Optimization and Job Scheduling in Heterogeneous Networks

The final publication is available at www.springerlink.comA heterogeneous network is a connected network of different platforms and operating systems. Job scheduling is a problem of selecting a free resource for unexecuted task from a pool of submitted tasks. Furthermore, it is required to find for every resource the best order of the tasks assigned to it. The purpose of this paper is to develop an efficient algorithm for job scheduling in heterogeneous networks. The algorithm should include parameters such as properties of resources and properties of jobs. The algorithm includes a cost function that is required to be optimized which includes parameters such as the total processing time, average waiting time. Our results demonstrate that the proposed algoritghm can be efficiently used to determine the performance of different job scheduling algorithms under different sets of loads.http://link.springer.com/chapter/10.1007%2F978-90-481-3662-9_4

A game-theoretic and hybrid genetic meta-heuristics model for security-assured scheduling of independent jobs in computational grids

Scheduling independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. Much of current research in this domain is focused on the improvement of the efficiency of the Grid schedulers, both at global and local levels, which is the basis for Grid systems to leverage large computing capacities. However, unlike traditional scheduling, in Grid systems security requirements are very important to scheduling tasks/applications to Grid resources. The objective is thus to achieve efficient and secure allocation of tasks to machines. In this paper we propose a new model for secure scheduling at the Grid sites by combining game-theoretic and genetic-based meta-heuristic approaches. The game-theoretic model takes into account the realistic feature that Grid users usually perform independently of each other. The scheduling problem is then formalized as a noncooperative non-zero sum game with Nash equilibria as the solutions. The game cost function is minimized, at global and user levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model through a static benchmark of instances, for which we have measured two basic metrics, namely the makespan and flowtime. The obtained results suggest that it is more resilient for the Grid users (and local schedulers) to tolerate some job delays defined as additional scheduling cost due to security requirements instead of taking a risk of allocating at unreliable resources.Peer ReviewedPostprint (published version

Design and evaluation of a tabu search method for job scheduling in distributed enviorments

The efficient allocation of jobs to grid resources is indispensable for high performance grid-based applications. The scheduling problem is computationally hard even when there are no dependencies among jobs. Thus, we present in this paper a new tabu search (TS) algorithm for the problem of batch job scheduling on computational grids. We consider the job scheduling as a bi-objective optimization problem consisting of the minimization of the makespan and flowtime. The bi-objectivity is tackled through a hierarchic approach in which makespan is considered a primary objective and flowtime a secondary one. An extensive experimental study has been first conducted in order to fine-tune the parameters of our TS algorithm. Then, our tuned TS is compared versus two well known TS algorithms in the literature (one of them is hybridized with an ant colony optimization algorithm) for the problem. The computational results show that our TS implementation clearly outperforms the compared algorithms. Finally, we evaluated the performance of our TS algorithm on a new set of instances that better fits with the concept of computational grid. These instances are composed of a higher number of -heterogeneous- machines (up to 256) and emulate the dynamic behavior of these systems.Peer ReviewedPostprint (published version

Static Scheduling Strategies for Heterogeneous Systems

In this paper, we consider static scheduling techniques for heterogeneous systems, such as clusters and grids. We successively deal with minimum makespan scheduling, divisible load scheduling and steady-state scheduling. Finally, we discuss the limitations of static scheduling approaches

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