B Eickhoff

L Mazzola

Y Bezin

G Tucker

H Stradtmann

A Haigermoser

H Chollet

J Landais

Track loading limits and cross acceptance of vehicle approvals

Keywords: Cross-acceptance, rail vehicle, approvals, track loading, track forces, track dynamics, authorisation, certification, simulation

The requirements for track loading limits are one of the main barriers to simple crossacceptance of vehicles where rolling stock that is already operating successfully in one (or more) networks has to be re-tested before it can be approved for operation on another network. DynoTRAIN WP 4 studied this area in order to determine whether the additional requirements were justified, or if the process could be made much cheaper and simpler without increasing the risk of track deterioration for the networks.

The review of national requirements identified modified criteria and limit values for track forces in some member states but these can be obtained from additional analysis of the normal test results with no new tests required. The influence of design rail inclination has also been found not to be significant, provided a realistic range of wheel-rail contact conditions are included in the tests. For line speeds greater than or equal to 160km/h the current standards for track construction across the member states appear to be similar. On lower speed lines in some countries, a 'weaker' track condition may require a lower limit on one of the vehicle assessment parameters.

Track dynamics modelling has shown that the vehicle assessment parameters used in international standards are suitable for use in cross-acceptance for track forces. The use of multiple regression analysis allows the estimated maximum value for relevant parameters to be evaluated for different target conditions and then compared with the appropriate limit value, or with values for existing, comparable vehicles. Guidance has also been provided on the relevant parameters to consider when developing operating controls for different types of track deterioration.

Introduction

The requirements for track loading limits are one of the main barriers to simple crossacceptance of vehicles where rolling stock that is already operating successfully in one (or more) networks has to be re-tested before it can be approved for operation on another network. DynoTRAIN Work Package 4 (WP4) studied this area in order to determine whether the additional requirements were justified, or if the process could be made much cheaper and simpler without increasing the risk of track deterioration for the networks.

The objectives of WP 4 were to:

 improve the process of cross-acceptance between different countries;  reduce the number of additional tests required for approval onto non-TSI compliant infrastructure;  develop limit values related to infrastructure construction and maintenance;  clearly identify specific local requirements;  provide proposals for cross-acceptance processes, including the use of simulations;  provide proposals for operating limits dependent on infrastructure conditions;

A detailed review of the current requirements for track loading in TSIs, ENs, UIC leaflets, other international documents and national rules was carried out to check for differences. Any relationships of these requirements and limit values to track construction or maintenance standards were also noted. Existing track dynamics models were used to investigate relationships between the vehicle parameters assessed during approval tests and the forces and accelerations likely to cause deterioration in the track structure.

Information was gathered from a number of infrastructure managers regarding the construction and maintenance standards for different track categories and line speeds. Any specific approval requirements for rolling stock were also collected. The development work being undertaken by CEN TC256 WG10 on EN14363 was also reviewed to determine if any additional parameters should be considered.

The data from the DynoTRAIN WP 1 tests was analysed to investigate the relationships of the measured track loading parameters with influencing factors such as speed, cant deficiency and curve radius. Using track data from DynoTRAIN WP 1 and WP 2 and wheel and rail profiles from WP 3, a number of vehicle dynamic simulations were also undertaken to confirm the relationships found and investigate others, such as with wheelrail friction, that were not available from the tests.

WP4 activities

The following activities were undertaken by partners in WP4:

 Review of relevant requirements in national, international and bi-/ multi-lateral documents  Review of ORE, ERRI and UIC reports to seek to understand the background to current requirements  Detailed track dynamics modelling to check the relevance of the identified parameters and sensitivity to track and operating conditions  Survey of infrastructure construction and maintenance requirements across networks to identify similarities and differences  Use of data from WP 1 tests to examine the relationships between track and vehicle response  Vehicle dynamic simulations to extend the range of conditions considered  Investigations into the use of multiple regression analysis in compatibility assessment These various activities are explained in more detail below.

Review of relevant requirements

International documents

A range of parameters are applied for vehicle approval, including cross acceptance, and for technical compatibility checks. For track loading assessment the following parameters are used in the international standards ([1] and [START_REF]Testing and approval of railway vehicles from the point of view of their dynamic behaviour -Safety -Track fatigue -Running behaviour[END_REF]) and TSIs ( [START_REF]EC: Commission Decision concerning a technical specification for interoperability relating to the rolling stock sub-system of the trans-European high-speed rail system[END_REF], [START_REF] Cr | EU: Commission Decision concerning a technical specification for interoperability relating to the rolling stock subsystem -'Locomotives and passenger rolling stock' of the trans-European conventional rail system[END_REF] and [START_REF]technical specification of interoperability relating to the subsystem 'rolling stockfreight wagons' of the trans-European conventional rail system[END_REF]): These assessment parameters are measured on the test vehicle, there is no measurement on the track itself, and therefore the parameters are not directly assessing the track behaviour. However it is assumed that the measurements taken give a good indication of the expected effect of the assessed vehicle on the track, in terms of forces in the track structure (rails, pads, sleepers, ballast) and the resulting deterioration.

 Qqst -Quasi-
Qqst describes the quasi-static vertical force on the outer rail in plain curve sections of curves below 400m radius and is associated with track (and especially rail) fatigue. It is also probable that Qqst could be relevant for assessment of track settlement.

Q or Qmax describes the dynamic vertical force on the rail and is associated with deterioration of track components (rails, fastenings, sleepers, ballast).

Yqst describes the quasi-static lateral force on the outer rail in plain curve sections of curves below 400m radius and is associated with lateral wear of the outer rail, lateral resistance of rails, welds, joints and fastenings and the resistance of rails to bending stresses.

ΣYmax is used to control the risk of lateral shifting of the whole track (sleepers and both rails) under the influence of cant deficiency forces. The force is a running average over 2m and modelling shows a rather consistent trend between the sleeper force and the input wheel-rail lateral force.

Hmax is used in place of ΣYmax in the simplified measuring method when measurement of lateral wheel-rail forces is not carried out and is an indirect alternative. It considers the lateral force at the axle level.

Bqst describes the resultant force (combined vertical and lateral) on the outer rail in plain curve sections of curves below 400m radius and is associated with track (and especially rail) fatigue.

Tqst is a proposed new parameter from CEN TC256 WG10 SG8 which is related to rail surface damage and this also appears to be relevant and should be considered as an addition.

The limit values related to these various parameters have been developed over many years and, in some cases, the background for the limits is not clear today. Some limit values are based on scientific studies, while others are based on experience and comparison with existing vehicles which were at that time believed to be at the limit of acceptability for the track.

National and multi-national documents

In order to understand more completely the current requirements, in the area of track loading limits, additional national or local requirements were also analysed. This work concentrated on requirements found in the Member States of the European Community as additional to the international requirements in the TSIs. Twenty National Reference Documents were available at the ERA Cross Acceptance unit website and the following clauses from these documents were analysed:

Running safety and dynamics

Including tolerance of vehicle to distortion of track, running on curved or twisted tracks, safe running on points and diamond crossings, etc.

Track loading compatibility parameters

E.g. dynamic wheel force, wheel forces exerted by a wheel set on the track (quasi static wheel force, maximum total dynamic lateral force, quasi static guiding force).

Checklists from bi-or multilateral agreements for cross acceptance and data from the "International Requirement List" IRL were also analysed and some additional requirements were identified.

Background documents

Where possible, the background documents were reviewed. These included the work of ORE committees B10, D71, C138, ERRI C209, the UIC group which reviewed UIC518 to produce the 2009 version [START_REF]Testing and approval of railway vehicles from the point of view of their dynamic behaviour -Safety -Track fatigue -Running behaviour[END_REF] and the CEN group currently reviewing EN14363 [START_REF]Railway applications -Testing for the acceptance of running characteristics of railway vehicles -Testing of running behaviour and stationary tests[END_REF].

ORE committee B10 'Constructional arrangements for improving the riding stability and the guiding quality of electric and diesel locomotives and vehicles', summary report No.15, October 1974, makes reference to the limit value for ΣYmax , but gives no additional information on the background.

ORE committee D71 'Stresses in the rails, the ballast and in the formation resulting from traffic loads', summary report No. The vertical stiffness appeared to have the most influence on the permissible maximum values for Y and Q forces. An increase in sleeper spacing from 0.6m to 0.7m resulted in an increase of at most 10% in the maximum stresses, the effect of variation in axle spacing was negligible and the change from UIC60 to UIC54 rail gave an increase in stresses by at most 10%. No international limit values were developed in this study, the conclusions state that 'the limiting values of Y and Q should be determined by each railway on the basis of local conditions and requirements'.

RP9 was the final report and compared the limit values obtained by C138 under specific test conditions with the Y and Q forces actually obtained in service so as to assess:

 The risk of lateral displacement of the track (ΣY)  The stressing of the rails (Q, Y).

The work of C138 considered the ΣY forces acting over a 2m length and this criterion is still used today. However over time there have been changes in the way in which the values are calculated, see C138 RP9 Sec 2.1. This states that in UIC432 the '2m' value was the value which was exceeded for 2m, say 2m(max). This assessment was simple to make visually on a paper trace of the measured signal using a straight-edge of 2m equivalent length. C138 RP9 and more recent documents assess the '2m' value as a running average over the 2m length, say 2m(ave). This assessment was easier when computer assessment of measured signals became the usual process.

The resulting values are often similar but the running average is always greater than or equal to the maximum value, (2m(ave) ≥ 2m(max)) and in some cases the difference can be large, having the effect of making the limit values more difficult to meet.

ERRI C209 was tasked to assess the specifications in UIC518:1995 and for this purpose the railways made available and analysed already existing test results. DT 338 considered vehicles running at speeds between 140 and 200 km/h using tests on locomotives, coaches and a body-tilt power car. Some small exceedence of the limit values for Qmax was noted with heavy locomotives. DT 339 considered vehicles running at speeds below 140 km/h and noted that the Qmax limit of 170 kN with an axle load of 22 t was not met in curves. No specific comments on track force limit values were made in DT 337 (speeds >200 km/h). The C209 Rpt1 proposed a Qlim criterion related to vehicle speed, a version of which was adopted. Rpt2 made a number of proposals (for example changes to some of the acceleration limit values), which were incorporated in the February 1999 draft of UIC 518 but the track force limits were unchanged.

Results of review of requirements

The analysis showed that:

 Rules are sometime widely spread in laws (generic requirements), national standards, national regulations  Sometimes it is required to follow (not defined) requirements of the Infrastructure Manager  By far not all documents with requirements are available  In most cases only the international standards ([1] and [START_REF]Testing and approval of railway vehicles from the point of view of their dynamic behaviour -Safety -Track fatigue -Running behaviour[END_REF]) are applied without additional track loading requirements  The main barriers for mutual recognition (cross acceptance) are differences in rail inclination leading to different contact geometry conditions Specific national requirements may result from national legal requirements and/or may reflect special conditions of track construction or maintenance. In most cases no scientific justification is given.

Detailed track dynamics modelling

To improve the understanding of track damage mechanisms and parameters relevant for vehicle track-interaction, existing track dynamics simulation tools were used. These tools allowed an insight into the interaction between vehicle and track for a range of frequencies not covered in other work. They were aimed at understanding the relationship between track loading limits presently in force within the EU countries and the actual loading applied on different track components, such as rail pad, fastening, sleepers and ballast. These studies concentrated on locomotives and laden freight wagons, as these are generally the most critical vehicles for track forces. The outputs from these simulations were also analysed in terms of track damage considering three aspects; vertical track settlement, track component fatigue and rail surface damage.

This goal was achieved by means of numerical simulations which allow for numerical experiments, where not only the forces in the track elements but also wheel-rail contact forces are estimated. Interesting results were obtained from a sensitivity analysis on track constructions and their behaviour both in tangent track and in curves, assessed in a deterministic way and providing information on the dependence that track forces have on track loading assessment quantities suggested by the standards.

Finite element track model

To this end, a mathematical model of train-track interaction was developed using a finite element schematisation of the flexible track and a multi-body model of the vehicle. The train-track interaction model reproduces in the time domain the dynamic interaction effects associated with the coupling of the two sub-systems via wheel-rail contact, including the movement of the train relative to the track [START_REF] Diana | Interaction between railroad superstructure and railway vehicles[END_REF].

The vehicle model is based on a multi-body formulation, according to which the vehicle (or the complete train set) is divided into modules, each one representing one carbody or one bogie, this latter inclusive of the wheelsets [START_REF] Diana | Interaction between railroad superstructure and railway vehicles[END_REF], [START_REF] Bruni | Lateral dynamics of a railway vehicle in tangent track and curve: tests and simulation[END_REF]. The equations of motion for each module are then written with respect to a local moving frame travelling along a path defined based on the ideal geometry of the line. The equations of motion for the vehicle system are linearised with respect to kinematics only, by assuming the motion to be a small perturbation around the large motion of the moving references introduced, whereas the non-linear effects associated with wheel-rail contact and with the behaviour of some suspension components are accounted for without introducing simplifying assumptions.

The model considers three-dimensional motion of the system, allowing the analysis of the non-stationary behaviour of a single rail vehicle or of an entire trainset running in tangent and curved track.

In the present study, weak interaction is assumed among the vehicles forming the train set, and hence the mathematical model is confined to one single vehicle, in particular a Locomotive vehicle.

The track model is based on a finite element schematisation of the track, in which the rails are modelled by Euler-Bernoulli beam elements, while the sleepers are considered as concentrated masses spaced by 0.60 cm. The rail pads and fastening devices are represented by linear visco-elastic elements connecting the rails to the sleepers, and finally the ballast is partly modelled as visco-elastic layers, in lateral and vertical direction, connecting the sleeper and the ground. Furthermore approximately half of the ballast mass is considered as rigidly attached to the sleeper. Although this model is a simplified one from the point of view of the ballast/sleeper, it was demonstrated by preliminary analyses to be adequate for the purpose of this work.

The finite element model is completely three dimensional, although the longitudinal displacements of the nodes are decoupled from the vertical and lateral displacement components and are therefore neglected in the analysis. In this work the track model consisted of 300 sleeper spans, corresponding to a track of 180 m length. Figure 1 shows a sectional schematic of the model described above.

Figure 1: Track model schematisation

The equations of the finite element track model are written with respect to a fixed reference frame, and therefore during the simulation the train travels over the track model.

In particular, wheel-rail contact forces represent the coupling between the track model and the vehicle model. In fact they are functions of both vehicle and track motion, therefore the model used for the simulation of the dynamic interaction between vehicle and track solves, for each integration step, the system of equations iterating at each time-step on the terms of the forces, which are functions of the degrees of freedom of both the systems.

The vehicle and track subsystems are described by two different set of equations which are simultaneously integrated in the time domain using a modified Newmark time-step procedure. The co-simulation procedure is based on the coupling of train and track dynamics as result of the contact forces exchanged at the wheel/rail interface, as shown in Figure 2.

Figure 2 : Numerical model for train/track interaction: vehicle and track as separated and interacting sub-systems

The model of wheel-rail contact used to reproduce the dynamic coupling between the vehicle and the track is a pre-tabulated, multi-Hertzian one. Prior to the simulation, wheel/rail contact geometry is processed starting from measured or theoretical wheel and rail profiles, and contact parameters required to compute wheel/rail contact forces are stored in a contact table. The parameters in the contact table include the contact angle, the variation of the wheel rolling radius with respect to the nominal one, the curvatures of the wheel and rail profiles in the contact point region [START_REF] Bruni | Lateral dynamics of a railway vehicle in tangent track and curve: tests and simulation[END_REF].

Based on wheel and rail displacements and velocities in the contact point, the value of the contact forces is derived. An elastic co-penetration in the normal direction is computed accounting for the relative wheel/rail displacements and contact angle, then the normal force is identified according to the Hertzian formulae. The longitudinal and lateral creepages are then defined, and the creep force components are computed using the heuristic formulae by Shen, Hedrick & Elkins [START_REF] Braghin | Experimental and numerical investigation on the derailment of a railway wheelset with solid axle[END_REF].

The contact forces are then transformed to the global reference in which vehicle and track displacements are defined, and the vectors of generalised forces Fcv and Fct acting respectively along the vehicle and track coordinates.

Combined use of vehicle and track dynamics models

Alongside the modelling technique described above another modelling approach was taken which considers the vehicle-track dynamics interaction and the track forces distribution in two consecutive and independent steps. The main purpose of this approach is to allow the simulation of a high number of curving conditions together with a high sample of representative track irregularities. There is a small loss of accuracy in the process due to the non-interactive prediction of wheel and rail forces, but this mainly affects frequencies higher than those of interest within the frame of EN14363 for which forces are low pass filtered at 20Hz and this approach was also compared to the previous complete system interaction modelling to ensure a valid interpretation of the results.

The various vehicle service conditions tested with the method are the vehicle speed and loading condition but also wheel profile state (i.e. new or worn) and cant deficiency. The cant deficiency in curves generates a non-compensated centrifugal force that loads the outer rail and also increases the lateral cornering forces imposed by the leading outer wheel. The simulation occurs in two steps:

1) the vertical and lateral wheel forces generated at one bogie in curving situations are predicted from a commercial railway vehicle dynamics software assuming a simplified linear elastic track model and using two non-linear vehicle models: a locomotive (23t axle load) and a container freight wagon (20t).

2) the predicted forces are used as input function onto a fixed length flexible track model. The dynamic load from the leading axle is applied at a fixed location directly above one sleeper, while the load from the second axle is applied at the specified distance from the bogie geometry. The resulting ballast and rail pad forces under the leading axle are used as output.

Figure 3 shows the input load predicted from the first step simulation for the locomotive, across a 12km track site with decreasing curve radius from 8km to 600m (EN14363 test track zone 2). As the curve tightens, the cant deficiency increases and the vehicle speed is adjusted in steps so that a representative set of cant deficiencies are simulated within the limits specified by EN14363, whose upper limit is 1.1 times admissible cant deficiency (with cdadm = 130mm). The maximum vehicle speed considered is 1.1 times the vehicle maximum running speed (Vmax = 200km/h). The vertical forces are thus increasing on the high rail of the curve while reducing on the lower rail. At the same time the lateral forces imposed by the leading axle on the high rail are clearly increasing towards the end part of the track, as well as on the low rail to a lesser extent.

After these input forces are applied to the flexible track model, the corresponding maximum forces on the rail pad and sleeper directly below the input forces can be plotted. Figure 4 shows the flexible track with a snapshot of the dynamics loading and the position of the measured sleeper response. Example outputs of this approach are shown in Figure 6 and Figure 7.

Results of track dynamics modelling

In the following an example of numerical experiments in tangent track is described together with the main results from the time histories. The numerical experiments were carried out considering a locomotive (based on the BR120 tested in WP1) running at its maximum service speed (i.e. 200 km/h) along a straight track. A preliminary analysis was carried out neglecting the track irregularity. The sleeper and rail fastening force time histories were compared for a section situated at mid-length of the overall track length. Different track constructions were considered: soft, typical and stiff, being considered homogeneous for all the track length. The values chosen were based on the EUROBALT project and are given below with vertical stiffness in kN/mm and damping in kNs/m (all per sleeper end). Figure 5 shows the sleeper and rail fastening forces for the passage of the locomotive.

Figure 5 : Vertical forces in the track; rail fastening (left) and ballast (right)

The immediate conclusion is that if a reduction of track loading is desired, a soft track configuration or at least a typical configuration is preferable. However, when accelerations at the rail pad and sleeper were analysed instead of forces, it was found that when a stiff track is adopted, a lower level of peak acceleration can be obtained with respect to softer tracks. This raises the question of the dependence of track degradation on different mechanical phenomena induced in the track during train passage. Most track degradation models assume ballast settlement to be caused by the forces generated under the sleeper. However, it is a known fact that too high levels of acceleration can be a cause of accelerated degradation, known as "ballast liquefaction". Also, the amount of track settlement produced for the same level of forces generated under the sleepers is dependent on the type of ballast, and hence on the vertical stiffness of the track.

The relationships between the measured vehicle parameters and the forces in the track components were also studied.

The aim of this task was to determine the influence of vehicle service parameters on the forces in the track elements. The vehicle service parameters as understood for this work include:

 The vehicle type and its axle load  The vehicle speed  The track zone (with a focus on zone 2large radii, and zone 4tight curves)  The cant deficiency  The wheel and rail profiles types and condition  The wheel-rail friction coefficient Different infrastructure constructions and levels of track maintenance quality were compared in terms of forces transmitted through the rail pad and through the sleeper to the ballast. Sleeper acceleration was also investigated. This analysis can be used to identify possible margins for an infrastructure type-related increase of the limit values to be used for compatibility checks between vehicle and infrastructure. Additionally, the effect of vehicle service parameters, such as vehicle speed, was investigated and the subsequent variation of track loading considered.

As an example, Figure 6 shows that Qmax has an almost linear relationship with the sleeperballast and rail pad forces. Figure 7 shows that Yqst also has a relationship with rail pad forces and sleeper-ballast forces, but in this case the relationship is not linear.

Simulations of the track shift limit with non-linear models

Up to now, the virtual homologation exercise has been limited to studying lateral forces on a perfectly elastic track model and comparing them to the Prud'Homme limit, which can be considered as a Coulomb limit adapted to a track structure. Looking to the future, the idea is to simulate directly the permanent deformation with an advanced track model where the elements are highly non-linear.

Classical elements, such as the rails, are modelled as elastic, uniform and straight Timoshenko beams, supported discretely by sleepers or blocks considered as masses, and the railpads and fastenings on the sleepers, modelled by linear springs and dashpots. But the interactions between sleepers and ballast have been divided into three dry friction elements according to the contributions of bottom (SS #2 on Figure 8), shoulders and crib ballast [START_REF] Prud'homme | La résistance de la voie aux efforts transversaux exercés par le matériel roulant[END_REF].

The under-sleeper stiffness (SS#1) is represented by an exponential expression in order to reproduce a very soft stiffness at small displacement, and a stiffer one for common displacement when the wheelset is passing, corresponding to EUROBALT results.

This unilateral contact is initially loaded by the weight of the elements and can be unloaded by the flexibility of the rails just before the wheelset passes. The vehicle and wheel/rail contact models Two freight wagons have been modelled to represent the Prud'homme cases. The carbody masses are varied to obtain 6, 12 and 17 t/axle. In each case, a pure lateral force is directly applied on the inner face of the outer wheel at rail level.

A preliminary task was to identify a set of track parameters that closely matched the Prud'Homme results and a "W" measurement of a standard track with wooden sleepers, in an optimisation procedure minimising the difference between the prediction and the experiments (Figure 9). Two series of computations, involving a vehicle loaded at 17 t/axle, were performed, with applied lateral forces of 4 t or 6 t.

Figure 9: Comparison between simulations and two sorts of experiments

The magnitude of the elastic deformations in vertical and lateral are approximately correct at both the first and the second wheelset passage. Both residual deformations are reached. Consequently this model can be used to simulate the lateral permanent shift on timber ties and ballasted CWR track. A similar approach using DB's results [START_REF] Seraphim | Gleisquerfestigkeit des destabilisierten Betonschwellengleises B70-W60-1667[END_REF] has been used to define the parameters of a concrete sleepered track.

The estimation of the lateral track-shifting force limit (ΣYmax) in the same conditions as the Prud'Homme tests is not trivial because, with only a few dry friction elements in the model, some discontinuities are revealed in simulations which are not present in experiments. Some limits are presented in Figure 10.

Figure 10: Simulated lateral resistance versus axle load for concrete and timber sleeper

As observed in the Prud'Homme experiments, numerical results show a linear relationship between the lateral track-shifting limit and the axle load. The slopes are close to the Prud'Homme criterion that proposed 0.33. The inaccuracy is partly due to the way of determining the lateral track-shifting limit and partly to the discretisation. More research on a more complex model could also improve the agreement. This would also need some additional experiments, which can be defined with the model parameter analysis.

With the identified models, the Prud'Homme experiments (wooden sleepers) are quite well reproduced for axle loads between 10 and 16t.

Once the track models are validated, the parametric studies show a small dependency on sleeper spacing and on the rail type for timber sleepers, and a weak dependency for the concrete ones. The model shows less sensitivity than the results compiled in ERRI reports [START_REF] Erri | Improved knowledge of forces in CWR track (including switches)[END_REF]. A first attempt had been made at the simulation of buckling but the sensitivity to temperature needs more development.

From this type of model, it can be proposed that in the future, in the virtual homologation process it will be possible to use non-linear advanced track models to simulate the parameter dependent track shift and not only the reaching of the lateral track-shift limit, used with the present linear models.

Survey of infrastructure construction and maintenance requirements

Using a questionnaire it was found that most networks use a track categorisation system based on some combination of line speed and service density. Across the member states who replied to the questionnaire, track design and construction rules vary depending on track category. In most cases the category can be linked back to line speed to allow a comparison between the different track constructions that a train of a given operating speed would be likely to encounter if it did move between different member states. This comparison was carried out based on existing standards in the member states and is not necessarily a direct reflection of the construction of all existing lines, which may have been built to older standards.

For line speeds greater than or equal to 160 km/h, the current standards for track construction across the member states appear to be similar. Differences between construction and maintenance standards are, as expected, more significant for lower speed lines. For example, track with a line speed of 140 km/h will have a 600 mm sleeper spacing in Germany, whilst for the same line speed in GB there could be a sleeper spacing of 700 mm. On lower speed lines in some countries a 'weaker' track condition may require a lower limit for one of the vehicle assessment parameters.

Relationships between track and vehicle response

The LOC & PAS TSI [START_REF] Cr | EU: Commission Decision concerning a technical specification for interoperability relating to the rolling stock subsystem -'Locomotives and passenger rolling stock' of the trans-European conventional rail system[END_REF] allows in clause 4.2.3.4.2.2 in certain cases (e.g. when a vehicle exceeds the limit value for the quasi-static guiding force), that the operational performance of the rolling stock (e.g. maximum speed) may be limited by the infrastructure, considering track characteristics (e.g. curve radius, cant and rail height) in order to reduce loading below the limit value. This practice is already used on many networks in cases where the track design or maintenance status is not good and/or the track layout is demanding. The application of this rule is not easy as the dependencies between the relevant quantities and the operating conditions (i.e. the track layout, track geometric quality and friction levels) are not currently well defined.

Multiple regression can be a useful tool to describe the behaviour of a vehicle in terms of these dependencies. For each of the identified track force criteria, the sensitivity to different input parameters was therefore investigated by multiple regressions.

Vehicle dynamics simulations

To cover a wider range of conditions than those tested in WP1 [START_REF] Zacher | DynoTRAIN WP1 Stationary and on-track tests[END_REF], vehicle dynamic simulations were also used. The simulation cases covered a wide range of operating conditions, including those observed during test runs according to EN 14363 as well as normal operation of vehicles, however very extreme cases were excluded. Simulations were undertaken for three vehicle models, two locomotives (PRIMA II and BB 26000) and a freight wagon with Y25 bogies, and the regression behaviour compared with results from the WP 1 tests.

The simulation cases cover a wide range of operating conditions observed during test runs according to EN 14363, as well as normal operation of the vehicles (very extreme cases are excluded): o Use of measured track irregularities taken from WP 1 covering the range of class D geometry specified in prEN 13848-6:2012 [START_REF]-Part 6: Characterisation of track geometry quality[END_REF] for line speeds between < 80 km/h and 300 km/h. The level of irregularities is described by the standard deviation in the longitudinal level (vertical) sLL, and lateral, sAL geometry.  Contact Geometry:


o In curve radii below 600 m: Normal nominal conditions (Rail: UIC 60E1, 1:40, 1435 / 1445 for R ≤ 150, Wheels: S1002, 1425) complemented with real worn profiles measured in WP 1 o In straight track and high cant deficiency curves:

Normal nominal conditions (Rail: UIC 60E1, 1:40, 1435, Wheels: S1002, 1425) complemented with real worn profiles measured in WP 1

Evaluation of results by multiple regression

The diagrams of the results are presented as two lines of figures, the top line shows the measured output (on the Y axis) against the inputs (on the X axis); whilst the bottom line of figures presents the outputs, against each input parameter, adjusted to remove the influence of all other input parameters, referred to as "added variable plots". This second set of figures allows a comparison of the output parameter against the defined input parameter only, and removes 'aliasing' between two input parameters. For example when presenting the effect of speed on Q, results can be distorted because track with a higher line speed also tends to have better track quality; when the influence of track quality is removed a more direct relationship with speed can be demonstrated.

In the bottom line plots, the β value gives the coefficient between the input and output variables and the p-value (shown as % in brackets after the β value) indicates the significance level. A small p-value provides evidence that the sample depends on the input variable whilst a larger value suggests that there is no dependence. All output values are normalised against the limit value for that parameter.

As an example, the simulation results for ∑Ymax (Figure 11) correlate with speed, lateral track alignment standard deviation and wheel-rail equivalent conicity. The solid red lines are the regression lines, whilst the dashed red lines show a statistical confidence interval;

where a second, higher, solid red line is also shown, this is the maximum likely output value. For speed and wheel-rail equivalent conicity there is a wide confidence interval at the extremities of the input values. This is largely a function of the choice of input parameters, with a lot of cases focused on the 160 km/h speed range and conicity cases tending to be focused toward the lower (more commonly experienced) values. Because of this possibility for a less strong correlation, speed and wheel-rail equivalent conicity are deemed to have a weak correlation with ∑Ymax; whilst lateral alignment standard deviation is deemed to have a strong correlation.

Figure 11: Parameters affecting ∑Ymax for the BB26000 locomotive on straight track, plots with: speed; track geometry (lateral); equivalent conicity; axleload; wheel-rail friction

Figure 12 shows a comparison of the regression of outputs for ∑Ymax for the measured results from the BR120 locomotive and the simulated results from the BB26000 and Prima II-2 locomotives.

BR120 loco measured results

In the simulations it was possible to control friction levels and also to change the mass of the vehicle; however, in the measured results outputs are only correlated against speed, lateral track alignment standard deviation and wheel-rail equivalent conicity.

The measured results and both simulations all show a positive increase in ∑Ymax with speed and lateral alignment standard deviation. The measured output for the BR120 and simulated output for the BB26000 are relatively similar for the effect of speed; whilst the simulated BB26000 is more sensitive to lateral track alignment standard deviation. The Prima II-2 locomotive simulated results show less of a correlation in ∑Ymax with speed and track alignment standard deviation, there is a general increase with both but it is more difficult to draw a clear correlation.

Results for the effect of wheel-rail equivalent conicity show a clear difference between the measured results and simulated results. The measured results show ∑Ymax to be reasonably independent of conicity; whilst both sets of simulated results show an increase in ∑Ymax with increased conicity.

For all measured results, the confidence interval is much tighter, this is because of the higher number of samples included.

This work has shown the benefit, and applicability of multiple regression analysis for investigating the behaviour of vehicles on different infrastructure conditions, and has demonstrated that this method can be applied with either test data or results from a validated dynamic model. This will enable the amount of testing needed to demonstrate vehicle compatibility with different infrastructure conditions to be reduced.

Relationships between parameters

The most relevant parameters were identified and summary tables (see below) indicate the relationships between:

 Vehicle assessment parameters and track deterioration effects (Table 1)  Track installation and maintenance conditions and different track damage mechanisms (Table 2)  Vehicle assessment parameters and track / operating conditions (Table 3)

    Q or Qmax       Yqst    ΣYmax   Ymax    Tqst  Bqst   Bmax  
It can be seen that all of the parameters proposed for vehicle assessment have some relationship with one or more deterioration mechanisms of the track. Some of the deterioration mechanisms can be influenced by more than one of the assessment parameters.

   ()  ()  Fatigue of rails    ()    () Fastenings      ()   () Sleepers    ()      Ballast          ()  Track bed        Track geometry - Vertical    ()    ()  Track  ()    () () ()  

geometry -Lateral

From the relationships in Table 2, Table 3 seeks to identify the relationships between the vehicle assessment parameters and the track and operating conditions. These tables allow potential operating controls to be considered that may control the track deterioration if the track is weak, or if certain vehicle performance parameters have high values. In combination with the developed methods for extended evaluation of data from testing and from simulation, additional testing for track loading is no longer necessary in most cases.

Proposed assessment methods for cross acceptance

Parameters for assessment

The following track force assessment parameters are used in the international standards [1] and [START_REF]Testing and approval of railway vehicles from the point of view of their dynamic behaviour -Safety -Track fatigue -Running behaviour[END_REF]) and TSIs ([3], [START_REF] Cr | EU: Commission Decision concerning a technical specification for interoperability relating to the rolling stock subsystem -'Locomotives and passenger rolling stock' of the trans-European conventional rail system[END_REF] and [START_REF]technical specification of interoperability relating to the subsystem 'rolling stockfreight wagons' of the trans-European conventional rail system[END_REF]): Studies in WP4 have shown that all of these parameters are useful for assessing the influence of a vehicle on track forces and the deterioration of track and track components.

Rail inclination and wheel-rail conditions

A number of documents related to Cross Acceptance which were reviewed by WP4 required additional tests because the nominal rail inclination of the track used for the initial approval was different from that where the new approval was requested. For example, a main issue in the checklist for cross acceptance between France and Germany is the question of rail inclination, because France uses a rail inclination of 1:20 whilst Germany uses 1:40. There is agreement and recognition between the two countries of test methods, assessment parameters and limit values, but tests on the alternative inclination are required.

There is a difference in equivalent conicity and radial steering index for rails and wheels in the design condition, depending on the rail inclination and the design wheel profile. However, the influence of the design inclination is strongly modified by the wear of wheels and rails to obtain the in-service conditions.

As the wheel profiles in normal use on the French network are based on the 1:40 tread angle, which is the same as the normal wheel profiles in use on the German network, the worn shape of the rails is strongly influenced by these wheels. The studies in DynoTRAIN WP3 Erreur ! Source du renvoi introuvable. show that for the actual in-service condition there is almost no difference in the characteristics of the two networks (France and Germany) because of the always present wear of the rails related to wear of the wheels.

The influence of the chosen wheel profiles can also be seen by comparison of the results in WP3 from France (where most wheel profiles are based on 1:40) and from GB (where most wheel profiles are based on 1:20) and the resulting worn rail conditions are very different although the design inclination is the same.

Thus additional tests should not be required simply because of the design inclination of the rails. It should be sufficient to demonstrate that a sufficient range of equivalent conicity has been covered.

In Germany tests are required at a higher value of equivalent conicity, in particular for safety (stability). This is required because the maximum values reached in some parts of the German network are particularly high due to the combination of in-service track gauge and rail shape. There should not be any additional requirement for tests for track forces because of these higher conicity values.

Use of simulations and multiple regression analysis

Figure 13 shows an example for an analysis of the quasi-static track loading parameters taking into account the influences of curvature (1/R) and cant deficiency (I). The target test condition (I = 150 mm, Rm = 350 m) is marked by a larger red triangle. In this example the limit for Yqst is slightly above the limit of 60 kN, while the Qqst is clearly below the limit of 145 kN. In that case the combined parameter Bqst reflecting the tension in the rail foot remains below the limit of 180 kN that is given by the two separate values (Bqst,lim = Yqst,lim +0,83 Qqst,lim).

Note: To avoid the inclusion of test results achieved under exceptional friction condition, for this analysis Yqst values were used, that were "corrected" for the friction conditions by using the formula of UIC 518:2009. This formula normalises high Yqst -values caused by Y/Qivalues above 0.40 taking into account 50 % of the assumed effect of the friction.

In this example it can be observed, as in many other cases, that the lateral force Yqst is much less dependent on cant deficiency than the vertical force Qqst.

Figure 13: Example for multiple regression analysis of test results for quasi-static track loading parameters

For the extension of the curvature range to smaller curve radii, the same approach might be applied to simulation results. In that case the simulation results can be adjusted with the test results if necessary. Using the simulation approach, the influence of design parameter change (e.g. axle load) can also be included as an independent parameter in the analysis.

Results analysed and presented in this way can be used to look at trends to define operating rules depending on local infrastructure conditions. This applies to detailed analysis of the compatibility of a certain vehicle design with the infrastructure as well as to a more rough analysis taking into account the trends of several vehicles, which are typical for the traffic on a network or line.

In the use of multiple regression it is important to consider the following factors:

 Selection of data to ensure a good spread of input conditions  Identification of key influencing variables  Consideration of the relevant target conditions  Exclusion of any 'outliers' to avoid very wide confidence intervals For example, regarding the target conditions, these should be related to the practical operating and maintenance rules on the network concerned, so that it is probably not realistic to combine maximum cant deficiency with the worst track quality.

It is recommended that the process used should be documented. This documentation should include:

 The tests, or simulations, used  The input variables selected to be included and the reasons  The range of input conditions covered, including cross-plots  The statistical properties of the regressions obtained More detail is given in [16].

Conclusions of WP4

Studies in WP 4 have shown that all of the parameters used in international standards are useful for assessing the influence of a vehicle on track forces, and the deterioration of track and track components. The influence tables developed allow potential operating controls to be considered that may control the track deterioration if certain of the vehicle performance parameters have high values, or if some limits are exceeded.

For line speeds greater than or equal to 160km/h the current standards for track construction across the member states appear to be similar. On lower speed lines in some countries, a 'weaker' track condition may require a lower limit on one of the vehicle assessment parameters.

The review of national requirements identified modified criteria and limit values for track forces from Austria, Switzerland and Norway. These can be obtained from additional analysis of the normal test results with no new tests required. In GB there are additional requirements for compatibility, but these are assessed using calculations or simulations and do not depend on tests. In Germany tests are required for safety at higher values of equivalent conicity, but it is not clear how this relates to track forces. The influence of design rail inclination has also been found not to be significant, provided a realistic range of wheel-rail contact conditions are included in the tests. In France, the application of the parameter Bqst has been studied to determine relevant operating conditions for new vehicles on weaker parts of the network and this method could be extended to other railways.

The use of multiple regression analysis allows the estimated maximum value for relevant parameters to be evaluated for different target conditions and then compared with the appropriate limit value, or with values for existing, comparable vehicles. The target values for the input variables should be selected to represent the real track conditions where the new acceptance is required. Realistic combinations of the conditions should be selected as, for example, it is probably not realistic to consider the maximum uncompensated lateral acceleration at the same time as the worst track quality. This method can be applied to either test data or results from a validated dynamic model. This will enable the amount of testing needed to demonstrate vehicle compatibility with different infrastructure conditions to be reduced. Some recommendations for the application of multiple regression have been provided.

Guidance has also been provided on the relevant parameters to consider when developing operating controls for different types of track deterioration.

Figure 3 :

 3 Figure 3: Predicted input vertical (top) and lateral (middle) load for model 2 as well as track running conditions (bottom).

Figure 4 :

 4 Figure 4: Flexible Track System Model with input load vectors and measured sleeper position.

Figure 6 :

 6 Figure 6: Relationship between track forces (rail pad and sleeper-ballast) and Qmax

Figure 7 :

 7 Figure 7: Relationship between track forces (rail pad and sleeper-ballast) and Yqst The result of the work only allows assessment of the 'relative' behaviour of different track configurations. It does not quantify absolute forces imposed by a specific vehicle on a specific track, nor does it provide absolute force limits to be imposed on a particular track configuration. The main conclusions from this work are:  The assessment criteria values in the range <20 Hz are a good indicator of dynamic forces on track elements (pad/fastening and sleeper/ballast) in the wider frequency range (including 1st and 2nd mode around 30-50 Hz and 300-500 Hz respectively)  Sleeper-ballast accelerations are thought to have an influence on track settlement and they are not directly related to <20 Hz vehicle forces measured during test.

Figure 8 :

 8 Figure 8: VOCO discrete track structure used to simulate Prud'Homme experiments a. The track layout The Prud'Homme trials took place on a curved 800 m radius curve. The track components consisted of continuous welded rails (CWR) of 46 kg/m (ex-U33), timber sleepers and a freshly tamped ballast bed. The cant deficiency, which is unknown, is assumed to be zero. b. The vehicle and wheel/rail contact models

  Curve Radius R:150 m … ∞ (Right hand curves only)  Cant deficiency I: up to 150 mm  Speed V up to o 200 km/h for BB 26000, o 140 km/h for PRIMA II, o 120 km/h for the freight wagon with Y25 bogies  Friction Coefficient µ: 0,1 ... 0,45 ... 0,6  Geometric track quality:

  13 describes the programme of work which covered: from the point of view of rail loading. It identifies three potential consequences for the rail under the action of excessive loads:

	 Plastic deformation
	 Failure due to brittle fracture
	 Failure due to fatigue damage.
	Plastic deformation was not considered during the study, partly because of the limitations
	of the calculation methods available at that time, and the rail was assumed to be elastic. For
	a range of different applied loads it is indicated for what magnitude of Y and Q forces, and
	at what position on the rail, the stresses reach the 'standard' value of 108 N/m 2 . The
	results, which were valid for a new UIC60 rail profile, sleeper spacing 0.6m, and loads
	applied mid-way between two sleepers, showed that the adopted conditions (up to a wheel
	load of 100kN) were not dangerous for this rail.
	RP6 examined the influence on the permissible Y and Q forces of various parameters:
	 Horizontal and vertical stiffness of the track
	 Sleeper spacing
	 Adjacent axles
	 Rail profile.
	 Stresses in rails
	 Stresses in rail fastening systems
	 Stresses in sleepers
	 Stresses in ballast and formation
	and concentrated on experimental methods. Whilst some of the results are of general
	interest there are no limit values or vehicle test methods proposed.

ORE committee C138 'Permissible maximum values for the Yand Q-forces as well as the ratio Y/Q' produced several reports. Rpt No. 2, Rpt No. 6 and Rpt No. 9 have been reviewed for relevant background information. RP2 deals with the limit values for Y and Q forces

Table 1 : Relationships between track deterioration effects and vehicle assessment parameters

 1

	Track deterioration effect
				Track
	Fatigue / wear of rails	Fastenings Sleepers Ballast	Track bed	geometry Vertical / Cross-	Track geometry Lateral
				level
	Qqst

Table 2

 2 summarises the sensitivities identified between the track installation and maintenance conditions and the different track damage mechanisms. It should be noted that speed and cant deficiency are clearly related in curves. The Table has attempted to indicate which of them is the key factor. The symbol () indicates a less strong sensitivity than .

Table 2 : Sensitivities between track installation and maintenance conditions and track damage mechanisms

 2

	Operating conditions	Track installation			Track geometry	Track maint-enance	Wheel-rail cond-itions
	Speed	Cant deficiency	Axleload	Curve radius	Sleeper type and	spacing	Rail type & fastening	Ballast type & depth	Track support stiffness	Track quality -vertical	Track quality -lateral		Wheel/ Rail profile,	lubrication / friction	modifier
	Surface
	damage of
	rails

Table 3 : Relationships between vehicle assessment parameters and track / operating conditions

 3

		Operating conditions	Track installation				Track geometry
		Speed	Cant deficiency	Curve radius	Sleeper type	and spacing	Rail type	Ballast	Track support	stiffness	Track quality -	vertical / cross-	level	-Track quality	lateral
	Qqst -Quasi-static vertical wheel load		
	Q or Qmax -Maximum vertical wheel force	()								
	Yqst -Quasi-static lateral guiding force		()	
	ΣYmax -Lateral track-shifting force	()										
	Ymax -Maximum guiding force		()		()								
	Tqst -rail surface damage assessment										()		
	Bqst -Quasi-static loading forces						
	Bmax -Maximum loading forces						

Acknowledgement

This paper describes work undertaken in the context of the DynoTRAIN project, Railway Vehicle Dynamics and Track Interactions: Total Regulatory Acceptance for the Interoperable Network (www.triotrain.eu). DynoTRAIN was a collaborative projectmedium-scale focused research project supported by the European 7th Framework Programme, contract number: 234079 and was led by UNIFE.

BB26000 loco simulated results

Prima II-2 loco simulated results

Figure 12: Comparison of ∑Ymax locomotive regressions on straight track, plots with: speed; track geometry (lateral); equivalent conicity; axleload; wheel-rail friction

The locomotive models used for the simulation are not the same as the BR120 locomotive used on the test runs but the vehicles are similar and the results were expected to be comparable.


Jean Luca Bez

Francieli Zanon Boito
email: francieli.boito@posgrad.ufsc.br

Lucas M Schnorr
email: schnorr@inf.ufrgs.br

Philippe O A Navaux
email: navaux@inf.ufrgs.br

Jean-Franc ¸ois Méhaut
email: jean-francois.mehaut@imag.fr

TWINS: Server Access Coordination in the I/O Forwarding Layer

Keywords: High Performance Computing, Parallel File Systems, I/O Forwarding, I/O Scheduling, Access coordination

This paper presents a study of I/O scheduling techniques applied to the I/O forwarding layer. In high-performance computing environments, applications rely on parallel file systems (PFS) to obtain good I/O performance even when handling large amounts of data. To alleviate the concurrency caused by thousands of nodes accessing a significantly smaller number of PFS servers, intermediate I/O nodes are typically applied between processing nodes and the file system. Each intermediate node forwards requests from multiple clients to the system, a setup which gives this component the opportunity to perform optimizations like I/O scheduling.

We evaluate scheduling techniques that improve spatiality and request size of the access patterns. We show they are only partially effective because the access pattern is not the main factor for read performance in the I/O forwarding layer. A new scheduling algorithm, TWINS, is presented to coordinate the access of intermediate I/O nodes to the data servers. Our proposal decreases concurrency at the data servers, a factor previously proven to negatively affect performance. The proposed algorithm is able to improve read performance from shared files by up to 28% over other scheduling algorithms and by up to 50% over not forwarding I/O.

I. INTRODUCTION

Scientific applications -such as climate and seismic simulations -feed the High-Performance Computing (HPC) field with performance requirements to provide understanding of complex phenomena. These performance requirements justify the appearance of ever increasing large scale parallel platforms. For instance, the Aurora supercomputer [START_REF] Facility | Aurora supercomputer[END_REF], expected for the next few years, will have over 50,000 processing nodes to achieve 180 petaflops. Such large scale platforms typically include a shared storage infrastructure over a dedicated set of nodes with a parallel file system (PFS) deployment. If all processing nodes were to concurrently access the shared file system servers, contention would compromise performance.

The I/O forwarding technique aims at reducing the number of clients concurrently accessing the file system servers by placing some special nodes (called I/O nodes) that receive the processing nodes requests and forward them to the file system [START_REF] Dongarra | The international exascale software project roadmap[END_REF]. In this schema, the number of I/O nodes is typically larger than the number of file system servers, and smaller than the number of processing nodes. Furthermore, in this scenario, processing nodes may be powered with only a very simplified local I/O stack to avoid its interference on performance [START_REF] Xu | Hybrid hierarchy storage system in MilkyWay-2 supercomputer[END_REF]. Besides performance improvements, the concept of I/O forwarding has the advantage of creating an additional layer between applications and file system. This layer can work to keep compatibility between both sides and apply optimizations such as request reordering, aggregation and compression.

The I/O scheduling optimization technique has already been successfully applied to the forwarding layer [START_REF] Vishwanath | Accelerating I/O forwarding in IBM blue gene/p systems[END_REF], [START_REF] Ohta | Optimization techniques at the I/O forwarding layer[END_REF]. However, previous scheduling algorithms work to adjust the access patterns generated to the file system. In this work, we evaluate the forwarding layer performance and demonstrate that these techniques are only partially effective because the access pattern is not the main factor that influences the performance of requests through I/O nodes.

We propose a new scheduling algorithm for the I/O nodes which works to decrease contention in the access to the parallel file system data servers. Our algorithm uses time windows and coordinates accesses from intermediate nodes so that at each time window they focus on one of the servers. As far as we know, this is the first work to propose a scheduling technique such as this to the forwarding layer.

Based on an extensive set of experiments, we detect I/O performance improvements with our algorithm over state-ofthe-art algorithms. Moreover, our solution provides gains for 1D strided access patterns which are comparable to the use of collective I/O operations, while being completely transparent to applications and I/O library independent.

The rest of the paper is organized as follows. Section II presents related work, comparing our approach against the state of the art. Section III discusses the I/O forwarding framework used in this work. Then, in Section IV we propose our new scheduling algorithm and discuss its implementation and requirements. The experimental methodology is presented in Section V. Section VI evaluates the performance of our new scheduling algorithm for the forwarding layer compared against existing solutions. Section VII concludes the paper and discuss future directions.

II. RELATED WORK

Large scale HPC platforms are typically used by multiple concurrent applications sharing a parallel file system infrastructure. For this reason, applications may observe performance degradation, a phenomenon known as interference [START_REF] Bhatele | There goes the neighborhood: performance degradation due to nearby jobs[END_REF], [START_REF] Shah | Capturing inter-application interference on clusters[END_REF], [START_REF] Kuo | How file access patterns influence interference among cluster applications[END_REF], [START_REF] Yildiz | On the Root Causes of Cross-Application I/O Interference in HPC Storage Systems[END_REF]. To alleviate its effects, a popular choice is the use of I/O scheduling [START_REF] Zhang | IOrchestrator: Improving the performance of multinode I/O systems via inter-server coordination[END_REF], [START_REF] Song | Server-side I/O coordination for parallel file systems[END_REF], [START_REF] Liu | Hierarchical I/O scheduling for collective I/O[END_REF], [START_REF] Dorier | CALCioM: Mitigating I/O interference in HPC systems through cross-application coordination[END_REF], [START_REF] Dai | Two-Choice Randomized Dynamic I/O Scheduler for Object Storage Systems[END_REF], [START_REF] Boito | Automatic I/O scheduling algorithm selection for parallel file systems[END_REF]. These techniques, applied at some layer of the I/O stack, decide where and when requests must be served.

Song et al. [START_REF] Song | Server-side I/O coordination for parallel file systems[END_REF] proposed a scheduling algorithm for PFS servers. A window-wide coordination concept was employed to make all data servers focus on serving requests from one application at a time. Our proposal to make intermediate I/O nodes dedicate time windows to different data servers was inspired by the work of Song et al. Nonetheless, there are at least three differences between their proposal and ours. First, they target the PFS servers while we change the intermediate I/O nodes behavior. Second, their algorithm coordinates accesses to different applications, while we coordinate server accesses. And third, their algorithm uses the application unique identifier to decide the order of requests inside each time window. Requests from multiple applications may be eventually served in the same window. Differently, we completely dedicate each window to a single server.

Section II-A presents existing techniques to improve the forwarding layer. Section II-B discusses approaches to decrease concurrency at the parallel file system data servers.

A. I/O Forwarding

Considerable research has been focused on improving the I/O forwarding layer performance. Some of them studied the I/O subsystem of an IBM Blue Gene/P supercomputer. In this architecture, the data staging mechanism initially applied multiple threads per I/O node, without any coordination among them. Vishwanath et al. [START_REF] Vishwanath | Accelerating I/O forwarding in IBM blue gene/p systems[END_REF] identified some contention-related bottlenecks associated with this design. They improved performance by allowing asynchronous operations in the I/O nodes and by including a simple FIFO scheduler to coordinate accesses from multiple threads. This scheduler alone provided improvements of up to 38%. They also optimized data movement between layers through a topology-aware approach [START_REF] Vishwanath | Topology-aware data movement and staging for I/O acceleration on Blue Gene/P supercomputing systems[END_REF]. Isaila et al. [START_REF] Isaila | Design and evaluation of multiple-level data staging for blue gene systems[END_REF] proposed a two-level prefetching scheme for this architecture.

Similarly to what was done by Vishwanath et al., Ohta et al. [START_REF] Ohta | Optimization techniques at the I/O forwarding layer[END_REF] improved performance of the IOFSL framework by using I/O scheduling. They implemented two algorithms: a simple FIFO and a quantum-based algorithm called Handle-Based Round-Robin (HBRR). The latter is based on an algorithm successfully applied to parallel file systems data servers [START_REF] Boito | Automatic I/O scheduling algorithm selection for parallel file systems[END_REF], [START_REF] Lebre | I/O scheduling service for multi-application clusters[END_REF], [START_REF] Qian | A novel network request scheduler for a large scale storage system[END_REF], that aims at reordering and aggregating requests to improve the generated access pattern.

In this paper, we compare our new scheduling algorithm against FIFO and HBRR. The former provides the simplest coordination, while the latter is a more complex solution to improve the access pattern. They compose our baseline because they represent the state of the art in I/O scheduling for the forwarding layer. Nevertheless, as it is shown in Section VI-A, improving the access pattern is only partially effective to improve performance in this layer.

B. Concurrency at the parallel file system's data servers

Chen et al. [START_REF] Chen | LACIO: A new collective I/O strategy for parallel I/O systems[END_REF] proposed a new collective I/O approach which uses the physical data layout information to make each aggregator access as few data servers as possible. A similar approach was presented by Wang et al. [START_REF] Wang | Iteration based collective I/O strategy for Parallel I/O systems[END_REF]. Their technique breaks collective I/O calls into multiple iterations to fit the buffer size. These partitions are optimized so each server is accessed by only one aggregator at each iteration. These initiatives obtain performance by decreasing the number of clients concurrently accessing each server. This approach potentially decreases network contention. Additionally, Yildiz et al. [START_REF] Yildiz | On the Root Causes of Cross-Application I/O Interference in HPC Storage Systems[END_REF] have shown that concurrency at the data servers is one of the key factors for interference.

For these reasons, we propose an I/O scheduling algorithm that seeks to focus an I/O node's accesses to a single data server within each time window. As far as we know, ours is the first work to apply this strategy to the forwarding layer. This design choice has the advantage of making it completely transparent to applications, I/O libraries, and file systems.

III. THE I/O FORWARDING SCALABILITY LAYER

In this paper, we study I/O scheduling techniques at the I/O forwarding layer. Therefore an I/O forwarding framework was necessary to implement and validate our ideas. We have chosen to use the open source IOFSL framework [START_REF] Ali | Scalable I/O forwarding framework for high-performance computing systems[END_REF] so we could build on previous contributions and effectively compare our new solution with the state of the art.

IOFSL uses the stateless ZOIDFS protocol, the API from the ZOID forwarding infrastructure [START_REF] Iskra | ZOID: I/O forwarding infrastructure for petascale architectures[END_REF], and the Buffered Message Interface (BMI) network abstraction layer [START_REF] Carns | BMI: a network abstraction layer for parallel I/O[END_REF] to provide request forwarding over multiple file systems and networks. The IOFSL software stack consists of two main components: a ZOIDFS client library running on the compute ... Requests come from the processing nodes (left) and leave for the file system (bottom right).

nodes and an I/O forwarding daemon running on the intermediate I/O nodes. The client library transparently forwards requests from the compute node to the corresponding I/O node.

In the I/O nodes, multiple threads are created to process the clients' requests. The request scheduler component coordinates these threads' accesses. It offers two options of scheduling algorithms -FIFO and HBRR -to fill a dispatch queue and thus decide the order requests must be processed. As discussed in Section II-A, FIFO is a simple time order algorithm, and HBRR is a handle-based round robin. HBRR employs multiple queues, one per handle, where contiguous requests are aggregated whenever possible. From each queue, a maximum number of requests (defined by the quantum parameter) may be served before moving to the next queue [START_REF] Ohta | Optimization techniques at the I/O forwarding layer[END_REF].

The flow of requests through the IOFSL I/O node daemon is illustrated by Fig. 1. After going through one of the scheduling algorithms, requests are stored in the dispatch queue. From the dispatch queue, requests to the same file and of the same type (read or write) are aggregated before being forwarded to the PFS. Although all clients data and metadata operations go through the IOFSL nodes, only data reads and writes go through the request scheduler component and are affected by scheduling algorithms. Section VI-A will discuss the performance obtained by IOFSL with these algorithms.

IV. TWINS: DECREASING CONCURRENCY AT THE PFS DATA SERVERS

The previous section described the scheduling infrastructure for the I/O forwarding layer, composed of two scheduling algorithms -FIFO or HBRR -and an aggregator at the dispatch queue. This model focuses on changing the access pattern to improve performance. However, as it is shown in Section VI-A, these schedulers are only partially effective because they do not take into consideration resource contention and data placement on the parallel file system servers. In this section, we present a new scheduling algorithm for the I/O forwarding layer called Server Time WINdows (TWINS). The main idea behind TWINS is coordinating intermediate I/O nodes' accesses to the file system so that, at any given moment: I. an I/O node is focusing its accesses to only one of the parallel file system data servers; II. the different I/O nodes are focusing on different servers.

TWINS pseudo-code is presented in Algorithm 1. It keeps multiple request queues, one per data server. During the execution, TWINS iterates the different queues following a round robin scheme, respecting a time window that must be dedicated to each server. If server i is the current server being accessed but there are no requests to this server, the scheduler will wait until requests to server i arrive or the time window ends, even if there are queued requests to other servers.

The rest of this section discusses TWINS characteristics and implementation. Section VI-B presents performance improvements provided by our new algorithm.

Algorithm 1 Server Time WINdows

Require: Q[i] is the updated list of requests to server i 1: i ← 0 2: while true do 3:

resetT imer()

4:
while elapsedT ime() < windowSize do

5: if length(Q[i]) > 0 then 6: processRequest(Q[i]) 7: else 8:
timeout ← windowSize -elapsedT ime()

9:
timedW aitF orRequests(Q[i], timeout) i ← nextServer(i) 13: end while

A. Implementation with AGIOS

We integrated the AGIOS scheduling library [START_REF] Boito | Automatic I/O scheduling algorithm selection for parallel file systems[END_REF] in IOFSL as a scheduling option (just like FIFO and HBRR). AGIOS can be used by I/O services to manage incoming requests at file level and provides a simple API to the development of new scheduling algorithms. We implemented TWINS through this simple API. It would have been possible to implement the algorithm inside the IOFSL source code. However, doing so with AGIOS makes our solution more generic, as it can be used by other I/O services, or by other I/O forwarding frameworks.

The new organization of the I/O node daemon is illustrated in Fig. 2. Using TWINS, requests are added to the per-server queues upon arrival at IOFSL. When the algorithm decides to process a request, a callback function written inside IOFSL simply adds it to the dispatch queue. This ensures requests will be processed in the order dictated by the scheduler.

Differently from FIFO, that uses a single queue, and HBRR, that uses two queues per file handle, TWINS applies one queue per data server. Considering the number of files is typically far superior to the number of servers, the overhead induced by TWINS regarding the management of multiple queues is expected to be lower than what is caused by HBRR.

B. Finding out to which server a request is

In addition to typically available information about requests -file handle, offset, type, and size -our algorithm requires a server identifier. We have modified IOFSL to collect the file distribution information from the PFS when opening or creating a file. Since this information is easily available to clients in most file systems such as PVFS2 [START_REF] Carns | PVFS: A parallel file system for linux clusters[END_REF] and Lustre [START_REF] Microsystems | LUSTRE file system -high-performance storage architecture and scalable cluster file system[END_REF], our solution can still be considered file system generic.

The distribution information is requested only once per file. The induced overhead is of 54.3ms on our experimental environment (average of 124 observations). This time can be expected to be diluted by longer read and write times.

Using the file distribution information, the starting server for a request is obtained as a function of its starting offset and stripe size -also part of the collected information. This is done when requests arrive, before adding them to the queues.

Considering the described TWINS algorithm, we can notice that simply following this approach would cause all intermediate I/O nodes to focus on the same servers at the same time. To cause the desired distribution effect, we add an extra server identifier translation step. This translation is done according to the I/O node identifier. The N th I/O node will use the N th permutation of the servers list as a translation rule. Therefore, if the number of intermediate nodes is larger than the number of servers, more than one node may access the same server at the same time, but these concurrent accesses are minimized.

For instance, if there are two I/O nodes (N 0 and N 1 ) and two data servers (S 0 and S 1 ), N 0 will go through the servers in the order S 0 , S 1 , while N 1 will use the order S 1 , S 0 . Therefore the translation function in N 0 maps S 0 to 0 and S 1 to 1, while in N 1 it will map S 1 to 0 and S 0 to 1. This ensures each I/O node will focus on a different server at each time window.

V. EXPERIMENTAL METHODOLOGY

All experiments in this paper were conducted in clusters from the Grid'5000 [START_REF] Bolze | Grid5000: A large scale and highly reconfigurable experimental grid testbed[END_REF] at Nancy. Four nodes from the Grimoire cluster were used as PVFS2 servers (acting as both data and metadata servers) and 32 nodes from the Grisou cluster were used as clients. Four nodes from Grisou were used as IOFSL servers. Hence, each I/O node runs on a separate machine, which is not shared with clients or PFS servers.

Each node from Grimoire and Grisou has two 8-core Intel Xeon E5-2630 v3 and 128GB of RAM. A 558GB hard disk is used for storage at the servers. Nodes are interconnected through a 10Gbps Ethernet network, and there is a 10Gbps link between the clusters. Both clusters were completely reserved during the experiments to minimize network interference.

PVFS version 2.8.2 was used with its default parameters, including 64KB stripe size and striping through all four servers. Data servers were configured to perform I/O operations directly to their storage devices, bypassing buffer caches. This was done to avoid a situation where the tests scale would hide the access pattern impact on performance.

The IOFSL dispatcher uses the PVFS2 client library to communicate directly with the file system, allowing direct access to the file system instead of accessing it through the PVFS2 kernel module. The use of IOFSL is transparent to applications, as accesses are forwarded through the ZOID API. An environment variable is set at the processing nodes to determine to where requests must be redirected. Clients are equally distributed among the I/O nodes.

The IOFSL daemon was executed with all its default parameters. The maximum number of requests that can be aggregated from the dispatch queue (batch size) was 16. Minimum and maximum numbers of threads are four and 16, respectively. For the event handler, IOFSL state machines were used.

The MPI-IO Test benchmark was executed by 128 processes to generate requests through the MPI-IO library. Each process generates 1024 read or write requests of 32KB, i.e. 32MB of data is accessed per process, a total of 4GB per test. Tests were executed for the file-per-process approach, where each process contiguously accesses its own independent file, and for the shared-file one. With a shared file, processes either access their own contiguous portions inside the shared file, or follow a 1D strided access pattern. These experiments represent access patterns that are usual among scientific applications.

From each benchmark execution, we take the completion time of the slowest process. We use the makespan as a performance metric because it represents the total time to process a workload from the file system point of view.

Experiments were repeated at least 8 times, and error bars were calculated using a 99.7% confidence interval. Different experiments were executed in a random order to avoid bias imposed by some uncontrolled parameter, or some unexpected effect caused by a specific experimentation order.

VI. PERFORMANCE EVALUATION

This section presents our performance evaluation. First we discuss the performance obtained using the IOFSL framework with the baseline scheduling algorithms. Then, in Section VI-B we describe results with our new TWINS algorithm.

A. Performance of the baseline scheduling algorithms

IOFSL represents a synchronous I/O forwarding approach, i.e., the intermediate I/O nodes are an extra hop between processing nodes and the file system. In this approach, I/O nodes do not act as burst buffers but instead, the clients' expectation of persistent storage in the file system is met.

Figures 3 and4 present results obtained by read and write tests, respectively, with the three tested access patterns. In all graphs, the first column (in yellow) shows time obtained without IOFSL, and the second and third columns (in red) show time obtained using IOFSL with its base scheduling algorithms. From these, the first bar represents the FIFO scheduling algorithm, while the second bar represents HBRR.

We can see that read performance is improved up to 36% by using IOFSL, despite the extra transmission cost between clients and the file system. Performance benefits from using the I/O forwarding layer to all tested read access patterns. On the other hand, for write requests, performance is decreased for all situations. This decrease is higher for the file-per-process scenario than for the shared file ones.

One could believe the explanation for the good results observed for read tests is that the gains obtained by aggregating requests before forwarding them to the file system compensates the overhead imposed by the extra hop. Nonetheless, this is not the case. Table I presents the average request size observed at different layers of the I/O stack during the experiments with the IOFSL base schedulers and shared-file approach. In the file-per-process scenario, each file is accessed by a single process one request at a time, so there are no aggregation opportunities. Single median values are presented because values were similar between FIFO and HBRR tests. We can see that the write tests present similar aggregated request sizes (by IOFSL), but still do not achieve the same gains as read tests. Therefore, despite requests aggregations usually being helpful for performance, they are not the main factor in the observed read performance improvements. Another evidence in this direction is that the best gains were observed for the file-per-process scenario, where there are no aggregations. Furthermore, despite making more effort into generating a better access pattern, the HBRR algorithm does not outperform FIFO. Results for the two algorithms were not significantly different in any of the situations.

Table II presents the average offset distance of requests leaving the I/O nodes during the read shared-file tests. The offset distance is a spatiality metric calculated by taking the offset difference between every two consecutive requests. The higher the distance, the less contiguous an access pattern is. The contiguous local access pattern presents the highest average offset distances, i.e., it is actually the least contiguous global access pattern. This happens because each process accesses contiguously its own portion of the shared file, but different processes are accessing requests that are sparse in the file. During the 1D strided test, requests from different processes are contiguous to each other. The average offset distances during tests with FIFO and HBRR are very similar. Hence, we can notice both algorithms result in very similar access patterns. This explains why they perform similarly. of the 1D strided shared-file test through a single I/O node. In the intermediate I/O node, we have traced all requests' arrival time. We have considered only the first 128, i.e. the first request from each of the 128 processes. Since tests are synchronous, all requests after the first 128 depend on the time it took to process the previous ones, so they are not independent. We have used the median because it is less sensitive to outliers, and timing values inside each test tend to have high variability. For read requests the median time difference is 26.09µs, whereas for write requests it is 50.92µs.

Since read requests are smaller than writes (they do not carry data), they arrive at a faster pace to the I/O nodes (or to the server if I/O nodes are not present). Hence, the extra hop between clients and PFS works to "funnel" requests and decrease concurrency at the servers. For this reason, FIFO and HBRR, which work to improve the generated access pattern, are only partially effective in improving read performance. A more effective strategy would be to work to further decrease contention in the access to the file system. The next section presents results obtained with our new scheduling algorithm, proposed to explore this idea.

B. Performance of the TWINS algorithm

In this section, we evaluate the performance of the new TWINS algorithm, which works to decrease contention in the access to the parallel file system data servers. As described in Section IV, in order to do that, TWINS divides execution in time windows and focuses each IOFSL node's accesses to a different data server during each time window.

The third group of bars from all graphs in Figures 3 and4 (in blue) present results obtained with our algorithm for different time window sizes. TWINS provides read performance improvements of up to 28% over the baseline scheduling algorithms and of up to 50% over not using IOFSL. The best results were obtained for the shared-file 1D strided access pattern, and gains for the shared-file contiguous pattern were also observed -up to 20% over the baseline.

The lower improvements obtained for contiguous access patterns are justified by the requests distribution among the data servers caused by the access patterns. In the 1D strided test, processes start their accesses at different servers and this behavior is kept throughout the execution. Therefore, with this access pattern the scheduler always has requests for all servers and thus has the opportunity to perform meaningful coordination. In the contiguous test, since each process segment has 32MB, which is a multiple of the stripe size × the number of servers, all processes start their accesses by the same server. In this case, the opportunities to accesses coordination -i.e., the situations where there are queued requests for multiple servers -appear during the execution as the delays induced by TWINS causes some processes execution to advance faster than others. This phenomenon is not guaranteed to happen.

Performance does not benefit from using TWINS in read tests with the file-per-process scenario and in write tests. Even for these cases, performance is not decreased significantly compared to the baseline algorithms as long as a small window size is used. Therefore, in addition to improving performance of some situations, our proposal does not necessarily harm performance in situations where it is not able to provide improvements.

1) Impact of the time window parameter: TWINS' behavior is affected by its time window duration. A window that is too small does not allow for an effective coordination of accesses among the data servers because it is not long enough to allow the execution of multiple requests. Moreover, a fast time window does not hold requests to other servers for long enough so requests to the currently accessed data server are out of the dispatch queue to the file system. If requests for different servers are in the dispatch queue at the same time, they could be aggregated before being forwarded to the PFS and thus the scheduling algorithm work would be undone. On the other hand, a window that is too large imposes overhead as there are not enough requests to each data server to fill a whole window, so the scheduler spends too much time waiting. Another source of overhead, in this case, would be the delay imposed to requests, which could not be compensated by the gains of decreasing concurrency at the data servers.

The best window duration is not the same for all situations where TWINS improves performance. The best results for contiguous shared-file read tests were obtained with a onesecond time window, while the best results for 1D strided shared-file read tests were for a eight-seconds window. There is a trade-off to be observed between the induced overhead and how distributed among the servers requests are. Further analysis is required to determine how the scheduler could automatically find the best window duration. This will be the focus of future work.

2) Multi-application scenario: To confirm our algorithm performance on a multi-application scenario, we have conducted additional experiments using the Ifer benchmark [START_REF] Yildiz | IFER: MicroBenchmark for Studying the Cross-Application I/O Interference[END_REF]. This benchmark splits the set of processes into groups running on two different sets of nodes. Each group of processes executes a series of MPI-IO operations, simulating two applications accessing the shared file system in contention. We have modified Ifer to perform read operations to previously created files. For these experiments, each application has 64 processes and presents the shared-file 1D strided access pattern.

Fig. 5 presents execution times of the first application (A) in the multi-application experiments. The lines represent different options -not using the forwarding layer, using it with the baseline algorithms and using it with TWINS. The x-axis represents the time difference between start time for applications A and B: when dt is 0 both start at the same time, positive dt means A starts before B, and negative dt means B starts first.

We can see the I/O forwarding layer also improves read performance for the multi-application scenario -up to 35% with the baseline algorithms. The interference experienced by the application is decreased by FIFO and HBRR up to 25%, except when applications start with a 10-seconds difference.

The interference factor is calculated as the ratio between the execution time of the application under contention and the time of the application executing by itself. TWINS improves performance up to 16% over FIFO and HBRR, and up to 45% over not using IOFSL. Interference is further decreased by using TWINS -up to 12% over the baseline algorithms and up to 31% over not using I/O nodes.

3) Comparison with collective I/O: The traditional way of improving performance of 1D strided access patterns with small requests is to use collective I/O operations. Fig. 6 compares the performance obtained by TWINS for this access pattern with what is achieved by making the single application perform collective calls. As a reference, times obtained using IOFSL with the baseline algorithms are also presented. We can see TWINS is able to provide as much performance improvement as the use of collective operations for 1D strided read access patterns with small requests. It is important to notice that TWINS represents a more transparent and generic solution than MPI-IO collective operations. Because it is applied in the I/O nodes, TWINS is completely transparent to applications and I/O library generic. Therefore applications using any method to perform I/O operations, such as POSIX, can benefit from this optimization. To the best of our knowledge, this is the first work to propose a scheduler to the I/O forwarding layer which transparently coordinates accesses to alleviate concurrency at the PFS data servers.

VII. CONCLUSIONS AND FUTURE WORK

In this paper, we studied the I/O scheduling technique at the I/O forwarding layer by evaluating algorithms previously applied to this layer -FIFO and HBRR. This evaluation has shown that, despite improving read performance, techniques that work to adjust the access pattern (requests aggregation and reordering) are only partially effective.

We have proposed a new scheduling algorithm for the I/O forwarding layer called TWINS. Our algorithm uses time windows to coordinate the I/O nodes' accesses to different data servers, working to decrease contention in the access to the servers. To the best of our knowledge, ours is the first work to apply such a technique in the I/O forwarding layer.

TWINS results have shown performance improvements for shared-file read access patterns of up to 28% over the FIFO and HBRR algorithms. Compared to not using I/O forwarding nodes, the gains were of up to 50%. Improvements were also shown for a multi-application scenario, accompanied with a decrease in interference. Even for situations where TWINS is not able to improve performance, it does not harm it. Performance obtained by TWINS for the 1D strided read access pattern was comparable to what can be achieved by making the application use collective operations. We have compared our results with collective I/O because this would be a popular alternative applied to improve performance of the applications with 1D strided access patterns. Nevertheless, compared to collective I/O, our proposal is completely transparent to applications and library independent.

Future work will focus on proposing an automatic mechanism to tune the TWINS' time window duration parameter based on the observed access pattern. Moreover, we will use TWINS to provide performance improvements to real scientific applications.

Fig. 1 :

 1 Fig.1: Flow of requests through the IOFSL I/O node daemon. Requests come from the processing nodes (left) and leave for the file system (bottom right).

Fig. 2 :

 2 Fig. 2: Flow of requests through the IOFSL I/O node daemon with the new TWINS scheduling option.

Fig. 3 :

 3 Fig. 3: Execution time of read tests without IOFSL, with IOFSL using FIFO or HBRR, and using the TWINS algorithm with different time window sizes (ms).

Fig. 4 :

 4 Fig. 4: Execution time of write tests without IOFSL, with IOFSL using FIFO or HBRR, and using the TWINS algorithm with different time window sizes (ms).

Fig. 5 :

 5 Fig. 5: Execution time for an application under contention caused by another concurrent application.

Fig. 6 :

 6 Fig. 6: TWINS vs. collective I/O operations

TABLE I :

 I Average request size at different I/O stack levels.

		Contiguous	1D strided
		READ WRITE READ WRITE
	Leaving clients	32KB	32KB 32KB	32KB
	Leaving I/O nodes 58KB	58KB 58KB	58KB
	Arriving at servers 43KB	44KB 50KB	49KB

TABLE II :

 II Average offset distance observed during read tests.

	Contiguous	1D strided
	FIFO	HBRR	FIFO	HBRR
	1341.00MB 1340.95MB 45.22MB 44.74MB
	We have measured the time difference between consecutive
	requests. These values were obtained from four new executions

ACKNOWLEDGMENT

Experiments presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https: //www.grid5000.fr/). This research was accomplished in the context of the International Joint Laboratory LICIA. Research has received funding from the EU H2020 Programme and from MCTI/RNP-Brazil under the HPC4E Project, grant agreement n • 689772. This work was also supported by STIC-AmSud/CAPES scientific cooperation program under EnergySFE research project grant 99999.007556/2015-02. The authors would also like to thank Kamil Iskra, Rob Latham, and Rob Ross from the Argonne National Laboratory for insights about the IOFSL framework.