LIP

In this paper, we consider the makespan optimisation when scheduling a batch of identical workflows on a heterogeneous platform as a service-oriented grid or a micro-factory. A job is represented by a directed acyclic graph (DAG) with typed tasks and no fork nodes (in-tree precedence constraints). The processing resources are able to process a set of task types, each with

In this paper, we consider the makespan optimisation when scheduling a batch of identical workflows on a heterogeneous platform as a service-oriented grid or a micro-factory. A job is represented by a directed acyclic graph (DAG) with typed tasks and no fork nodes (in-tree precedence constraints). The processing resources are able to process a set of task types, each with

In this paper we study the problem of batch scheduling within a homogeneous cluster. In this context, the problem is that the more processors the job requires the more difficult it is to find an idle slot to run it on. As a consequence the resources are often inefficiently used as some of them remain unallocated in the final schedule. To address this issue we propose a technique called job folding that uses virtualization to reduce the number of processors allocated to a parallel job and thus allows to execute it earlier. Our goal is to optimize the resource use. In this paper we propose several heuristics based on job folding and we compare their performance with classical on-line scheduling algorithms as FCFS or backfilling. The contributions of the paper are both on the design of the job folding algorithms and on their performance analysis.

With the increasing numbers of Cloud Service Providers and the migration of the Grids to the Cloud paradigm, it is necessary to be able to leverage these new resources. Moreover, a large class of High Performance Computing (HPC) applications can run these resources without (or with minor) modifications. But using these resources come with the cost of being able to interact with these new resource providers. In this paper we introduce the design of a HPC middleware that is able to use resources coming from an environment that compose of multiple Clouds as well as classical \\hpc resources. Using the \\diet middleware, we are able to deploy a large-scale, distributed HPC platform that spans across a large pool of resources aggregated from different providers. Furthermore, we hide to the end users the difficulty and complexity of selecting and using these new resources even when new Cloud Service Providers are added to the pool. Finally, we validate the architecture concept through cosmo...

In 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. Key-words: Batch scheduling, grid computing, heterogeneous platform, genetic algorithm Laboratoire d’Informatique de l’Universite de Franche-Comte, UFR Sciences et Techniques, 16, route de Gray, 25030 Besancon Cedex (France) Telephone : +33 (0)3 81 66 64 55 — Telecopie : +33 (0)3 81 66 64 50 Algorithme Genetique ...

With the increasing numbers of Cloud Service Providers and the migration of the Grids to the Cloud paradigm, it is necessary to be able to leverage these new resources. Moreover, a large class of High Performance Computing (hpc) applications can run these resources without (or with minor) modifications. But using these resources come with the cost of being able to interact with these new resource providers. In this paper we introduce the design of a hpc middleware that is able to use resources coming from an environment that compose of multiple Clouds as well as classical hpc resources. Using the Diet middleware, we are able to deploy a large-scale, distributed hpc platform that spans across a large pool of resources aggregated from different providers. Furthermore, we hide to the end users the difficulty and complexity of selecting and using these new resources even when new Cloud Service Providers are added to the pool. Finally, we validate the architecture concept through cosmolo...

ABSTRACT In this paper we study the problem of scheduling a collection of workflows, identical or not, on a SOA (Service Oriented Architecture) 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 typed tasks with unrelated processing costs and are able to transmit files through communication links for which the communication times are not negligible. The goal of our study 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 its performance analysis.