Scheduling and Dynamic Management of Applications over Grids

The work presented in this Thesis is about scheduling applications in computational Grids. We study how to better manage jobs in a grid middleware in order to improve the performance of the platform. Our solutions are designed to work at the middleware layer, thus allowing to keep the underlying architecture unmodified. First, we propose a reallocation mechanism to dynamically tackle errors that occur during the scheduling. Indeed, it is often necessary to provide a runtime estimation when submitting on a parallel computer so that it can compute a schedule. However, estimations are inherently inaccurate and scheduling decisions are based on incorrect data, and are therefore wrong. The reallocation mechanism we propose tackles this problem by moving waiting jobs between several parallel machines in order to reduce the scheduling errors due to inaccurate runtime estimates. Our second interest in the Thesis is the study of the scheduling of a climatology application on the Grid. To provide the best possible performances, we modeled the application as a Directed Acyclic Graph (DAG) and then proposed specific scheduling heuristics. To execute the application on the Grid, the middleware uses the knowledge of the application to find thebest schedule.Les travaux présentés dans cette thèse portent sur l'ordonnancement d'applications au sein d'un environnement de grille de calcul. Nous étudions comment mieux gérer les tâches au sein des intergiciels de grille, ceci dans l'objectif d'améliorer les performances globales de la plateforme. Les solutions que nous proposons se situent dans l'intergiciel, ce qui permet de conserver les architectures sous-jacentes sans les modifier. Dans un premier temps, nous proposons un mécanisme de réallocation permettant de prendre en compte dynamiquement les erreurs d'ordonnancement commises lors de la soumission de calculs. En effet, lors de la soumission sur une machine parallèle, il est souvent nécessaire de fournir une estimation du temps d'exécution afin que celle-ci puisse effectuer un ordonnancement. Cependant, les estimations ne sont pas précises et les décisions d'ordonnancement sont sans cesse remises en question. Le mécanisme de réallocation proposé permet de prendre en compte ces changements en déplaçant des calculs d'une machine parallèle à une autre. Le second point auquel nous nous intéressons dans cette thèse est l'ordonnancement d'une application de climatologie sur la grille. Afin de fournir les meilleures performances possibles nous avons modélisé l'application puis proposé des heuristiques spécifiques. Pour exécuter l'application sur une grille de calcul, l'intergiciel utilise ces connaissances sur l'application pour fournir le meilleur ordonnancement possible

DIET : new developments and recent results

Among existing grid middleware approaches, one simple, powerful, and flexibleapproach consists of using servers available in different administrative domainsthrough the classic client-server or Remote Procedure Call (RPC) paradigm.Network Enabled Servers (NES) implement this model also called GridRPC.Clients submit computation requests to a scheduler whose goal is to find aserver available on the grid. The aim of this paper is to give an overview of anNES middleware developed in the GRAAL team called DIET and to describerecent developments. DIET (Distributed Interactive Engineering Toolbox) is ahierarchical set of components used for the development of applications basedon computational servers on the grid.Parmi les intergiciels de grilles existants, une approche simple, flexible et performante consiste a utiliser des serveurs disponibles dans des domaines administratifs différents à travers le paradigme classique de l’appel de procédure àdistance (RPC). Les environnements de ce type, connus sous le terme de Network Enabled Servers, implémentent ce modèle appelé GridRPC. Des clientssoumettent des requêtes de calcul à un ordonnanceur dont le but consiste àtrouver un serveur disponible sur la grille.Le but de cet article est de donner un tour d’horizon d’un intergiciel développédans le projet GRAAL appelé DIET 1. DIET (Distributed Interactive Engineering Toolbox) est un ensemble hiérarchique de composants utilisés pour ledéveloppement d’applications basées sur des serveurs de calcul sur la grille

Study of the behaviour of heuristics relying on the Historical Trace Manager in a (multi)client-agent-server system

We compare some dynamic scheduling heuristics that have shown good performances on simulation study against MCT on experiments on real solving platforms. The heuristics rely on a prediction module, the Historical Trace Manager. They have been implemented in NetSolve, a Problem Solver Environment built on the client-agent-server model. Numerous different scenarios have been examined and many metrics have been considered. We show that the predicting module allows a better precision in task duration estimation and that our heuristics optimize several metrics at the same time while outperforming MCT

A Genetic Algorithm to Schedule Workflow Collections on a SOA-Grid with Communication Costs

International audienceIn this paper we study the problem of scheduling a collection of workflows, identical or not, on a SOA grid. A workflow (job) is represented by a directed acyclic graph (DAG) with typed tasks. All of the grid hosts are able to process a set of task types with unrelated processing costs and are able to transmit files through communication links for which the communication times are not negligible. The goal is to minimize the maximum completion time (makespan) of the workflows. To solve this problem we propose a genetic approach. The contributions of this paper are both the design of a Genetic Algorithm taking the communication costs into account and the performance analysis

A Bi-Criteria Algorithm for Scheduling Parallel Task Graphs on Clusters

International audienceApplications structured as parallel task graphs exhibit both data and task parallelism, and arise in many domains. Scheduling these applications on parallel platforms has been a long-standing challenge. In the case of a single homogeneous cluster, most of the existing algorithms focus on the reduction of the application completion time (makespan). But in presence of resource managers such as batch schedulers and due to accentuated pressure on energy concerns, the produced schedules also have to be efficient in terms of resource usage. In this paper we propose a novel bi-criteria algorithm, called biCPA, able to optimize these two performance metrics either simultaneously or separately. Using simulation over a wide range of experimental scenarios, we find that biCPA leads to better results than previously published algorithms

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Improvements and Study of the Accuracy of the Tasks Duration Predictor, New Heuristics

The Historical Trace Manager is a task duration predictor module embedded in the agent of a Problem Solving Environment relying on the client-agent-server. The HTM is introduced in and . In this paper, we explain some improvements built into the HTM and NetSolve, the Problem Soving Environment we use for our tests, in order to synchronize the HTM to the reality.We also introduce two new scheduling heuritics relying on the HTM information: Advanced HMCT and Minimum Length.We study the scheduling of several scenarios, including the simultaneous submissions of DAGS and independent tasks, on a real heterogeneous platform.The excellent behavior of the HTM validates its estimations of the duration of each task concurrently running in the system. It can consequently predict the contention tasks may have on each other if scheduled and executed concurrently on the same computing resource.Heuristics performances show the relevancy of the HTM information through the experiments: their ability of behaving with a constant quality between two executions of the same experiment as well as the quality of their respective scheduling choices to optimize several criteria at the same time. We also show that heuristics which rely on minimizing the contention give generally the best results regardless the criterion.We finally compare the behavior of the heuristics previously tested in to the one observed here with more precise information on the global system state due to the synchronization mechanisms. Surprisingly, in the time-shared model, it does not necessarily improve the job repartition among the servers, performances can consequently decrease and the utilization of the fastest servers can become critical

On the construction of decentralised service-oriented orchestration systems

Modern science relies on workflow technology to capture, process, and analyse data obtained from scientific instruments. Scientific workflows are precise descriptions of experiments in which multiple computational tasks are coordinated based on the dataflows between them. Orchestrating scientific workflows presents a significant research challenge: they are typically executed in a manner such that all data pass through a centralised computer server known as the engine, which causes unnecessary network traffic that leads to a performance bottleneck. These workflows are commonly composed of services that perform computation over geographically distributed resources, and involve the management of dataflows between them. Centralised orchestration is clearly not a scalable approach for coordinating services dispersed across distant geographical locations. This thesis presents a scalable decentralised service-oriented orchestration system that relies on a high-level data coordination language for the specification and execution of workflows. This system’s architecture consists of distributed engines, each of which is responsible for executing part of the overall workflow. It exploits parallelism in the workflow by decomposing it into smaller sub-workflows, and determines the most appropriate engines to execute them using computation placement analysis. This permits the workflow logic to be distributed closer to the services providing the data for execution, which reduces the overall data transfer in the workflow and improves its execution time. This thesis provides an evaluation of the presented system which concludes that decentralised orchestration provides scalability benefits over centralised orchestration, and improves the overall performance of executing a service-oriented workflow