The performance of processor co-allocation in multicluster systems

Bucur, Anca; Epema, Dick

doi:10.1109/ccgrid.2003.1199381

Cited by 44 publications

(30 citation statements)

References 9 publications

(11 reference statements)

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“…The jobs are online and have exponentially distributed inter-arrival and execution times. These distributions have also been used in previous related work [4] [15]. A job's execution time is unknown before it finishes execution.…”

Section: Job Streammentioning

confidence: 99%

“…The details of the jobs in the widest component based groups are dependent on the partitioning approach. When using the random approach (at thres = 11), W 1 and W 2 consist of jobs whose widest component is 1 and 2 while W 3 and W 4 consists of jobs whose width of the widest component is in the ranges [3,4] and [5,19]. W 1 , W 2 , W 3 and W 4 jobs constitute 24%, 26%, 38% and 12% of the job stream respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…S 1 consists of jobs with size = 1. S 2 , S 3 and S 4 groups are made up of jobs in the size range [2,3], [4,7] and [8,38] respectively. S 1 , S 2 , S 3 and S 4 constitute of 24%, 26%, 26% and 24% of the job stream respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…This leads to a lower effective utilization of the multi-cluster system. Studies [4] have shown that despite the drawbacks of the slower wide area network, co-allocation is a viable option.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, research work has been extended to computational and data grids [13][27] as well as multi-cluster systems [4] [15]. A multi-cluster system is set up by connecting multiple clusters (possibly in different locations) into a bigger computational infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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Group-Wise Performance Evaluation of Processor Co-allocation in Multi-cluster Systems

Ngubiri

Vliet

2008

Job Scheduling Strategies for Parallel Processing

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Abstract. Performance evaluation in multi-cluster processor co-allocation -like in many other parallel job scheduling problems-is mostly done by computing the average metric value for the entire job stream. This does not give a comprehensive understanding of the relative performance of the different jobs grouped by their characteristics. It is however the characteristics that affect how easy/hard jobs are to schedule. We, therefore, do not get to understand scheduler performance at job type level. In this paper, we study the performance of multi-cluster processor co-allocation for different job groups grouped by their size, components and widest component. We study their relative performance, sensitivity to parameters and how their performance is affected by the heuristics used to break them up into components. We show that the widest component us characteristic that most affects job schedulability. We also show that to get better performance, jobs should be broken up in such a way that the width of the widest component is minimized.

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Section: Job Streammentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Group-Wise Performance Evaluation of Processor Co-allocation in Multi-cluster Systems

Ngubiri

Vliet

2008

Job Scheduling Strategies for Parallel Processing

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KOALA: a co‐allocating grid scheduler

Mohamed

Epema

2007

Concurrency and Computation

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SUMMARYIn multicluster systems, and more generally in grids, jobs may require co-allocation, that is, the simultaneous or coordinated access of single applications to resources of possibly multiple types in multiple locations managed by different resource managers. Co-allocation presents new challenges to resource management in grids, such as locating sufficient resources in geographically distributed sites, allocating and managing resources in multiple, possibly heterogeneous sites for single applications, and coordinating the execution of single jobs at multiple sites. Moreover, as single jobs now may have to rely on multiple resource managers, co-allocation introduces reliability problems. In this paper, we present the design and implementation of a co-allocating grid scheduler named KOALA that meets these co-allocation challenges. In addition, we report on the results of an analysis of the performance in our multicluster testbed of the co-allocation policies built into KOALA. We also include the results of a performance and reliability test of KOALA while our testbed was unstable.

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Network‐aware selective job checkpoint and migration to enhance co‐allocation in multi‐cluster systems

Jones

2009

Concurrency and Computation

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SUMMARYMulti-site parallel job schedulers can improve average job turn-around time by making use of fragmented node resources available throughout the grid. By mapping jobs across potentially many clusters, jobs that would otherwise wait in the queue for local resources can begin execution much earlier; thereby improving system utilization and reducing average queue waiting time. Recent research in this area of scheduling leverages user-provided estimates of job communication characteristics to more effectively partition the job across system resources. In this paper, we address the impact of inaccuracies in these estimates on system performance and show that multi-site scheduling techniques benefit from these estimates, even in the presence of considerable inaccuracy. While these results are encouraging, there are instances where these errors result in poor job scheduling decisions that cause network over-subscription. This situation can lead to significantly degraded application performance and turnaround time. Consequently, we explore the use of job checkpointing, termination, migration, and restart (CTMR) to selectively stop offending jobs to alleviate network congestion and subsequently restart them when (and where) sufficient network resources are available. We then characterize the conditions and the extent to which the process of CTMR improves overall performance. We demonstrate that this technique is beneficial even when the overhead of doing so is costly.

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The performance of processor co-allocation in multicluster systems

Abstract: Abstract

Cited by 44 publications

References 9 publications

Group-Wise Performance Evaluation of Processor Co-allocation in Multi-cluster Systems

Group-Wise Performance Evaluation of Processor Co-allocation in Multi-cluster Systems

KOALA: a co‐allocating grid scheduler

Network‐aware selective job checkpoint and migration to enhance co‐allocation in multi‐cluster systems

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