is protected by the reservation scheme. Let us now address the fairness issue for BE traffic. Fig. 2 shows the achieved BE throughputs for stations 5 and station 1. Since station 5 inserts its traffic before station 1 in the concentration scenario, it is to be expected that station 5 receives more opportunities for inserting its BE traffic unless the reservation scheme does enforce fairness. We can indeed see that station 5 is slightly favoured compared to station 1 in terms of BE traffic throughput, but fairness, although not perfectly enforced, is quite correct. Indeed, with greedy sources, the upstream stations could indeed starve the downstream stations unless the reservation scheme counterbalanced the topology advantage. Fig. 2 also shows how supported BE traffic decreases as G traffic increases. Let us now assess the performance of the reservation scheme for the "any-to-any" scenario. Using relations ( 6) and ( 7) for b a2a = 0 and β = 0.9, we observe that the system with only G traffic is unstable for (N s -1)a a2a > 0.77. Therefore, relevant areas in Fig. 3 and Fig. 4 are on the left hand side, for a load offered per station ((N s -1)a a2a ) smaller than 0.77. Fig. 3 shows the achieved throughput in any station for both G and BE traffic, without and with BE traffic. As in the "concentration" case, we see that the G traffic throughput is not affected by the support of BE traffic thanks to the reservation scheme. We can also check that spare resources are only partially used since in the area of interest (for the load offered per station smaller than 0.77), the total throughput varies between 0.51 and 0.85. This is not as efficient as in the previous case. Actually, the inefficiency especially at low G load is due to the fact that the reservation scheme precludes spatial reuse for BE traffic. This is why, at minimal G load, the reservation scheme only allows to use half of the ring capacity. This behaviour does not affect the "concentration" case which cannot take advantage of spatial reuse.

We assess the performance offered to both G and BE traffic in Fig. 4 which shows the sojourn time for G traffic and the insertion time for BE traffic. As predicted by our analytical model, we observe that the system becomes unstable when (N s -1)a a2a is close to 0.8. We also see that the sojourn time for G traffic is indeed impacted by BE traffic by comparing the mean waiting times for the cases without and with BE traffic. However, we see that the sojourn time degradation is very limited. We can also see that the insertion time for BE traffic is significantly larger than the sojourn time for G traffic. This was to be expected since the opportunistic insertion process is obviously more efficicent and less constraining than the reservation based scheme used by BE traffic.

VI. CONCLUSION AND DISCUSSION

We have presented here preliminary results regarding a multiservice MAC for a WDM optical packet ring. A previous work [START_REF] Sadeghioon | A label based MAC for OPS multi-rings[END_REF] proposed using a very simple opportunistic access mode for Guaranteed (G) traffic, and the present work advocates using a reservation based scheme in order to also support Best Effort (BE) traffic.

Actually, this proposal may appear paradoxical : it is quite common, in a multiservice framework, to explicitly reserve resources for G traffic while using opportunistic modes to send BE traffic.

However, the present proposal makes a lot of sense. Indeed, opportunistic access methods are to be preferred, whenever applicable, since they minimize latency in general ; such a method can indeed be applied to G traffic, as long as the network is properly dimensioned for the amount of G trafic to be supported.

As ECOFRAME is intended to operate as an optical MAN, it is quite realistic to assume that the network operator has indeed dimensionned its network according to a realistic traffic matrix, and only attempts to transmit G traffic that conforms to the traffic matrix.

On the other hand, the network operator could wish to take advantage of spare resources in order to operate beyond the planned traffic matrix, while still protecting the G traffic. This is why we have attempted to add BE support to our original MAC.

We have also chosen to avoid packet discard in the optical domain, i.e. to ensure that each packet that was successfully inserted in the optical support would always be delivered, except possibly in case of physical layer failures. This is a strong design option, which can be defended with several arguments

• a MAN is first and foremost a transport network; transport network are classically designed as "no loss" networks, i.e. highly dependable networks; • an optical packet carries typically 100kbits, that is the aggregate traffic of at least a dozen or more sources; loosing a single optical packet may indeed affect many applicative flows; • it it usually more efficient to avoid congestion by delaying packets than to apply drop-based end-to-end congestion control (i.e. delaying packets increases the round trip time for TCP controlled flows and thus naturally limits its instantaneous rate, while avoiding retransmissions).

This design option however precludes the use of opportunistic access for BE traffic, since G traffic delivered QoS could be negatively affected by BE packets in transit. Some kind of controlled access has to be implemented for BE traffic. Packet by packet reservation is one such method, that has already been proposed for example in [START_REF] Bonald | Swing: Traffic capacity of a simple WDM ring network[END_REF] which however only supports BE traffic and does not enforce egress rate limitations. This is why we had to design a new reservation scheme for extending our MAC to multiservice support.

Other approaches such as [START_REF] Cadéré | Virtual circuit allocation with QoS guarantees in the ECOFRAME optical ring[END_REF] rely on a centralized computation of "fair shares". This approach allows spatial reuse, but is more sensitive to failures than our purely distributed reservation scheme.

Future studies shall compare the present approach both to drop-based congestion control approaches and to centralized fairness and congestion control approaches.

The preliminary results regarding the proposed reservation based access scheme for BE traffic have shown that this scheme can efficently use spare resources in scenarios where spatial reuse is not required (typically a concentration scenario), but is less efficient whenever spatial reuse improves the performance. This is a potential drawback, although most operational scenarios are currently based on concentration/distribution architectures for Metro networks.

The performance in terms of Fairness of the distributed reservation based scheme has been shown to be correct, even in the concentration scenario where the topological location of a station has a potentially large impact on access performance. We currently try to improve this fairness performance, while also studying other asymetrical scenarios.

Fig. 1 .

 1 Fig. 1. Concentration scenario. Hub Throughput versus offered G traffic per station. Ns = 5, Nw = 3.

Fig. 2 .

 2 Fig. 2. Concentration scenario. Achieved ingress throughput versus offered G traffic per station. Ns = 5, Nw = 3.

Fig. 3 .

 3 Fig. 3. Any-to-any scenario. Achieved ingress throuput versus offered G traffic per station. Ns = 6, Nw = 3.

Fig. 4 .

 4 Fig. 4. Any-to-any scenario. Mean insertion times versus offered G traffic per station. Ns = 6, Nw = 3.

ACKNOWLEDGMENT

The work described in this paper was carried out with the support of the French "Agence Nationale de la Recherche" in the framework of the ECOFRAME project (grant number 2006 TCOM-002-06).