Santiago Rodríguez-Valderrama

Carlos Escamilla-Alvarado
email: carlos.escamillalv@uanl.edu.mx

Hector Amezquita-Garcia

José J Cano-Gómez

Jean-Pierre Magnin

Pasiano Rivas-García

Hector J Amezquita- Garcia

Evaluation of feeding strategies in upflow anaerobic sludge bed reactor for hydrogenogenesis at psychrophilic temperature

Keywords: bioenergy, dark fermentation, energy balance, room temperature

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The worldwide energy production is mainly derived from fossil fuels such as oil, coal and natural gas, comprising activities such as exploration, exploitation, refining, transportation and even combustion, which affect negatively the environment and human health. The conventional stocks of fossil fuels will decrement overtime, whereas energy demand will continue raising as population and consumption habits increase, following urbanization and modernization [START_REF] Asif | Energy supply, its demand and security issues for developed and emerging economies[END_REF]2]. In order to expand the energy sources and to reduce fossil fuels related problems (e.g. air pollution and greenhouse gas emissions), renewable alternatives are under development [START_REF] Elbeshbishy | A critical review on inhibition of dark biohydrogen fermentation[END_REF] such as solar, geothermal, ocean and biomass energies [START_REF]Renewable Energy Sources and Climate Change Mitigation[END_REF].

Bioenergy is produced from biomass through thermochemical, thermal and biotechnological processes for its use in industry, power, and transport sectors [START_REF] Rahman | Wide Spread Exploitations of Bioenergy: Are the Ways towards Sustainable Energy?[END_REF].

Biohydrogen may be considered a nitrogen-and sulfur-free biofuel when produced from ideal conditions dark fermentation (DF). In practice, biological CO2 is produced as consequence of microbial anaerobic respiration, hydrogen sulfide may be produced by action of sulfate-reducing bacteria (SRB) if sulfate is present in concentrations over 3 g-SO3 2-L -1 [START_REF] Chen | Fermentative hydrogen production at high sulfate concentration[END_REF], and NOx emissions increase when hydrogen is combusted along with gasoline, methane or methanol in spark-ignited combustion engines [START_REF] Kim | The effects of hydrogen on the combustion , performance and emissions of a turbo gasoline direct-injection engine with exhaust gas recirculation[END_REF]. Still, hydrogen produced from natural gas, oil, coal gasification and water electrolysis, require high energy consumption, concomitantly causing air pollution and global warming [START_REF] Nikolaidis | A comparative overview of hydrogen production processes[END_REF].

Various fermentation systems have been developed for hydrogen production, such as the batch, semi-continuous and continuous, for either one or multiple stages. Depending on substrate characteristics the bioreactor type may be considerably different. For insoluble substrates the suspended biomass bioreactors -continuous stirred tank reactor (CSTR), upflow anaerobic sludge bioreactor (UASB), and fluidized bed bioreactors-are a preferred option; whereas for soluble substrates the immobilized biomass reactors (fixed bed bioreactor and biotrickling filter) are better designs. For instance, UASB is appraised to be a good option for treating organic fraction of municipal solid waste and synthetic wastewater, as demonstrated in the work by Alzate-Gaviria et al. [START_REF] Alzate-Gaviria | Comparison of two anaerobic systems for hydrogen production from the organic fraction of municipal solid waste and synthetic wastewater[END_REF] who demonstrated that an UASB bioreactor outperformed a packed bed reactor treating such substrates. When using soluble substrates (e.g. cheese whey, sucrose), it has been determined that CSTR is not the most appropriate design, these reactors often have washing of microorganisms caused by a cell growth rate lower than the rate at which the microorganisms leave the digester [START_REF] Ortiz | Hydrogen production research in Mexico : A review[END_REF]. Even though biotrickling filters may be used to treat soluble and insoluble matter, insoluble matter may cause clogging problems [START_REF] Oh | Thermophilic Biohydrogen Production From Glucose With Trickling Biofilter[END_REF]. On the other hand, agroindustrial wastes have been treated by anaerobic solid substrate fermentation [START_REF] Robledo-Narváez | The influence of total solids content and initial pH on batch biohydrogen production by solid substrate fermentation of agroindustrial wastes[END_REF] using the systems designed by Poggi-Varaldo and Oleszkiewicz [START_REF] Jung | Fundamentals of the fermentative production of hydrogen[END_REF], which have been also successfully adapted to semicontinuous acidogenic operation for hydrogen production and to intermittently vented batch operation [START_REF] Escamilla-Alvarado | A multivariable evaluation of biohydrogen production by solid substrate fermentation of organic municipal wastes in semi-continuous and batch operation[END_REF]. Other important alternative for insoluble biomass is the anaerobic sequencing batch reactor (ASBR), since this system is regarded as the batch type of the UASB.

The UASB perform depends on an adequate formation of microbial flocules. If inoculum is not in granular form, promoting its formation can be a difficult and time-consuming task.

Start-up periods of 150-300 d are commonly reported in literature [START_REF] Jung | Bioreactor design for continuous dark fermentative hydrogen production[END_REF]. Liu et al. [START_REF] Liu | Enhanced hydrogen production in a UASB reactor by retaining microbial consortium onto carbon nanotubes (CNTs)[END_REF] explored the retention of anaerobic digester sludges using either activated carbon or carbon nanotubes, finding that the latter prevented cell washout more effectively. However, many researchers prefer to use already formed granules from brewery wastewater treatment UASB reactors [START_REF] Assawamongkholsiri | Effect of acid, heat and combined acid-heat pretreatments of anaerobic sludge on hydrogen production by anaerobic mixed cultures[END_REF][START_REF] Phowan | Hydrogen production from cassava pulp hydrolysate by mixed seed cultures: Effects of initial pH, substrate and biomass concentrations[END_REF]. At laboratory scale, hydrogen production in continuous mode is explored to increase stability, organic matter removal, energy efficiency and yields [START_REF] Tapia-Venegas | Biohydrogen production by dark fermentation: scaling-up and technologies integration for a sustainable system[END_REF][START_REF] Hawkes | Continuous dark fermentative hydrogen production by mesophilic microflora: Principles and progress[END_REF].

Important variables used to control and to increase the hydrogen yields and production rates are inoculum pretreatment [START_REF] Das | Advances in biological hydrogen production processes[END_REF], pH influence [START_REF] De Gioannis | Biohydrogen production from dark fermentation of cheese whey: Influence of pH[END_REF], effect of hydraulic retention time (HRT) [START_REF] Si | The role of hydraulic retention time on controlling methanogenesis and homoacetogenesis in biohydrogen production using upflow anaerobic sludge blanket (UASB) reactor and packed bed reactor (PBR)[END_REF], organic loading rate (OLR), carbon and sludge sources [START_REF] Chang | Biohydrogen production using an up-flow anaerobic sludge blanket reactor[END_REF][START_REF] Muñoz-Páez | Comparison of biohydrogen production in fluidized bed bioreactors at room temperature and 35 °C[END_REF]. Determining the optimal HRT and OLR could maximize the biogas and hydrogen production [START_REF] Kumar | HRT dependent performance and bacterial community population of granular hydrogen-producing mixed cultures fed with galactose[END_REF], and thus it has been of interest to design strategies to evaluate its influence on hydrogen production.

For instance, medium and long-term operation works usually begin with 48 or 24 h HRT [START_REF] Kaviyarasan | Application of UASB Reactor in Industrial Wastewater Treatment -A Review[END_REF][START_REF] Mohammadi | Optimization of fermentative hydrogen production from palm oil mill effluent in an up-flow anaerobic sludge blanket fixed film bioreactor[END_REF]; thereupon HRT is reduced by a pair-number factor, mostly of 4 or 6 h [START_REF] Si | The role of hydraulic retention time on controlling methanogenesis and homoacetogenesis in biohydrogen production using upflow anaerobic sludge blanket (UASB) reactor and packed bed reactor (PBR)[END_REF][START_REF] Chang | Biohydrogen production using an up-flow anaerobic sludge blanket reactor[END_REF][START_REF] Nualsri | Direct integration of CSTR-UASB reactors for two-stage hydrogen and methane production from sugarcane syrup[END_REF].

Moreover, changes between continuous and discontinues modes have also been evaluated [START_REF] Kotsopoulos | Biohydrogen Production in Granular Up-Flow Anaerobic Sludge Blanket ( UASB ) Reactors With Mixed Cultures Under Hyper-Thermophilic Temperature ( 70 °C )[END_REF][START_REF] Sivagurunathan | Feasibility of enriched mixed cultures obtained by repeated batch transfer in continuous hydrogen fermentation[END_REF]. Final HRT for soluble substrates -such as monosaccharides-are 8-12 h [START_REF] Hawkes | Sustainable fermentative hydrogen production: Challenges for process optimisation[END_REF], although some improvements have allowed to reduce it to 4 h not without metabolic unbalance risk [START_REF] Jung | Bioreactor design for continuous dark fermentative hydrogen production[END_REF][START_REF] Si | The role of hydraulic retention time on controlling methanogenesis and homoacetogenesis in biohydrogen production using upflow anaerobic sludge blanket (UASB) reactor and packed bed reactor (PBR)[END_REF].

It is well known the effect of temperature in hydrogenogenic performance. According to bacterial metabolism, three temperature intervals are observed even in hydrogen production, psychrophilic (<25 ºC), mesophilic (30-40 ºC), thermophilic (50-60 ºC), among which the most used have been mesophilic and thermophilic [START_REF] Hallenbeck | Fundamentals of the fermentative production of hydrogen[END_REF]. However, it should be considered that psychrophilic metabolism at 18-25 ºC has reduced energy expenses compared to its counterparts, making it an attractive alternative that if improved could produce net energy gains to the systems [START_REF] Zieliński | Biohydrogen production at low load of organic matter by psychrophilic bacteria[END_REF]. Therefore, the aim of this study was to explore the biohydrogen production in a bench-scale UASB reactor operating at psychrophilic temperature through different feeding strategies (i.e. varying hydraulic retention times and sucrose concentration in the feeding); moreover, an energetic evaluation in terms of gross energetic potential rate and energetic heating losses was performed at the best conditions and compared to literature.

Materials and Methods

Inoculum

Methanogenic granules were obtained from brewery industry anaerobic sludges (Obregón, Sonora, Mexico) and stored at 4 °C. Before use, granules were rinsed with sterilized isotonic solution and heat-treated at 90 °C for 1 h in a water bath to inhibit methane producing microflora and to enrich the hydrogen-producing heat-shock resistant microbes [START_REF] Zinatizadeh | Influence of thermal and chemical pretreatment on structural stability of granular sludge for highrate hydrogen production in an UASB bioreactor[END_REF]. The inoculum was characterized in terms of pH, humidity, total solids (TS), volatile solids (VS) and ashes (Table 1). Humidity, TS, VS and ashes were carried out in triplicate using standard techniques [START_REF] Wef | Standard Methods for the Examination of Water and Wastewater[END_REF].

PLEASE INSERT TABLE 1 HERE

Experimental system and procedure

A plexiglass 0.4 L working volume UASB reactor (0.056 m diameter, 0.23 m height) was fed with mineral medium containing sucrose as carbon source with a peristaltic pump as shown in Fig. 1. The UASB was installed on a laboratory bench and operated at psychrophilic temperature (21  2 ºC). The initial amounts of inoculum, mineral medium and phosphate buffer solution were 77 gwb, 40 mL and 283 mL, respectively; consequently, the concentration of the inoculum in terms of VS was 4155 mg L -1 . The mineral medium composition was [START_REF] Cisneros-Pérez | Inoculum pretreatment promotes differences in hydrogen production performance in EGSB reactors[END_REF] (mg L -1 ): ZnCl2, 75; MgCl2•6H2O, 100; MnCl2•4H2O, 10.77; FeCl3•6H2O, 25.97; CuCl2•2H2O, 3.41; and NiCl2•6H2O, 101.25. A phosphate buffer (pH = 7.28) was prepared using 30 g K2HPO4 L -1 and 20 g KH2PO4 L -1 . The feeding and recirculation operations were performed through a peristaltic pump (323, Watson Marlow, England).

Recirculation was carried out at the lowest attainable speed (30 rpm, 0.1 L h -1 ).

PLEASE INSERT FIGURE-1 HERE

Three different feeding strategies were applied varying HRT and sucrose concentration to evaluate biohydrogen production: first strategy (24h/31c) was a 24 h HRT regime fed daily with 400 mL at 31 gsucrose L -1 , second strategy (12h/19c) was a 12 h HRT regime fed daily with 800 mL at 19 gsucrose L -1 ; third strategy (4h/8c) was a 4 h HRT continuous regime fed with 2400 mL d -1 at 8.3 gsucrose L -1 . The corresponding OLR for each strategy were 31, 38 and 49.8 g (L d) -1 . For the 24h/31c and 12h/19c strategies, due to specifications of the peristaltic pump, feeding was performed daily during 4 and 8 h of operation, respectively.

The feeding was prepared mixing 40 mL mineral medium and 160 mL phosphate buffer, completing total volume with distilled water if needed. After completing the feeding, the reactors contents were homogenized by recirculation during 5 min at maximum speed of the peristaltic pump.

Analytical methods

The pH was determined with a pH-meter (PC18, Conductronic, Mexico), whereas biogas production was measured by hydraulic displacement (3 L column) method everyday using NaCl saturated and pH 3.0 acidified (sulfuric acid) solution to inhibit microbial growth and to avoid CO2 dissolution. Hydrogen composition was measured by absorption in NaOH (4N) and gas chromatography. The biogas and hydrogen volumes were normalized to 273.15 K and 101.3 KPa. The gas chromatograph (GC-15A, Shimadzu Corporation, Kioto, Japan) was equipped with thermal conductivity detector and a Porapak N column. The oven, injector and detector temperatures were 35, 150 and 150 °C, respectively. Helium was used as carrier gas. The total volatile fatty acids (TVFA) concentration was determined by Anderson et al. [START_REF] Anderson | Determination of bicarbonate and total volatile acid concentration in anaerobic digesters using a simple titration[END_REF] titration method. VFA profile of the liquid phase was determined by a gas chromatograph (Thermo Scientific Trace 1310) equipped with a capillary column (Nukol, 30 m x 0.25mm, Fused silica, Supelco, USA) and flame ionized detector (FID). For both the injector and the detector the temperature was set at 250 °C. The oven was heated at 120 °C for 1 min, followed by heating to 140°C at 20 °C min -1 and kept at this temperature for 1 min.

Finally, the oven was heated from 14 °C to 240 °C at heating rate of 40 °C min -1 . Previously, the samples were centrifuged (10,000 rpm, 5 min) and filtrated (0.45 µm). Sucrose concentration was determined by total sugars method with sulfuric acid and phenol compared against a calibration curve obtained using dextrose [START_REF] Dubois | Colorimetric method for determination of sugars and related substances[END_REF].

Calculations

Concentration of total sugars during feeding for each HRT were correlated through linear regression and expressed through Eq. 1.

Y = b0 + b1X (1)
Where Y denotes the total sugars (g L -1 ), X the time during feeding (h), b0 and b1 are the linear regression coefficients.

For the energy evaluation two variables were defined: energy heating losses (EHL) and gross energetic potential rate (EPRgross). According to Eq. 2, EHL -expressed as kJ (L d) - 1is an intensive property equivalent to the energy consumed for heating a liter of influent to the continuous reactor during one average day of operation, Cp is the specific heat at constant pressure for the medium considered, which for practical reasons was stated to be the same as water (4.184 kJ (kg ºC) -1 ), T is the difference of temperature between operation temperature and a reference temperature (21 ºC), HRT is the hydraulic retention time expressed in days,  is the density of the influent (considered the same as water at 20 ºC, 0.9982 kg L -1 ).

𝐸𝐻𝐿 = 𝐶𝑝∆𝑇 𝐻𝑅𝑇 * 𝜌 (2)
According to Eq. 3, EPRgross -expressed as kJ (L d) -1is the energy potential of hydrogen produced from a liter of operation volume of the continuous reactor in one average day [START_REF] Muñoz-Páez | Comparison of biohydrogen production in fluidized bed bioreactors at room temperature and 35 °C[END_REF][START_REF] Escamilla-Alvarado | Energy analysis of in-series biohydrogen and methane production from organic wastes[END_REF]. Eq. 3 was calculated as the product of the hydrogen production rate (HPR, in NL H2 (L d) -1 ), the higher heating value of hydrogen (HHVH2 = 282.8 kJ mol -1 ) and the factor for converitng NL H2 to mol H2 (1 mol H2 is equivalent to 22.4 L H2 according to ideal gas law for 273.15 K and 1 atm).

𝐸𝑃𝑅 = 𝐻𝑃𝑅 * 𝐻𝐻𝑉 𝐻2 * 1 𝑚𝑜𝑙 𝐻2 22.4 𝐿 (3)

Results and discussion

The UASB reactor was operated for 70 days at room temperature ( In Fig. 2a the performance of the UASB reactor in terms of daily hydrogen production is observed, whereas the pH and TVFA variations during the operation of the UASB are found in Fig. 2b. The pH in the reactor was recorded twice a day: the first previous to its daily feeding (pHr) and the second after it was completed and its contents were homogenized (pHadj).

The first strategy (24h/31c) began after a heat-shock pretreatment of the microbial granules, which were previously acclimated to sucrose during 20 d [START_REF] Chang | Biohydrogen production using an up-flow anaerobic sludge blanket reactor[END_REF]. It can be seen that despite previous acclimatization to the substrate, the reactor presented a three-day lag phase, most likely due to germination of the spore-forming microbes, which may take from 60 minutes when fermentation occurs at proper pH, temperature, nutrients condition, to several days when operation is performed at not optimal germination conditions [START_REF] Hawkes | Sustainable fermentative hydrogen production: Challenges for process optimisation[END_REF][START_REF] Duangmanee | Hydrogen Production by Anaerobic Microbial Communities Exposed to Repeated Heat Treatments[END_REF].

Another possibility is the adaptation phenomena of biomass to psychrophilic temperature.

Anaerobic bacteria may adapt easily to low temperatures, and high-rate anaerobic treatment has been achieved at psychrophilic conditions [START_REF] Mckeown | Low-temperature anaerobic digestion for wastewater treatment[END_REF]. Afterwards, the hydrogen production during 24h/31c raised up to 382.8 NmL H2 d -1 at the end of this strategy. It is noteworthy that pH varied before and after feedings (Fig. 2): pHr was 5.8-5.3 whereas pHadj was 6.5-6.3.

This was indicative of a fermentative activity that was in accordance to high TVFAs contents;

the TVFAs exhaust the buffering capacity of the phosphates buffer (alkalinity), which is 6.71- Afterwards, production diminished to a minimum of 920 NmL H2 d -1 (day 53), following a new oscillation. This reduction in hydrogen production was corresponded by a reduction in the pHadj, which fell from 6.5 to 5.5. TVFA profile presented a slightly increasingly oscillations during this strategy. Indeed, 89% of the operation in 12h/19c production fluctuated between 800-1470 NmL H2 d -1 . The average molar hydrogen yield in this strategy was 0.685 mol H2 molhexose -1 . which was a value much below than the theoretical maximum from concomitant acetic and butyric fermentation of 3 mol H2 molhexose -1 , or 6 mol H2 molsucrose -1 , considering that half a mol of hexose undergoes acetic fermentation whereas the other half mol undergoes butyric fermentation. When low hydrogen yields are obtained it could be ascribed to part of the substrate being used to microbial growth and maintenance [START_REF] Argun | Bio-hydrogen production by different operational modes of dark and photo-fermentation: An overview[END_REF], or more likely to a fermentative deviation [START_REF] Escamilla-Alvarado | A multivariable evaluation of biohydrogen production by solid substrate fermentation of organic municipal wastes in semi-continuous and batch operation[END_REF]; for instance, at lower pH values the abundance of lactic acid bacteria is acknowledged, which compete with the hydrogenogenic microorganisms for the carbon source and even alter the redox balance. In our case it is noteworthy that the most abundant metabolite was butyric acid (5.25-7.03 g L -1 ), which was in accordance with lower hydrogen yields (theoretical HY is 2 mol H2 molhexose -1 in butyric fermentation, against 4 mol H2 molhexose -1 in acetic fermentation). Indeed, our HY was in the interval of reported In Table 3 (0.5-1.47 mol H2 molhexose -1 ).

In strategy 4h/8c, biogas production ranged from 1006 to 1550 NmL H2 d -1 , the highest production attained during the whole operation of the UASB reactor, which occurred at day 65. Due to the continuous operation followed in this strategy, no difference could be represented as pHr and pHadj in Fig. 2, therefore only one measurement of the pH was performed. The pH presented decreasing values from 5.5 down to a minimum of 4.9;

concomitantly, TVFA increased up to the maximum for the whole operation (7500 mg L -1 ).

Chang and Lin [START_REF] Chang | Biohydrogen production using an up-flow anaerobic sludge blanket reactor[END_REF] suggested this effect to be expectable considering that as HRT is reduced, acid fermentation is fostered causing an accumulation in VFA that consequently decreases alkalinity and pH. In their work, this led to TVFA of 1,083 mg L -1 , yield of 100 mL H2 (gsucrose d) -1 and hydrogen composition of ca. 20% at a HRT of 4 h.

The pH effects in hydrogen production are undeniable yet there is no consensus on stablishing an optimal fixed value. Our highest HPR 3625 and 3750 NL H2 (L d) -1 were found at days 45 and 65, with corresponding pH of 5.15 and 5.1. In contrast, Mu et al. [START_REF] Mu | The role of pH in the fermentative H2production from an acidogenic granule-based reactor[END_REF] studied the role of pH in the fermentative H2 production from an acidogenic granule-based reactor, finding that at pH 4.2 their HPRmax and HYmax were 3.48 L H2 (L d) -1 and 1.61 mol H2 molhexose - 1 , respectively. However, operating at low pH is risky since iron containing hydrogenases may be affected [START_REF] Kapdan | Bio-hydrogen production from waste materials[END_REF].

PLEASE INSERT FIGURE-2 HERE

Some authors have assayed low HRT, such as Chang and Lin [START_REF] Chang | Biohydrogen production using an up-flow anaerobic sludge blanket reactor[END_REF] or Si et al. [START_REF] Si | The role of hydraulic retention time on controlling methanogenesis and homoacetogenesis in biohydrogen production using upflow anaerobic sludge blanket (UASB) reactor and packed bed reactor (PBR)[END_REF].

Chang and Lin [START_REF] Chang | Biohydrogen production using an up-flow anaerobic sludge blanket reactor[END_REF] in a UASB fed with sucrose (20 g L -1 ) found that hydrogen production depended greatly on the HRT changes evaluated from 24 to 4 h. They reported that at 24 and 4 h HRT the hydrogen production was drastically affected, whereas at 8 h HRT they obtained their HPR rate (6.84 L (L d) -1 ) with an average hydrogen content of 32% (v/v). Si et al. [START_REF] Si | The role of hydraulic retention time on controlling methanogenesis and homoacetogenesis in biohydrogen production using upflow anaerobic sludge blanket (UASB) reactor and packed bed reactor (PBR)[END_REF] in a UASB reactor using glucose as carbon source and carbon nanotubes for supporting the biomass, also found low hydrogen production at 24 h HRT, whereas at 8 and 4 h they achieved the highest productions (4.38 L (L d) -1 ).

In Fig. 3 it is shown the total sugars consumption at HRT 24, 12, and 4 h during their first 10 h after feeding as a depiction of microbial activity. For 24 and 12 h HRT the sucrose concentration was increasing over time pointing to an accumulation substrate. For 4 h HRT, the profile shown in this figure was obtained after beginning the strategy 4h/8c, showing that sucrose concentration continuously decreased (a deficit of substrate), an indicative of a higher consumption rate of the sugars. According to Eker et al. [START_REF] Eker | Hydrogen gas production from waste paper by dark fermentation: Effects of initial substrate and biomass concentrations[END_REF] at low initial sugars concentration (3.84 to 18.9 g L -1 ) its consumption is faster. The initial sugar concentration at 4, 12 and 24 h HRT was 8.3, 19 and 37 gsucrose L -1 . The profiles of each strategy were fitted to a linear regression model, showing that 24 and 4 h HRT had a characteristic linear performance (R 2 = 0.96 and 0.87, respectively), whereas at 12 h HRT deviated from linear behavior (R 2 = 0.6).

PLEASE INSERT FIGURE 3 HERE

Our average results in terms of HY, molar hydrogen yield (MHY), HPR and metabolites found are listed in Table 2; the best results for HY and MHY were at 12h/19c. Even though the 4h/8c strategy had the best average HPR, the average of the strategy 12h/19c included a period of 10 days where the HPR was beginning to rise after the low results from 24h/31c, which lowered the average value.

In Table 3, the best results from our 12h/19c strategy are shown along with selected results from literature works using continuous reactors, mainly UASB, and soluble substrates such as sucrose, glucose or cheese whey as substrate. The best HPR were reported to be either at 6 or 8 h HRT, whereas at 12 h HRT also good results were obtained.

For instance, Si et al. [START_REF] Si | The role of hydraulic retention time on controlling methanogenesis and homoacetogenesis in biohydrogen production using upflow anaerobic sludge blanket (UASB) reactor and packed bed reactor (PBR)[END_REF] reported that at 12 and 4 h HRT their HPR were 2.88 and 5.28 NL H2 (L d) -1 , respectively. The highest HPR from 12h/19c (3.66 NL H2 (L d) -1 ) was comparable to those from literature (2.35 -4 NL H2 (L d) -1 ) at similar HRT than ours.

Regarding HY, it varied in the range 0.5 -1.47 mol H2 molhexose -1 in mesophilic, thermophilic and hyperthermophilic regimes [START_REF] Zinatizadeh | Influence of thermal and chemical pretreatment on structural stability of granular sludge for highrate hydrogen production in an UASB bioreactor[END_REF][START_REF] Anderson | Determination of bicarbonate and total volatile acid concentration in anaerobic digesters using a simple titration[END_REF][START_REF] Perera | Modeling and simulation of net energy gain by dark fermentation[END_REF] or 70 °C). Our HY (0.685 mol H2 molhexose -1 ) obtained at psychrophilic temperature was close to the minimal value of the precedent ranges and no real stabilization of HY was obtained during the experimental sequence.

Differences may be ascribed mainly to operation temperature and inoculum type.

When comparing the results of 12h/19c against reports at psychrophilic and ambient temperatures (Table 3), it is worth noticing that our HPR was higher. The closest to ours was that obtained by Moureira et al. [START_REF] Moureira | The influence of the degree of back-mixing on hydrogen production in an anaerobic fixed-bed reactor[END_REF] in a packed bed reactor using 2 g COD L -1 sucrose as substrate. In contrast, the highest HY (0.918 mol H2 molhexose -1 ) in similar experiments was that reported by Muñoz-Páez et al. [START_REF] Muñoz-Páez | Comparison of biohydrogen production in fluidized bed bioreactors at room temperature and 35 °C[END_REF] using an anaerobic fluidized bed reactor, compared the hydrogen production from sucrose at 8 g (L d) -1 using anaerobic fluidized bed reactor (AFBR) at ambient temperature (<30 °C). When comparing their own results against mesophilic operation, they found that HPR at ambient temperature was nearly 2.2-fold ( 920NmL H2 (lbed d) -1 ) than mesophilic operation. Other substrate that has shown good results

was cheese whey at 20 g COD (L d) -1 , that Carrillo-Reyes et al. [START_REF] Carrillo-Reyes | Different start-up strategies to enhance biohydrogen production from cheese whey in UASB reactors[END_REF] used in UASB reactors, obtaining HPR 1.41 NL H2 (L d) -1 and HY of 0.56 mol H2 molhexose -1 at 24 h HRT. Among the reasons of our highest results may dwell in the differences of sludge pretreatment, HRT used and reactor configuration [START_REF] Alzate-Gaviria | Comparison of two anaerobic systems for hydrogen production from the organic fraction of municipal solid waste and synthetic wastewater[END_REF][START_REF] Si | Comparison of upflow anaerobic sludge blanket (UASB) and packed bed reactor (PBR) for biohydrogen production. 20[END_REF]. For instance, Carrillo-Reyes et al. [START_REF] Carrillo-Reyes | Different start-up strategies to enhance biohydrogen production from cheese whey in UASB reactors[END_REF] disaggregated and heat-treated an anaerobic granular sludge; according to Show et al. [START_REF] Show | Critical assessment of anaerobic processes for continuous biohydrogen production from organic wastewater[END_REF], hydrogen production by granular sludge exhibits very high HPR at low HRT (up to ten-fold) than disaggregated sludge.

Selected results from Table 3 were used for calculating the EHL (Eq. 2) and the EPRgross (Eq. 3); the results are shown in Fig. 4. It was observed that all the works using temperature for maintaining mesophilic, thermophilic or hyperthermophilic regimes presented higher EHL than EPRgross, except for those reports using psychrophilic or room temperature regimes despite not having the highest EPRgross. According to Eq. 3, the EHL rises as temperatures of operation increases and HRT decreases, which was particularly evident for the works of Tähti et al. [START_REF] Tähti | Hydrogen and methane production in extreme thermophilic conditions in two-stage ( upflow anaerobic sludge bed ) UASB reactor system[END_REF], Braga et al. [START_REF] Fm Braga | Thermophilic biohydrogen production using a UASB reactor: performance during long-term operation[END_REF] and Yu and Mu. [START_REF] Yu | Biological Hydrogen Production in a UASB Reactor With Granules. II: Reactor Performance in 3-Year Operation[END_REF], who applied high temperatures (70, 55 and 38 °C, respectively) and short HRT (5, 2 and 6 h, respectively). This remarks the importance that if biohydrogen production systems are to be applied at industrial scale, it is not only important to maximize the yields and production rates, but also to decrease energy expenses. In this sense, the psychrophilic systems may have a real opportunity of development despite the lower hydrogen production when compared to mesophilic, thermophilic or hyperthermophilic systems. Perera et al. [START_REF] Perera | Modeling and simulation of net energy gain by dark fermentation[END_REF] arrived to similar observations than ours, arguing that the improvement in hydrogen production at temperatures above 25 °C is not defensible as net energy gains were negative and declining as temperature increased. Moreover, other strategies to increase the energetic gains of psychrophilic biohydrogen production would be by its coupling to other bioenergy processes such as microbial fuel cells, photofermentation and anaerobic digestion, among others [START_REF] Hernández-Flores | Bioelectricity generation from wastewater and actual landfill leachates: A multivariate analysis using principal component analysis[END_REF][START_REF] Escamilla-Alvarado | Saccharification of fermented residues as integral part in a conceptual hydrogen-producing biorefinery[END_REF][START_REF] Chen | Biohydrogen production using sequential two-stage dark and photo fermentation processes[END_REF][START_REF] Romero-Cedillo | A review of the potential of pretreated solids to improve gas biofuels production in the context of an OFMSW biorefinery[END_REF].

PLEASE INSERT

Conclusion

Semi-continuous and continuous feeding of sucrose in an UASB reactor operated at psychrophilic temperature achieved considerably hydrogen generation and accumulation of TVFAs. The best operation strategy was 12h/19c since it presented comparable results to those in literature that were obtained at mesophilic temperatures and similar HRT. Using HRT of 24 h gave low hydrogen production, whereas 4 h HRT presented higher production at the expense of lower hydrogen yields, which is undesirable when pure substances such as sucrose or glucose are used as substrate.

It is noteworthy that biohydrogen systems operating at psychrophilic or ambient temperature would not require energy expenditures for the heating of the influents, which would thus present an opportunity for achieving net energy gains.

LIST OF FIGURES

LIST OF TABLES

7 .

 7 71 according to Hendersson-Hasselbach equation. Indeed, adequate pH for hydrogen production has been reported in the range 4.5 to 7, depending on substrate, operation temperature, inoculum and reactor type[START_REF] Jung | Bioreactor design for continuous dark fermentative hydrogen production[END_REF]. The TVFA concentration showed slight variations according to the lag-phase presented in the beginning of the operation; the minimum TVFA concentration was 5800 mg L -1 at day 6, and from that day on the concentration of VFA increased continuously. The variations in hydrogen production during days 5 to 11 were lower than 15%, indicating that the reactor achieved the pseudo steadystate. According to Sivagurunathan et al.[START_REF] Sivagurunathan | Effect of hydraulic retention time (HRT) on biohydrogen production from galactose in an up-flow anaerobic sludge blanket reactor[END_REF] such variations in time are considered to be acceptable before attempting a change in feed strategy, or a change in HRT and sucrose concentration as in our case. Hereinafter the second strategy 12h/19c was applied.At the beginning of 12h/19c the biogas production was constantly increasing showing a six cycles wave-like performance (at 19, 25, 32, 40, 45 and 55 h) reaching a highest of 1466 NmL H2 d -1 at day 47. At this point HY reached its maximum value: 97.8 Nm H2 gsucrose -1 .

Fig. 1 .

 1 Fig. 1. Schematic diagram of the experimental apparatus.

Fig. 2 .Fig. 3 .Fig. 4 .Fig. 1 .

 2341 Fig. 2. UASB reactor performance (A) HRT and normalized hydrogen production versus time, (B) pH and VFAs versus time.

Fig. 2 . 1 .Fig. 3 .Fig. 4 .

 2134 Fig. 2. UASB reactor performance (A) HRT and normalized hydrogen production versus time,

TABLE 2

 2

	HERE
	PLEASE INSERT TABLE 3 HERE
	PLEASE INSERT FIGURE 4 HERE

Table 1 .

 1 Characteristics of inoculum.

Table 2 .

 2 Average results and soluble metabolites produced by different strategies.

	Table 3. Biohydrogen production in continuous reactors operating at different
	temperatures: psycrophilic (< 25°C), mesophilic, thermophilic and hyperthermophilic
	conditions.

Table 2 .

 2 Average results and soluble metabolites produced by different strategies.

	Strategies	HY	MHY	HPR	Soluble metabolites (g L -1 )
		(NmL H2 gsucrose -1 )	(mol H2 molsucrose -1 )	(NL H2 (L d) -1 )	EtOH HAc	HPr	HBu	SM	TVFA
	24h/31c	23.9	0.18	0.67	0.47 1.65	0.02	4.89 7.03	6.18
	12h/19c	69.1	0.51	2.60	0.13 1.41	0.03	3.68 5.25	6.81
	4h/8c	63.5	0.48	3.17	0.26 1.55	0.06	4.67 6.54	7.08
	Notes: HY,

hydrogen yield; MHY, molar hydrogen yield; HPR, hydrogen production rate; EtOH, ethanol; HAc, acetic acid; HPr, propionic acid; HBu, butyric acid; SM, soluble metabolites (EtOH+HAc+HPr+HBu).

Table 3 .

 3 Biohydrogen production in continuous reactors operating at different temperatures: psycrophilic (< 25°C), mesophilic, thermophilic and hyperthermophilic conditions. 644 This study Notes: a solid retention time (HRT equivalent 28.8 h); b calculated as hexose equivalent; PBR, packed bed reactor; AFBR, 646 anaerobic fluidized bed reactors; [H2]avg, hydrogen content in biogas; COD, chemical oxygen demand; HPR, hydrogen production rate; HRT, hydraulic retention time; HY, hydrogen yield; NR, not reported; Top, temperature of operation. 648

	Substrate	Inoculum	Reactor type	HRT/	HY/	HPR	Ref
	(concentration/OLR)			Top	[H2]avg in biogas
	Glucose	Granular sludge	UASB	5 h	0.73 mol H2 molhexose -1	2.07 NL H2 (L d) -1	[52]
	(5 g COD L -1 / 25.1 g			70 °C	51%
	COD (L d) -1 )
	Sucrose	Granular sludge	UASB	2 h	0.865 mol H2 molhexose -1	1.8 NL H2 (L d) -1	[53]
	(1.8 g COD L -1 / 24 g			55 °C	51.49%
	COD (L d) -1 )
	Sucrose			8 h	0.75 mol H2 molhexose -1	6.84 NL H2 (L d) -1	[24]
	(20 g COD L -1 / 60 g	Sewage sludge	UASB	35 ºC	42.4%
	COD (L d) -1 )
	(20 g COD L -1 / 40 g			12 h	0.81 mol H2 molhexose -1	4 NL H2 (L d) -1
	COD (L d) -1 )			35 ºC	NR
	Sucrose			6 h	0.5 mol H2 molhexose -1	5.28 NL H2 (L d) -1	[54]
	(10.67 g COD L -1 /	Anaerobic sludge	UASB	38 ºC	NR
	42.68 g COD (L d) -1 )
	(10.67 g COD L -1 /			10 h	0.9 mol H2 molhexose -1	2.88 NL H2 (L d) -1
	25.61 g COD (L d) -1 )			38 ºC	NR
	Glucose	Anaerobic sludge	UASB	8 h	1.47 mol H2 molhexose -1	4.38 NL H2 (L d) -1	[23]
	(8 g L -1 / 24 g (L d) -1 )	supported on		35 ºC	46%
	(8 g L -1 / 16 g (L d) -1 )	carbon nanotubes		12 h	1.45 mol H2 molhexose -1	3 NL H2 (L d) -1
				35 ºC	38%
	Glucose	Sewage sludge	Chemostat-	1.5 a	0.68 mol H2 molhexose -1	1.13 NL H2 (L d) -1	[60]
	(NR/ 12.46 g (L d) -1 )		type anaerobic	24-28 °C	45.1%
			reactor
	Cheese whey	Anaerobic	UASB	24 h	0.56 b mol H2 molhexose -1	1.41 NL H2 (L d) -1	[49]
	(NR/ 20 g COD (L d) -1 )	granular sludge		22-25 °C	76%

Acknowledgements

The authors express their gratitude to Dr. Eduardo Soto Regalado and Dr. Felipe Cerino Córdova for supporting the project (02-106534-PST-15/123), and to Dr. Ángel Martínez Hernández, Dr. Margarita Loredo Cancino and Dr. Luis Humberto Alvarez Valencia for their advising and facilitating laboratory equipment.

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Laurent Fernando

Lucille Stuani

Tony Lionel Palama

Tony Kaoma

Jean-Emmanuel Sarry

Fabien Jourdan

Nathalie Poupin

Modélisation du réseau métabolique des cellules de Leucémies Aiguës Myéloïdes pour comprendre les différences métaboliques liées à la mutation sur IDH1

come

Modélisation du réseau métabolique des cellules de Leucémies Aiguës Myéloïdes

pour comprendre les différences métaboliques liées à la mutation sur IDH1

  LAM) sont des maladies hématologiques liées à la transformation maligne de progéniteurs hématopoïétiques dans la moelle osseuse aboutissant à la destruction du tissu sanguin sain. Des mutations récurrentes ont été observées chez les patients atteints de LAM dont l'une sur des enzymes clés du métabolisme : les isocitrate déshydrogénases 1 et 2 (IDH1, IDH2). La mutation IDH1-R132H conférerait aux cellules LAM une flexibilité métabolique induisant des dépendances métaboliques et énergétiques spécifiques, qui seraient responsables de la chimiorésistance de ces cellules. Dans cette étude, nous avons utilisé une approche globale de modélisation in silico du réseau métabolique des cellules LAM, basée sur des données de transcriptomique et d'exométabolomique, afin d'identifier l'ensemble des voies métaboliques activées par la mutation IDH1-R132H dans ces cellules. À partir du réseau métabolique humain global (Recon2; Thiele et al., 2013) et des données d'expression de gènes obtenues sur des cellules LAM avec ou sans la mutation IDH1-R132H, nous avons utilisés des algorithmes d'optimisation permettant de générer des modèles de réseaux métaboliques représentant spécifiquement le réseau métabolique fonctionnel de cellules LAM avec et sans mutation, c'est-à-dire les réactions du réseau global qui sont spécifiquement actives dans les cellules étudiées. Plusieurs algorithmes publiés visant à l'identification de réseaux tissu-ou cellule-spécifiques à partir de données de transcriptomique ont été testés et adaptés. Les données d'exométabolomique ont été utilisées, via des méthodes de modélisation sous contraintes, pour contextualiser les modèles cellules-spécifiques générés et rendre compte de la production ou consommation des métabolites mesurés. La comparaison des réseaux cellules-spécifiques générés a permis notamment de mettre en évidence que les voies du métabolisme des acides gras étaient différemment utilisées chez les cellules mutées ou non mutées. Les modèles générés pourront être utilisés pour identifier des dépendances métaboliques présentes spécifiquement dans les cellules LAM mutées.

	Laurent Fernando	1 , Lucille Stuani	2 , Tony Palama	3 , Tony Kaoma	4 , Jean-Emmanuel Sarry	2 , Fabien Jourdan	1 , Nathalie Poupin	1
	1 INRA, UMR1331 Toxalim, Université de Toulouse, Toulouse, France.
	2 INSERM, UMR1037, Centre de Recherche en Cancérologie de Toulouse, Toulouse, France.
	3 LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.
	4 Department of Oncology, Luxembourg Institute of Health, Luxembourg.
	Les leucémies aigues myéloïdes (


Kokouvi Benoit

Yann Labit
email: ylabit@laas.fr

Novel Adaptive Data Collection based on a Confidence Index in SDN

Keywords: SDN, Continuous Monitoring, Data Collection, Monitoring Overhead, Adaptive sampling, Time Series

SDN makes networks programmable by bringing flexibility in their control and management. An SDN controller decides how packets should be forwarded and installs flow rules on the data-plane devices for this end. For efficient control and management, the SDN controller needs to monitor the network state continuously in order to have an accurate and up-todate view of the underlying data-plane. The need for this highvisibility on the network brings specific types of monitoring as streaming telemetry where data is streamed continuously from network devices (wired switches, wireless energy constrained nodes, etc.) as bulk time series data. But this may generate a lot of overhead. Adaptive monitoring techniques provide ways to reduce this overhead, but generally, they require complex user parameter tuning and also they effectively handle data dissemination overhead with counterpart certain energy-consuming treatment on the source nodes. In light of this, we propose novel adaptive sampling technique based on a confidence index that considerably reduces the number of exchanged messages about 55-70 % while maintaining an accurately collected data, represented by the explained variance score that is about 0.7 and 0.8. And more, our proposition achieves these results while being a lightweight solution for source nodes.

I. INTRODUCTION

Software Defined Networking (SDN) [START_REF] Haleplidis | Software-defined networking (sdn): Layers and architecture terminology[END_REF] is the physical separation of the control plane from the forwarding plane. It eases control and introduces flexibility in network management. Innovation and optimization in networks are also eased. SDN paradigm is used for both wired and wireless networks, but it was traditionally developed for wired environments where it is more mature. Its application to the wireless context has a lot of advantages even if it needs readjustments and adaptations.

For an SDN architecture to work well, and especially in the case of dynamic networks, we need to frequently or continuously monitor the network and collect its information. This monitoring consists essentially of network state information (performance metrics, physical topology, hop latency, queue occupancy, remaining energy, etc.) and traffic/flow information collected from data-plane devices such as wired servers, switches, routers, wireless nodes, base stations, etc. This collected information may help in the control of packet forwarding allowing the controller to adapt flow rules optimally. It is also used by management or monitoring services or applications such as DDoS Attack Detection [START_REF] Yang | Sdn-based ddos attack detection with cross-plane collaboration and lightweight flow monitoring[END_REF].

Inspired by self-driving cars, self-driving networks constitute a new promising research topic [START_REF] Feamster | Why (and how) networks should run themselves[END_REF] [START_REF] Kalmbach | Empowering self-driving networks[END_REF]. Their implementation will leverage SDN, machine learning, and automation, just to name a few. And here data collection is of great interest, and more real-time telemetry is needed.

Continuous monitoring is then ineluctable in SDN and this monitoring must not be restricted to only traffic or flow information but all relevant ones that can be useful in any decision making process at the controller level. The only downside is that it may generate a lot of overhead for an SDN architecture. To handle this, adaptive data collection techniques are developed. Based on [START_REF] Tsai | Network monitoring in software-defined networking: A review[END_REF], an overview of network monitoring in SDN, [START_REF] Tangari | Selfadaptive decentralized monitoring in software-defined networks[END_REF] an adaptive approach for monitoring in SDN and [START_REF] Taherizadeh | Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review[END_REF], presenting a state-of-the-art review of monitoring in edge computing and its requirement, the literature adaptive techniques handle efficiently overhead reduction problem, but they still present these 3 aspects:

• they are tailored for a polled-based monitoring model and generally for traffic and flow information collection. Other information may be useful, and for continuous monitoring a push model is more adequate as in streaming telemetry. So, adaptive techniques have to be compatible with this latter model or more have to be generic; • they require complex parameters tuning, which limits their adoption; • the lightness of the solutions for source nodes, that could be energy constrained devices, is questionable. Considering the aspects discussed above, we propose a new adaptive sampling approach, COCO (information COnfidence index COllection) with the following contributions:

• We propose, for continuous monitoring overhead reduction, an adaptive monitoring framework using a confidence index α assigned to any information to be collected continuously. Its algorithms are presented (section III). • Then, we carry out experiments on our proposed framework, its evaluation results are discussed (section IV).

• And finally, we present an overview of adaptive monitoring techniques from the literature (section V).

II. BACKGROUND A. SDN Controller

SDN controller is the heart, not to say the brain of an SDN architecture [8]. It exercises management-control over the data-plane devices to satisfy the needs of applications by translating their requirement (high-level intent) into flow rules (instruction) that will be executed by devices [START_REF]Framework for sdn: Scope and requirements[END_REF]. When satisfying these requirements, the controller has to achieve certain goals: QoS provisioning, optimal network resources utilization, security aspects, etc.

The SDN controller ensures the control part essentially with the OpenFlow protocol [START_REF]1.5. 1[END_REF]. For management, protocols used are OF-CONFIG, NETCONF, SNMP, etc.

By controlling and managing the underlying data-plane, the SDN controller plays the role of an active control element in a feedback loop, responding to network events, recovering from failure, reallocating resources [11], etc. Because the dataplane is a dynamic environment that changes over time, the controller by mediating between applications and networks resources [START_REF] Schaller | Software defined networking architecture standardization[END_REF], continually adapts the state of resources of its data-plane to maintain a desired (optimal) state. From Fig. 1, adapted from [8] [12], we can see control as the process of establishing and maintaining a desired state on the data-plane, and feedback is of utmost importance in this process. The feedback process constitutes a management task (events handling, statistics collection, monitoring, etc.).

Comparison Adjustment

SDN Controller

Delta

Data-plane Resources

B. Data collection in SDN

As shown above, with an SDN architecture we are in the presence of closed-loop of control. For this control loop to be efficient (i.e. allows applications to dynamically control the network while improving its utilization, QoS provisioning, etc.), the most active element of this loop, the SDN controller, has to provide to the applications network information pertinent to their needs and objectives [START_REF]Framework for sdn: Scope and requirements[END_REF]. We can distinguish two main categories of information:

• Traffic Forwarding information: flow statistics such as switch interfaces (ports) information like flow volume, data throughput, packet loss, port down or up, etc. • Network Resource State information: related more to the management part e.g. CPU usage, disk usage, etc. The information may be directly informative (port is down or up) or can be used as performance metrics and so on.

As mentioned by ONF in SDN Scope and Requirements this information that can be static (eg. datapath ID) or dynamic (eg. CPU usage) may be collected in several manners: provisioned, discovered, queried, notified and streamed.

Using these techniques, SDN controller would gather relevant information and provide global view and full knowledge of the data-plane under its control. The data collection importance is not to be proved but is increasing with the advent of machine learning techniques in networking in general [START_REF] Boutaba | A comprehensive survey on machine learning for networking: evolution, applications and research opportunities[END_REF] and especially in the control loop of SDN [START_REF] Xie | A survey of machine learning techniques applied to software defined networking (sdn): Research issues and challenges[END_REF], machine learning who breathes "only" by data. Monitoring will also have a great role in the implementation of self-driving networks, inspired by autonomous vehicles, which leverages SDN and Machine Learning [3] [4]. Self-driving networks need continuous resource monitoring and streaming telemetry fits well with this requirement [START_REF] Rafique | Machine learning for network automation: Overview, architecture, and applications [invited tutorial[END_REF]. Nevertheless, care should be taken to the abstract level and granularity of collected information and to the cost of this task.

Indeed, Streaming Telemetry [16], powered by the Open-Config working group, is a new approach for network monitoring in which data is streamed from devices continuously, thus bulk time series data is collected (e.g. statistics are uploaded every 15 seconds). An event-driven update and requested for ad-hoc data are also available. Unlike SNMP, Streaming Telemetry uses a publish-subscribe pattern and is model-driven telemetry enabling vendor-agnostic monitoring. It is based on open-source initiatives like YANG model, OpenConfig data model, gRPC (Google Remote Procedure Call) etc.

C. Problem with periodic monitoring

We have seen in section II-B the stress of network state information collection in SDN. This collection is usually done periodically, and this for several reasons. For example, we want network state information to be available when SDN services or applications request them, proactive approach for the network management. This periodic data collection is not without cost, but more it presents a dilemma: at which frequency (sampling period), data may be collected?

• If we use high frequency, we will have real-time and accurate information but with significant overhead. • Low frequency resolves the problem of overhead but we will not have accurate network state information.

Continuous monitoring, not to say periodic monitoring at high frequency, is needed to ensure high-visibility and deep or real-time insights on the data-plane. Streaming telemetry (discussed above) has arisen and is compatible with these requirements by streaming data each X seconds to an interested receiver, on a push basis, generating bulk time series data. Concerning overhead, the use of a push model offers a better efficiency compared to a collection in a polling fashion. Furthermore, to add more efficiency and tackle this overhead problem, we propose to stream the data, not with high frequency, but with a "virtual high frequency", or in other words, to use adaptive sampling in the data streaming process. The adaptive sampling procedure is based on a confidence index α representing the trust the receiver, here the controller, has on the source node for adjusting the reporting interval.

Our adaptive monitoring algorithm consists of adjusting collection period for a push-based monitoring model according to α. Indeed network devices stream to the SDN controller each T α (equals to T αmin = T 0 , initially) unit of time and this reporting interval increases automatically [T αmin , T αmax ] over time, and it will be adjusted (decreased) only if the receiver side is unable to capture the runtime evolution of the information being collected and thus can not guarantee a certain accuracy, initially defined. Then, it raises a deviation alarm and the reporting interval is decreased. A detailed description of our proposition is given in section III.

III. OUR SOLUTION: AN INFORMATION CONFIDENCE BASED ADAPTIVE DATA COLLECTION

We represent, like [START_REF] Trihinas | Low-cost adaptive monitoring techniques for the internet of things[END_REF], the metric M to be monitored as a sequence, at regular time intervals (e.g.

T 0 = 1s), of values m k , M = {m k } n k=0
, n → ∞. These n + 1 observations m 0 , ..., m n form a time series and m k may be seen as a tuple (id, t k , value, α k ). With our adaptive sampling algorithm, only some of these samples m k will be pushed to the SDN controller by the network node, source of the monitoring stream M.

The data collection process between the controller and the node according to our proposition COCO (information COnfidence COllection) is described in Fig. 2. Initially, a certain application, control or management service expresses its interest in certain information (1a). It specifies the metric M to be collected, at which frequency it may be available with T 0 , and η the collection (or forecast) imprecision tolerance indicator. The registrator then initialize the collection process on the node (1b) and (1c).

Information to be collected periodically is given a confidence index α, and T α is the period with which this information will be pushed i.e. at t k = t k-1 + T α k (2a). T α will evolve in a similar manner as TCP's congestion window evolution: slow-start and congestion avoidance. Indeed, we will begin with a low period T α (k) = (1 + α(k))T 0 = T 0 (α = 0) and α will be increased each β samples uploaded if a deviation alarm is not raised by the controller. Otherwise, α will be decreased according to the deviation degree γ (3). This is presented with our Adaptive Sampling Procedure on Algorithm 1. A mutual trust environment exists between the node and the controller. Indeed, the controller allows the nodes to upload samples with confidence α which means each (1+α)T 0 and the node trusts the controller to be able to have good forecasts and then an accurate representation of the metric evolution based on past data-points.

The COCO Agent is executed by the node (switch) as a software-based monitoring solution, of course in collaboration with the SDN controller which processes collected samples, forecasts uncollected ones and raises deviation alarm if needed. The complete process on the controller is described with Algorithm 2 and its details are given below.

The controller receives information samples at t k = t k-1 + T α k from the node. Firstly, it stores it and then it forecasts step equals to α + 1 next data-points (2b). The first α th data-points may be used in place of uncollected ones and the latter will be used in deviation checking process. The forecast is done, on previously collected samples and forecasts, using the univariate statistical time-series ARIMA (AutoRegressive Integrated Moving Average) model. ARIMA parameters (p, d, q) are computed automatically using Auto ARIMA [18] which chooses the best model order, selecting the combination that minimizes models quality estimators such as the Akaike Information Criterion (AIC) or the Bayesian information criterion (BIC). For convenient time series forecasting, we need to satisfy a minimum training set size which generally is 50. But this is for general cases with seasonality. For this study, we focus on non-seasonal time series and 30 gives good results. for this reason, we define a specific β, β 0 equals to 30 (default) for α equals to 0 in the beginning.

When the received sample is the β th data-point for the current α, deviation checking is done using Mean Absolute Scaled Error (MASE) on β last forecasts, β last really collected datapoints, according to the training set (previous data-points) used by the forecasting model. MASE, a measure of the accuracy of forecasts proposed in [START_REF] Hyndman | Another look at measures of forecast accuracy[END_REF], has many desirable properties compared to existing forecasting errors measurements, and it fits well with our deviation checking task. It is computed using Eq. 1, with y k representing a real observation, which is the value contained in the metric sample m k , and ŷk its corresponding forecast.

M ASE = 1/β β k=1 ( |y k -ŷk | 1/(β -1) β k=2 |y k -y k-1 | ) (1)
It compares the actual forecasting method to the one-step naive forecast computed in-sample with a ratio of their respective Mean absolute Errors (MAE). Then when MASE < 1, the actual forecasting performs well than the naive one and vice versa. We transform this threshold of 1 to MASE th = 1 + η which will be used in the deviation alarm raising decision. When the threshold is violated, the controller raises a deviation alarm of degree γ (default γ = 1). γ is sent to the node for updating (decreasing) α then T α . An illustration of T α 's evolution is represented in Fig. 3 as the evolution of α since T α = (1 + α)T 0 , where the α max reached is 6 then T αmax is 7T 0 and where we observe 5 raised alarms at the following time: (170, 240, 260, 470, 540)T 0 . Our framework follows these 2 objectives: have a rather accurate evolution (samples) of the target information and with a minimal number of messages exchanged between the controller and the node. This aims to reduce overhead, mainly on the data-plane. Indeed our framework proposes a lightweight adaptive solution for source nodes compared to related works where the adjustments of reporting intervals are done after computing, predictions on the nodes. In our case, apart from the case where a deviation alarm is raised, the adjustment is automatic and most of the computation is done on the controller side.

Also our approach requires minimal external parameters (T 0 and η) all the other parameters are internal to the framework.

IV. COCO EVALUATION

We evaluate our proposition COCO with three real traces and a synthetic one. N represents the number of data-points:

• Battery levels, discharge of a laptop with N = 211 • ARMA generate, synthetic data with N = 300 • CPU monitoring, CPU usage data with N = 800 [START_REF] Shafer | Rainmon: an integrated approach to mining bursty timeseries monitoring data[END_REF] • Switch Traffic (in bits per second) with N = 288 Fig. 4 shows the traces (as on the source node) and their corresponding collection at the controller level with COCO. We implement our proposed approach in Python and the simulation experiments were carried out on a machine Intel Core i7-7500U CPU 2.70 GHz x 4 with 16 GB of RAM running Ubuntu 16.04 LTS. For the evaluation, we use the following two metrics:

• Percentage of the number of samples collected compared to fixed T 0 collection mechanism for the efficiency of COCO. And we want this value to be as small as possible. It includes also messages from the controller to the node when a deviation alarm is raised. • Explained Variation Score (EVS) for the quality of COCO (accuracy). It expresses the proportion to which COCO accounts for the variation (dispersion) of a data set. It is computed with Eq. 2, where y represents the original trace and ŷ the collected version of y :

EV S(y, ŷ) = 1 - V ariance(y -ŷ) V ariance(y) (2)
The best score possible is 1.0. Firstly, we evaluate the impact of η, for β 0 = 30 and β = 10, on COCO's efficiency and its quality. η is used in the decision process as an indicator of the tolerable imprecision. Negative values (> -1) means MASE threshold < 1 imposes us to compulsory do better than the naive one-step forecast. And with positive values, we have more flexibility. Fig. 5 shows the importance of η on COCO efficiency and its collection quality. Indeed increasing its value improves the percentage from 70% to around 35% while maintaining a quite stable EVS 0.75, 0.8. An interesting result is for the Battery levels trace (with a quite simple evolution), where we have an EVS equals to 1.0 when η increases. More interesting this is followed by a decreasing of the percentage of messages exchanged. It reaches its lowest value of 36.49% from η equals to 1.75, and this with the same EVS 1.0. These interesting results presented may be seen in an intern point of view of COCO with α evolution according to η, represented in Fig. 6 by α max reached and the number of deviation alarms raised. They evolve in opposite manner when η increases. Then, in Fig. 7 (Left), for η equals to 0.5, we present the percentage of the number of samples pushed to the controller with COCO compared to a fixed period T 0 mechanism for different values of β. This percentage at β equals to 10 is around 30% and 45% which represents a reduction of 55%, 70% of the number of messages exchanged to have at the controller level an acceptable accurate evolution of the metric on the target node. The accuracy is presented with EVS on the right graph. We can see also that COCO's efficiency is more important in the long term, where represented with the size of the traces. So we have the lowest percentage with CPU monitoring trace with 800 data-points. It depends also on the values of β. Obviously when increasing β, this percentage increases also, and this with relatively the same EVS. Slower values of β are then recommended. To sum up this evaluation, we see that our proposed adaptive approach reduces considerably the number of messages exchanged in continuous monitoring, with good accuracy, and with the great advantage to being lightweight for source nodes, that mainly have just to push the samples with automatic sampling rate.

V. RELATED WORKS Lots of adaptive algorithms are provided in the literature from wireless sensor networks data collection [START_REF] Laiymani | Adaptive data collection approach for periodic sensor networks[END_REF] and Internet of Things (IoT) [START_REF] Trihinas | Low-cost adaptive monitoring techniques for the internet of things[END_REF] to monitoring in networking and especially in SDN for monitoring overhead optimization. As pointed out in [START_REF] Tangari | Selfadaptive decentralized monitoring in software-defined networks[END_REF], we can distinguish two main techniques:

• Threshold-based adaptive monitoring, where the monitoring period is adjusted by comparing the two last measurements variations to an upper and a lower thresholds. Payless [START_REF] Chowdhury | Payless: A low cost network monitoring framework for software defined networks[END_REF] uses this approach. Its authors focus on the trade-off between monitoring accuracy and network overhead by proposing a frequency adaptive statistics collection scheduling algorithm applied on selected switches. Payless's adaptive algorithm adjusts dynamically polling frequency based on collected data granularity. • Prediction-based adaptive monitoring, where the next measurement is predicted with last ones. the deviation between the forecast and the real observation guides the period adjustment. This approach is used in [START_REF] Zhang | An adaptive flow counting method for anomaly detection in sdn[END_REF] which uses a linear prediction based dynamic adjustment scheme to provide dynamic zooming into the flow space (temporal and spatial dimensions). Authors in [START_REF] Tangari | Selfadaptive decentralized monitoring in software-defined networks[END_REF] propose SAM (Self-tuning Adaptive Monitoring) which uses a prediction-based approach to dynamically adjust polling rate but with minimal parameter tuning effort, not like [START_REF] Zhang | An adaptive flow counting method for anomaly detection in sdn[END_REF] and [START_REF] Chowdhury | Payless: A low cost network monitoring framework for software defined networks[END_REF].

Other relevant works on the topic include [START_REF] Van Adrichem | Opennetmon: Network monitoring in openflow software-defined networks[END_REF] and [START_REF] Fawcett | Tennison: A distributed sdn framework for scalable network security[END_REF]. OpenNetMon [START_REF] Van Adrichem | Opennetmon: Network monitoring in openflow software-defined networks[END_REF], for traffic monitoring (flow-related information collection with OpenFlow), uses an adaptive polling rate that increases and decreases respectively when the measurement values differ between samples and when the values stabilize. TENNISON [START_REF] Fawcett | Tennison: A distributed sdn framework for scalable network security[END_REF] is a distributed security framework with effective and proportionate monitoring. It offers multilevel monitoring using appropriate tools (sFlow, IPFIX, DPI) from layer 1 to layer 2. Its sampling and polling rate are dynamically adjustable based on three thresholds.

Although the adaptive monitoring works presented above are very interesting, they are mainly tailored for traffic monitoring using a polling approach on the first hand. And on the other hand, most of them require complex parameters tuning. For fine-grained management at the SDN controller level and with the advent of self-driving networks with its real-time telemetry requirement, we do not need only flowrelated information collection with a poll-based approach but all relevant information that may contribute to the decision making process.

Taking into consideration all this, we have proposed COCO, an adaptive data collection framework for a push-based monitoring model and with low parameter tuning. It uses the two main techniques presented above: a prediction technique to forecast uncollected data-points and a threshold decision making process for deviation alarm raising, that expresses if T α should be increased or decreased (section III). It has also the advantage of being lightweight for source nodes.

VI. CONCLUSION

In this work, we presented an adaptive data collection framework in an SDN environment. It is based on a confidence index and with the evaluation results, it allows reducing periodic monitoring overhead about 55-70 % with a good accuracy represented by the explained variance score that is about 0.7 and 0.8. Compared to the state-of-the-art solutions it has the advantage to be a lightweight solution for data-plane nodes. This is very interesting, especially in Software Defined Mobile Ad-hoc NETworks (SD-MANETs) where nodes are constrained in energy but we need a continuous collection of the data-plane state for the good functioning of the architecture. Then SDN advantages could be brought efficiently to other environments like MANETs without its shortcomings, mainly the overhead generated by communications between the separated control and data planes. And this is a good start to stem the pessimism around the application of the SDN paradigm to the Wireless Multi-hop networks because of the high cost of data plane and controller interactions.

In our future works, we will implement our proposition in a real platform, continue the evaluations by comparing it to other related techniques.

Fig. 1 .

 1 Fig. 1. SDN Controller in a Control feedback loop

  Fig. 2. COCO Framework

Algorithm 1 : 6 j 7 if j == β then 8 if ¬ Deviation occurred then 9 α 11 α 13 Tα 2 : 8 Send Asynchronously γ to the source; 9 else 10 Add m k to last collected ; 17 forecasted

 1678911132891017 COCO Adaptive Sampling Procedure 1 Upload β0+1 metric samples m k each T0, k := 0 to β0 ; 2 α k+1 ←-α k + 1 and Tα k+1 ←-(1 + α k+1 )T0 ; 3 k ←-k + 1 and j ←-0 ; 4 while Forever do 5 Upload m k at t k = t k-1 + Tα k ; ←-j + 1 ; k+1 ←min(α k + 1, αmax) ; 10 else k+1 ←-α k / (2 * γ) ; 12 end k+1 ←-(1 + α k+1 )T0 ; COCO Forecasting and Deviation Checking 1 Retrieve the first β0 + 1 metric samples m k ; 2 steps ←-α k + 1 ; 3 while Forever do 4 Retrieve metric sample m k ; 5 if (m k is the β th sample for α k ) then 6 if MASE(last collected, last forecasted) < MASE th then 7 No Deviation: α ←min(m k .α k +1, αmax) ; Deviation γ: α k+1 ←-m k .α k / (2 * γ) ; ←-Auto ARIMA forecasting ; 18 Add the steps th forecast to last forecasted ; 19 end

Fig. 3 .

 3 Fig. 3. α evolution over time for a trace (CPU monitoring data)

Fig. 4 .

 4 Fig. 4. Original traces and their corresponding collection with COCO

Fig. 5 .

 5 Fig. 5. The impact of η on the collection efficiency and quality

Fig. 6 .

 6 Fig. 6. αmax reached and Number of deviation alarms raised

Fig. 7 .

 7 Fig. 7. Efficiency (Number of samples collected compared to Fixed T 0 collection mechanism)

pp lic at io ns R eq ui re m en ts

			Optimization Policy or Constraints
	Desired Resource State A Actual Resource State and Performance Metrics	(By Management Applications)	Deny New Request Notify Exception
	(and Performance Metrics)
	Polled	Notified		Control Signal (Modify resource state with flow rules)
	Measurements


Kokouvi Benoit Nougnanke

Marc Bruyere

Yann Labit
email: ylabit@laas.fr

Low-Overhead Near-Real-Time Flow Statistics Collection in SDN

Keywords: SDN, OpenFlow, Continuous Monitoring, Data Collection, Monitoring Overhead, Time Series

In Software-Defined Networking, near-real-time collection of flow-level statistics provided by OpenFlow (e.g. byte count) is needed for control and management applications like traffic engineering, heavy hitters detection, attack detection, etc. The practical way to do this near-real-time collection is a periodic collection at high frequency. However, periodic polling may generate a lot of overheads expressed by the number of OpenFlow request and reply messages on the control network. To handle these overheads, adaptive techniques based on the pull model were proposed. But we can do better by detaching from the classical OpenFlow request-reply model for the particular case of periodic statistics collection. In light of this, we propose a push and prediction based adaptive collection to handle efficiently periodic OpenFlow statistics collection while maintaining good accuracy. We utilize the Ryu Controller and Mininet to implement our solution and then we carry out intensive experiments using real-world traces. The results show that our proposed approach can reduce the number of pushed messages up to 75% compared to a fixed periodic collection with a very good accuracy represented by a collection error of less than 0.5%.

I. INTRODUCTION

Software-Defined Networking (SDN), by separating the control plane from the data plane, provides good abstractions to the network control plane, that becomes programmable, flexible and more innovation in networking is eased. The OpenFlow API is used to control the forwarding plane.

Apart from being used to control the forwarding plane through flow rules installed by the control plane, OpenFlow exposes flow statistics (packets and bytes count, flow duration, etc.) and other relevant flow information (flow entry expiration and packet-In). These statistics and event-based information need to be gathered to enriched the global view of the network and be used by many control and management functions as traffic engineering [START_REF] Curtis | Devoflow: Scaling flow management for high-performance networks[END_REF], routing [START_REF] Akin | Comparison of routing algorithms with static and dynamic link cost in software defined networking (sdn)[END_REF], anomaly detection [START_REF] Zhang | An adaptive flow counting method for anomaly detection in sdn[END_REF], congestion detection, identification of architectural bottlenecks, etc. This flow-level measurement is done either actively, by querying the statistics such as traffic counters, flow duration, etc. on a pull basis with Read-State messages, or passively after an event as flow entry expiration and packet-In.

Moreover, for efficient and near-optimal control and management, the network global view needs to be up-to-date which means near-real-time network state observation. One practical way to do this is by collecting non-event-based data (traffic counters especially) periodically at high frequency (e.g. reports interval of few seconds). But OpenFlow's pull-based statistics retrieving may impose high overheads [START_REF] Curtis | Devoflow: Scaling flow management for high-performance networks[END_REF], expressed by the number of request and reply messages on the data plane.

To handle these overheads, i.e. provide timely accurate statistics to the control plane but with low overheads, pullbased adaptive approaches consisting of using adaptive polling rates, instead of a fixed one were proposed. The collection granularity adjustment is done either according to the stability of the statistic being collected, by comparing the difference between two consecutive data-points to one or several thresholds [START_REF] Chowdhury | Payless: A low cost network monitoring framework for software defined networks[END_REF], or by using a prediction-based approach which adjusts the reporting intervals according to the ability to provide good estimations of future data-points based on the history composed of the previously collected data-points [3] [5].

The aforementioned approaches provide good accuracyoverhead trade-offs, but we can do better by detaching from the classical OpenFlow request-reply model for the particular case of periodic statistics collection. We then propose a push and prediction based adaptive periodic OpenFlow statistics collection with low overheads and almost no accuracy degradation. This will be possible since the switch, source of the statistic to be collected is involved in the adaptive process. Then it automatically knows the adaptive collection points in time, and on its own will just push to the controller the statistic value without the need for request messages. Doing this way, the reduction of overhead will be more consequent. Hence, we propose a push and prediction based adaptive for OpenFlow statistics near-real-time collection, which ensures low overheads with almost no accuracy degradation.

The main contributions of this paper are summarized below:

• We propose an adaptive collection mechanism that decreases considerably the overhead in terms of the number of messages used to collect flow bytes count in near-realtime (Section III). • We implement the proposed algorithm using the Ryu Controller and Mininet, and after that, we carry out intensive experiments using real-world backbone and university data-center traces. The evaluation results are presented and analyzed (section IV). • We also present a direct use-case of our low-cost nearreal-time bytes count collection, bandwidth monitoring for traffic engineering and then we discuss our work regarding P4 and INT (Section V).

II. RELATED WORKS

For continuous statistics collection in SDN, especially flow bytes count, traditional network monitoring flow sampling techniques like sFlow, NetFlow/IPFIX could be used. But we don't need this extra instrumentation on OpenFlow Switches since they provide directly flow statistics, by maintaining records on packets matching installed flow rules. We just need to collect these statistics efficiently. However, OpenFlow will face some issues to provide the same level of flowlevel measurements compared to the sampling techniques mentioned above, especially the limitation of the number of flow rules that a switch can support. In light of this, [START_REF] Suárez-Varela | Reinventing netflow for openflow software-defined networks[END_REF] emulates NetFlow/IPFIX operation in SDN, providing OpenFlow compatible flow sampling methods. For near-realtime statistics collection with good accuracy-efficiency tradeoff, [START_REF] Cheng | Adaptive sampling for openflow network measurement methods[END_REF] proposes per-flow sampling with adaptive polling frequency, polling frequency being adjusted depending on whether the sampled traffic is stable or busy. In the same context to provide timely data-plane state information with low overheads, [START_REF] Chowdhury | Payless: A low cost network monitoring framework for software defined networks[END_REF] proposes a threshold-based adaptive scheduling algorithm for flow statistics retrieving by polling.

III. PERIODIC COLLECTION OVERHEAD HANDLING

A. Problem Statement

A statistic S to be collected in near-real-time, here an OpenFlow bytes count maintained by the data-plane switch, is modeled by: S : R + -→ N ∀u, t ∈ R + : u ≤ t =⇒ S(u) ≤ S(t).

The bytes count S(t) matching a specific flow, or a port bytes count in the interval [0, t), is a cumulative, non-negative and non-decreasing function. It must be part of the global and up-to-date view exposed by the control plane. Practically it will be collected periodically and control and management functions' requirements may define the period T 0 to be used.

Then the statistic S may be seen as S T0 = {s i } n i=0 , n → ∞, a large sequence of data-points s i at regular time interval T 0 with s i ≤ s i+k , ∀k ∈ N, representing an increasing trend time series. We denote the collected version at the controller level as Ŝ. If collected at a fixed T 0 , ŜT0 will be considered equal to S T0 (with the hypothesis that there is no packet loss and that data plane and controller communication delay is zero, ideally with an out-of-band control deployment).

For fine-grained near real-time control and management, T 0 needs to be as small as possible but this may generate a lot of overhead. A question that could raise at this point is: "Isn't any magic T 0 to be used that will give a good trade-off between the collection accuracy and the overhead ?"

Our approach consists of not to have to look for this magic T 0 . Instead, we will provide the samples of S(t) with the application chosen T 0 , but not all the samples will be really collected. We will effectively collect at adaptive frequency (T 0 , 2T 0 , 3T 0 , ...) and the non-collected data-points will be predicted based on the already existing ones.

B. Pushed and Prediction based Adaptive Collection

Our proposition is an easy to deploy, transparent and lightweight solution that comes from our early previous work [START_REF] Nougnanke | Novel adaptive data collection based on a confidence index in SDN[END_REF], where we propose, a generic adaptive algorithm based on a confidence index for collecting any type of information (CPU usage, switch information, etc). Here we leverage this adaptive algorithm that is enhanced and optimized for statistics presenting a particular pattern, an increasing trend (counters).

Our adaptive approach is built upon two mainstays: the adaptive push model and the power of time series forecasting. A push model because the classical OpenFlow pull model (request/reply) is inefficient since with a periodic collection or even with our adaptive collection mechanism, the collection points in time are known by the source node. This latter has to push to the controller at the collection points on its own without the need for a request message.

The adaptive frequencies or periods are represented by T α = (1+α)T 0 , where α is a confidence index expressing the degree of confidence between the collector and the node, source of the statistic, on the ability of the prediction method to provide good estimations of uncollected data-points. The confidence index α is constructed incrementally, it means that if at a given α the predictions are good, we pass to a new confidence level α + 1. Conversely, the confidence index will degrade when we do not have good estimations. Hence our push and prediction based adaptive collection solution is called COCO for COnfidence based adaptive COllection.

Our collection overhead reducing mechanism operates on two entities: the collector and the data plane agent as shown in Fig. 1. Their algorithms are presented in the next section.

C. Algorithms

The COCO Agent executes the adaptive push procedure (see Algorithm 1) as a software-based monitoring solution on the node (switch), of course in collaboration with the SDN controller. In fact, for a statistic that is required to be up-to-date at T 0 granularity at the controller level, we attach the confidence index α, and T α = (1 + α)T 0 is the period with which this information (the samples ŝi = s i ) will be pushed. The reporting interval begins with a low period T α = (1 + α)T 0 = T 0 (α = 0) and α will be increased each β data-points pushed if a deviation alarm is not raised by the controller. Otherwise, α will be decreased according to the deviation degree γ. α evolves according to an AIMD (Additive-Increase/Multiplicative-Decrease) pattern.

Algorithm 2 describes the controller side process, where the collector processes collected data-points ( ŝi ), forecasts uncollected ones ( ŝj ) and raises deviation alarm if needed. The controller receives samples ŝi from the node each T α = (1 + α)T 0 . Firstly it forecasts step equals to α + 1 next data-points. The first α forecasts will be used as an estimation of { ŝj } α j=1 uncollected data-points between ŝi and ŝi+1 on a T 0 basis. Secondly, The last forecast the α + 1 ith one will be compared to ŝi+1 to estimate the prediction algorithm accuracy. For a given α, after collecting β data-points ŝi , an accuracy deviation checking is done using these data-points { ŝi } β i=1 and their corresponding forecasts (the α + 1 ith) to compute an indicator of the prediction error. This indicator is compared to the threshold η, when violated, a deviation alarm of degree γ (default γ = 1) is raised. γ is sent to the node for updating (decreasing) α then T α .

To compute the prediction error indicator we use Mean Absolute Scaled Error (MASE) [START_REF] Hyndman | Another look at measures of forecast accuracy[END_REF], a time series forecasting accuracy metric. It has many desirable properties compared to existing error metrics (e.g. MAPE). MASE is computed using β last forecasts, β last collected data-points, according to the previous data-points used for prediction (See Eq. 1).

M ASE = 1 β β k=1 ( | ŝk -pred( ŝk )| 1/(β -1) β k=2 | ŝk -ŝk-1 | ) (1)
It compares the actual forecasting method to the one-step naive forecast computed in-sample with a ratio of their respective Mean Absolute Errors (MAE). Then when MASE < 1, the actual forecasting performs well than the naive one and vice versa. We transform this threshold of 1 to η.

Concerning uncollected data-points forecasting, the two following statistical techniques are investigated:

1) The popular Box-Jenkins ARIMA family of methods shortened as ARIMA (AutoRegressive Integrated Moving Average): It exploits information embedded in the autocorrelation pattern of the data. Estimations are done based on the maximum likelihood. The univariate statistical time-series ARIMA model parameters (p, d, q) are computed automatically using Auto ARIMA [10]. 2) Holt's Trend Corrected Exponential Smoothing: Or Double Exponential Smoothing is used to do forecasting when the time series presents a particular trend (increasing or decreasing) without a seasonal pattern. For this model, we may retain two main hyper-parameters: α * the smoothing factor for the level (mean) and β * the smoothing factor for the trend or slope smoothing. For convenient forecasting, we need to satisfy a minimum training set size which generally is 50. Then, we define a specific β, β 0 equals to 50 (default) for α = 0 at the beginning. Moreover, we emphasize recent data-points supposing older ones less relevant, and also for the fact that we are doing an on-line prediction, we set a sliding window of size W = 1.5*β 0 for previous data-points to be used for the prediction.

The parameters used in our overhead reduction proposition are summarized in TABLE I.

TABLE I PARAMETERS

Parameters Description α

Confidence index on the ability to provide good estimations of uncollected data-points, ∈ [0,..,9] T 0

The required time granularity for statistic updates Tα = (1 + α)T 0

Effective pushing period for the confidence index α β 0

The minimal number of samples to be collected at the beginning on T 0 basis β Number of samples for a given α after which a deviation checking is done allowing α update η A threshold on the prediction error γ

The degree of a deviation alarm IV. EVALUATION

A. Setup and Evaluation Metrics

We evaluate our proposition using the topology in Fig. 2 on a virtual machine of 32 GB of RAM and 20 vCPUs.

With tcpreplay, we replay real network traffics from the two following public anonymized datasets. UNIV2 [START_REF] Benson | Network traffic characteristics of data centers in the wild[END_REF]: A university data-center trace collected on 22 January 2010. We Two main metrics are used in this evaluation. We compute these metrics in comparison to a fixed push mechanism at T 0 (which will be the case when we use for example sFlow).

Accuracy: To evaluate the collection error induced by our adaptive push mechanism compared to a fixed T 0 one, we use the Mean Absolute Percentage Error (MAPE). It's expressed in percentage, the lower this value, the better it is.

Overhead: We express the overhead reduction with the percentage of the number of messages with a fixed push collection compared to the effective number of messages with COCO. We want this value to be as high as possible.

B. Overhead Reduction and Collection Accuracy

Fig. 3 and Fig. 4 show the percentage of messages reduced (on the left) and the collection error with MAPE (on the right) according to the threshold η respectively for the traces UNIV2 and MAWI, for 2 combinations of (β 0 , β): (50, 10), (100, 10).

For all η > 0, meaning a fraction of inaccuracy is authorized on the collection, we always have a reduction of the overhead compared to the fixed periodic push collection. This reduction increases when η increases and may stabilize. We achieve up to 75% of reduction. And we have this with a very low error (less than 0.5 %) on the whole collection compared to the fixed T 0 collection. This very low error for both MAWI and UNIV2 increases with η and the reduction percentage.

C. Computation Time and Synthesis

When a data-point ŝi is collected, before collecting the next one ( ŝi+1 ) some computations are done, like computing estimations of missing data-points, and sometimes deviation checking. This inter-data-points processing time needs to be bounded for the good functioning of our algorithm and also this time may impact the possible values of T 0 . In Fig. 5, we show the range of this computation time for both ARIMA and Exponential Smoothing forecasting. Exponential Smoothing gives better results (around 0.025 seconds). ARIMA gives computation time around 0.125 seconds. From Fig. 3 and Fig. 4, ARIMA and ES are providing the same quality of forecasting even with the big difference in terms of computation time. Hence for our particular case where the time series presents an increasing trend without seasonality pattern, the better choice for uncollected datapoints is Double Exponential Smoothing.

V. USE CASE AND DISCUSSION

A. Use Case: Network Utilization Monitoring

With our lightweight near-real-time bytes count collection, we contribute a lot to Traffic Engineering in SDN. As part of TE, we can mention bandwidth management that consists of measuring and controlling traffic on network links. Fig. shows bytes count evolution collected with our adaptive push mechanism compared to a fixed push collection and the bandwidth estimated from this statistic. The bandwidth is expressed in bytes per second (Bps).

B. Discussion

The objective here is to position our work with the recent directions in SDN intending to take complete control over the data-plane with P4, INT, and data-plane programmability.

P4 (Programming Protocol Independent Packet Processor) [START_REF] Bosshart | P4: Programming protocol-independent packet processors[END_REF] is a high-level language to express how a switch is to be configured and it has to process packets. It's worth pointing out that P4's goal is not to replace OpenFlow, but OpenFlow may be seen as part of P4 and is one of many possible programs (e.g P4Runtine) to describe the forwarding behavior. Since there, our OpenFlow-based implementation can be considered P4-compatible. Moreover, instead of running our solution as a software agent on data-plane elements, it can be directly integrated into programmable switches through P4.

Generally implemented with P4, INT (In-band Network Telemetry) [START_REF] Kim | In-band network telemetry via programmable dataplanes[END_REF] is a new abstraction that allows data packets to collect switch internal state (queues size, queues latency, byte counts, etc), enabling monitoring of the network state by the data-plane. Even with this approach sometimes we need to collect from a selected node statistics to a central server. For example in [START_REF] Harrison | Network-wide heavy hitter detection with commodity switches[END_REF] a low-overhead network-wide heavy hitter detection is done where large flows are detected locally in the data-plane with a per-key threshold that triggers reports to a central coordinator. A similar approach is used in [START_REF] Castanheira | Flowstalker: Comprehensive traffic flow monitoring on the data plane using p4[END_REF]. At the triggering moment, our adaptive solution will be useful in reducing the overall monitoring task cost.

VI. CONCLUSION

This paper proposes an efficient adaptive push algorithm for near-real-time flow statistics collection done as a periodic collection. It is based on time series prediction that guides the adjustment of the adaptive periods. Our proposition has the advantage of being lightweight and easy to deploy. We evaluate it using real-world backbone and university datacenter traces. It reduces the collection overhead up to 75% compared to a fixed periodic push collection mechanism at the price of almost no accuracy degradation with less than 0.5% of collection error.

As future works, we will investigate the use of machine learning forecasting methods as RNN (Recurrent Neural Networks). And also we will implement our solution with P4 for data-plane elements instead of using it as a software agent.

Fig. 1 .

 1 Fig. 1. COCO Collection Architecture

Algorithm 1 : 5 Push ŝi at Tα ; 6 if 7 if ¬ Deviation occurred then 8 α 2 : 8 No 11 SendForecast

 156782811 COCO Agent: Adaptive Push Procedure 1 Push β0 data-points ŝi each T0, (α = 0) ; 2 α ←-α + 1 and Tα ←-(1 + α)T0 ; 3 i ←-i + 1 ; 4 while Forever do ( ŝi is the β ith data-point for α) then ←min(α + 1, αmax) ; COCO Collector: Forecasting and Deviation Checking 1 Collect the first β0 data-points ŝi ; 2 α ←-α + 1 and steps ←-α + 1 ; 3 while Forever do 4 Collect data-point ŝi ; 5 Add ŝi to last collect ; 6 if ( ŝi is the β ith data-point for α) then 7 if error indicator(last collect, last forecast) < η then Deviation: α ←min(α+1, αmax) ; α ←-α / (2 * γ) ; Asynchronously γ to the switch; steps future data-points ( ŝj) using previous ŝi ; 18 Add the steps ith forecast to last forecast ; 19 end

Fig. 2 .

 2 Fig. 2. Experimental Setup Topology

Fig. 3 .Fig. 4 .

 34 Fig. 3. UNIV2: Overhead Reduction and Collection Error (MAPE)

6

 6

Fig. 5 .

 5 Fig. 5. Compute time

Fig. 6 .

 6 Fig. 6. Bandwidth Estimated from the bytes count collected