Parallel Application Scheduling on Networks of Workstations

Anastasiadis, Stergios V.; Sevcik, Kenneth C.

doi:10.1006/jpdc.1997.1335

Cited by 36 publications

(22 citation statements)

References 27 publications

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“…2 cannot start processing the ®rst tile of the ith row of f 2 before 1 has computed the last tile of the ith row of f 1 and has sent that data to 2 , that is, at time-step s 1 i 1 t 1 com . Since 2 starts processing the ®rst row of f 2 at time s 2 , where s 2 P s 1 1 t 1 com , it is not delayed by 1 .…”

Section: Heuristic Allocation By Block Of Columnsmentioning

confidence: 99%

“…Distributing the computations (together with the associated data) can be performed either dynamically or statically, or a mixture of both. At ®rst sight, we may think that dynamic strategies like a greedy algorithm are likely to perform better, because the machine loads will be self-regulated, hence self-balanced, if processors pick up new tasks just as they terminate their current computation (see the survey paper of Berman [5] and the more specialized references [2,12] for further details). However, data dependences may lead to slow the whole process down to the pace of the slowest processor, as we demonstrate in Section 4.…”

Section: Introductionmentioning

confidence: 99%

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Static tiling for heterogeneous computing platforms

Boulet¹,

Dongarra²,

Robert³

et al. 1999

Parallel Computing

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In the framework of fully permutable loops, tiling has been extensively studied as a sourceto-source program transformation. However, little work has been devoted to the mapping and scheduling of the tiles on physical processors. Moreover, targeting heterogeneous computing platforms has to the best of our knowledge, never been considered. In this paper we extend static tiling techniques to the context of limited computational resources with dierent-speed processors. In particular, we present ecient scheduling and mapping strategies that are asymptotically optimal. The practical usefulness of these strategies is fully demonstrated by MPI experiments on a heterogeneous network of workstations. Ó

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Section: Heuristic Allocation By Block Of Columnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Static tiling for heterogeneous computing platforms

Boulet¹,

Dongarra²,

Robert³

et al. 1999

Parallel Computing

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show abstract

“…With adaptive partitioning processors in the system are not divided before the computation. When a new job arrives, a job manager in the system first locates idle processors and then allocates certain number of those idle processors to that job according to some processor allocation policies, e.g., those described in [2,10,14,15,17,18,20]. Therefore, the boundary lines are drawn during the computation and will disappear after the job is terminated.…”

Section: Introductionmentioning

confidence: 99%

Job scheduling strategies for networks of workstations

Zhou

Brent

Walsh

et al. 1998

Job Scheduling Strategies for Parallel Processing

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Abstract. In this paper we first introduce the concepts of utilisation ratio and effective speedup and their relations to the system performance. We then describe a two-level scheduling scheme which can be used to achieve good performance for parallel jobs and good response for interactive sequential jobs and also to balance both parallel and sequential workloads. The two-level scheduling can be implemented by introducing on each processor a registration office. We also introduce a loose gang scheduling scheme. This scheme is scalable and has many advantages over existing explicit and implicit coscheduling schemes for scheduling parallel jobs under a time sharing environment.

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“…Some representative examples of generic schedulers include [37], [38]. The techniques proposed in [39], [40] consider the class of malleable jobs where the number processors provisioned can be varied at runtime. Similarly, the scheduling techniques presented in [41], [42] consider moldable jobs that can be run on different number of processors.…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Resource Provisioning for MapReduce in a Cloud

Palanisamy

Singh

Liu

2015

IEEE Trans. Parallel Distrib. Syst.

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Abstract-This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services such as a generic compute cloud or a dedicated MapReduce cloud, Cura has a number of unique benefits. Firstly, Cura is designed to provide a cost-effective solution to efficiently handle MapReduce production workloads that have a significant amount of interactive jobs. Secondly, unlike existing services that require customers to decide the resources to be used for the jobs, Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs. While the existing models allow only a per-job resource optimization for the jobs, Cura implements a globally efficient resource allocation scheme that significantly reduces the resource usage cost in the cloud. Thirdly, Cura leverages unique optimization opportunities when dealing with workloads that can withstand some slack. By effectively multiplexing the available cloud resources among the jobs based on the job requirements, Cura achieves significantly lower resource usage costs for the jobs. Cura's core resource management schemes include cost-aware resource provisioning, VM-aware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that our techniques lead to more than 80% reduction in the cloud compute infrastructure cost with upto 65% reduction in job response times.

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Parallel Application Scheduling on Networks of Workstations

Cited by 36 publications

References 27 publications

Static tiling for heterogeneous computing platforms

Static tiling for heterogeneous computing platforms

Job scheduling strategies for networks of workstations

Cost-Effective Resource Provisioning for MapReduce in a Cloud

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