Algorithmes d'ordonnancement de graphes de tâches parallèles sur plates-formes hétérogènes en deux étapes

Perpi'2006 - Conférences conjointes RenPar'17 / SympA'2006 / CFSE'5 / JC'2006National audienceL'ordonnancement d'applications parallèles représentées par des graphes de tâches consiste à trouver l'ensemble de processeurs sur lesquels chaque tâche doit être exécutée afin de minimiser le temps d'exécution de ces applications tout en exploitant rationnellement les ressources. Alors que la plupart des algorithmes d'ordonnancement de graphes de tâches parallèles visent des grappes homogènes, cet article montre la nécessité d'avoir de tels algorithmes pour des agrégations de grappes de calcul qui sont de plus en plus répandues. Ainsi, nous proposons d'adapter une heuristique d'ordonnancement de tâches parallèles en milieu homogène au cas d'une plate-forme hétérogène

Adaptation d'un algorithme d'ordonnancement de tâches parallèles sur plates-formes homogènes aux systèmes hétérogènes

Rapport de stage d'initiation à la rechercheCe stage d'initiation à la recherche nous a permis de concevoir et d'évaluer des algorithmes d'ordonnancement de tâches parallèles en milieu hétérogène en partant d'un algorithme adapté aux plates-formes homogènes. Après avoir réalisé de nombreuses simulations sur les deux algorithmes que nous avons mis en place, nous avons noté que cette adaptation aux systèmes hétérogènes permettait de tirer profit des agglomérations hétérogènes de grappes homogènes de ressources de calculs lors de l'exécution des grosses applications composées de tâches parallèles. La comparaison des performances de nos deux algorithmes avec celles d'un autre algorithme nous a également permis de conclure que notre approche permettait d'obtenir un meilleur compromis entre le temps de completion des applications et la puissance de calcul utilisée

Heuristiques d'ordonnancement en deux étapes de graphes de tâches parallèles

Article publié à Techniques et Sciences Informatiques Volume 28. n° 1/2009National audienceL'ordonnancement d'applications parallèles représentées par des graphes de tâches consiste à trouver l'ensemble de processeurs sur lesquels chaque tâche doit être exécutée afin de minimiser le temps d'exécution de ces applications tout en exploitant rationnellement les ressources. Alors que la plupart des algorithmes d'ordonnancement de graphes de tâches parallèles visent des grappes homogènes, cet article montre la nécessité d'avoir de tels algorithmes pour des agrégations de grappes de calcul qui sont de plus en plus répandues et qui peuvent permettre de déployer des applications parallèles à échelles sans précédents. Nous proposons des améliorations d'une heuristique d'ordonnancement de tâches parallèles en milieu homogène. Ensuite, nous l'adaptons au cas des plates-formes hétérogènes de type grappe hétérogène de grappes homogènes. While most parallel task graph scheduling research has been done in the context of single homogeneous clusters, heterogeneous platforms have become prevalent and are extremely attractive for deploying applications at unprecedented scales. In this paper we address the need for scheduling techniques for parallel task applications for heterogeneous clusters of clusters by proposing a method to adapt existing parallel task graph scheduling heuristics that have proved to be efficient on homogeneous environments. Before adapting that heuristic to heterogeneous platforms, we propose some improvements for homogeneous platform

Critical Path and Area Based Scheduling of Parallel Task Graphs on Heterogeneous Platforms

International audienceWhile most parallel task graphs scheduling research has been done in the context of single homogeneous clusters, heterogeneous platforms have become prevalent and are extremely attractive for deploying applications at un- precedented scales. In this paper we address the need for scheduling techniques for parallel task applications for heterogeneous clusters of clusters by proposing a method to adapt existing parallel task graphs scheduling heuristics that have proved to be efficient on homogeneous environments. The contributions of this paper are: (i) a novel "virtual" cluster methodology for handling platform heterogeneity; (ii) a novel task placement step, designed to determine whether the placement step of heuristics for homogeneous platforms is adapted to the heterogeneous case; (iii) an empirical evaluation in a wide range of platform and application scenarios. This study shows that the proposed heuristics achieve better performance than the original when platform are heterogeneous and we discuss a number of trends apparent in our results

Self-Constrained Resource Allocation Procedures for Parallel Task Graph Scheduling on Shared Computing Grids

International audienceTwo of the main characteristics of computation grids are their heterogeneity and the sharing of resources between different users. This is the cost of the tremendous computing power offered by such platforms. Scheduling several applications concurrently in such an environment is thus challenging. In this paper we propose a first step towards the scheduling of multiple parallel task graphs~(PTG), a class of applications that can benefit of large and powerful platforms, by focusing on the allocation process. We consider the application of a resource constraint on the schedule and determine the number of processors allocated to the different tasks of a PTG while respecting that constraint. We present two different allocation procedures and validate them in simulation over a wide range of scenarios with regard to their respect of the resource constraint and their impact on the completion time of the scheduled applications. We find that our procedures provide a guarantee on the resource usage for a low cost in terms of execution time

A Comparison of Scheduling Approaches for Mixed-Parallel Applications on Heterogeneous Platforms

International audienceMixed-parallel applications can take advantage of large-scale computing platforms but scheduling them efficiently on such platforms is challenging. In this paper we compare the two main proposed approaches for solving this scheduling problem on a heterogeneous set of homogeneous clusters. We first modify previously proposed algorithms for both approaches and show that our modifications lead to significant improvements. We then perform a comparison of the modified algorithms in simulation over a wide range of application and platform conditions. We find that although both approaches have advantages, one of them is most likely he most appropriate for the majority of users

Concurrent Scheduling of Parallel Task Graphs on Multi-Clusters Using Constrained Resource Allocations

Scheduling multiple applications on heterogeneous multi-clusters is challenging as the different applications have to compete to access the resources. A scheduler thus has to ensure a fair distribution of the resources among the applications and prevent harmful selfish behaviors while still trying to minimize their respective completion time. In this study we consider mixed-parallel applications, represented by graphs whose nodes are data-parallel tasks, that are scheduled in two steps: allocation and mapping. We investigate several strategies to constrain the amount of resources the scheduler can allocate to each submitted application. This study is then evaluated over a wide range of scenarios

A Comparison of Scheduling Approaches for Mixed-Parallel Applications on Heterogeneous Platforms

International audienceMixed-parallel applications can take advantage of large-scale computing platforms but scheduling them efficiently on such platforms is challenging. In this paper we compare the two main proposed approaches for solving this scheduling problem on a heterogeneous set of homogeneous clusters. We first modify previously proposed algorithms for both approaches and show that our modifications lead to significant improvements. We then perform a comparison of the modified algorithms in simulation over a wide range of application and platform conditions. We find that although both approaches have advantages, one of them is most likely he most appropriate for the majority of users

Optimization of the latency in networks SDN

Unlike traditional networks, software-defined networking (SDN) are characterized by a physical separation of the control and the transfer plan. Thus, a centralized controller communicates the functions of control plan to each device via the OpenFlow Protocol whenever he is asked or that it deems appropriate. This impact strongly the latency time which is important for new services or multimedia applications. In order to optimize the time of transmission in network data with SDN, the proactive approach based on the algorithm back - pressure is usually oered. However, we note that the proactive approach while reducing strongly this time, does not account settings such as the failure of a node part of the way to transfer or the breaking of a bond that greatly increases the latency time. In this document, we will propose a joint routing approach based on proactive and reactive routing. This in order to optimize the routing functions by simply placing the traffic where capacity allows, in order to avoid congestion of highly stressed parts of the network taking into account the failures and significantly reduce the time latency. Simulation results show that our proposal allows a considerable reduction of latency even when there are failures in the network

Scheduling Parallel Tasks on Shared Heterogeneous Platforms

Aujourd'hui, les plates-formes hétérogènes et partagées que sont les grilles de calcul sont omniprésentes. De plus, le besoin d'exécuter des applications parallèles complexes est croissant. Cette thèse vise à ordonnancer des applications représentées par des graphes de tâches modelables (dont le nombre de processeurs est fixé par l'ordonnanceur) sur des grilles de calcul en exploitant le maximum de parallélisme, utilisant efficacement les ressources, gérant l'hétérogénéité et le partage des ressources. Nous avons pour cela opté pour des heuristiques pragmatiques car, bien qu'elles n'offrent pas de garantie de performance, elles peuvent néanmoins conduire à de bonnes performances moyennes tout en construisant des ordonnancements en des temps relativement courts. La plupart des heuristiques existantes n'ordonnancent les applications parallèles mixtes qu'en milieu homogène et utilisent parfois inefficacement les ressources. Nous avons donc tout d'abord étudié différentes heuristiques dans le cas de plates-formes homogènes et proposé des améliorations visant à améliorer le compromis entre réduction du temps de complétion et efficacité. Nous avons ensuite introduit la gestion de l'hétérogénéité dans l'heuristique proposée et comparé ses performances à celles d'un algorithme garanti. Enfin, nous avons tenu compte du caractère partagé des grilles en gérant la concurrence entre applications. L'approche retenue consiste à limiter la quantité de ressources que chaque application peut utiliser pour construire son ordonnancement. Nous avons également proposé plusieurs stratégies de détermination de cette contrainte de ressources.Today, computing grids, that are shared and heterogeneous platforms, are ubiquitous. Furthermore, the need to execute complex parallel applications is growing. The aim of this thesis is to schedule applications modeled by moldable task graphs (the number of processors allocated to each task is fixed by the scheduler) onto computing grids to exploit maximum parallelism, use efficiently the resources and manage the sharing of resources. We chose to design pragmatic heuristics because, although they do not guarantee performance, they can lead to good average performance while computing schedules in relatively short times. Almost all existing heuristics only schedule mixed parallel applications onto homogeneous platforms and they sometimes use inefficiently the resources. Thus, we first studied different scheduling heuristics in the case of homogeneous platforms and propose improvements to have a good compromise between the completion time and the efficiency. We then introduced management of heterogeneity in the proposed heuristic and compared its performances with those of a guaranteed algorithm. Finally, we have taken into account the fact that grids are shared by managing competition between applications. Our approach is to limit the amount of resources that each application can use to build its schedule. We also proposed different strategies to determine that resource constraint