Scheduling of Parallel Jobs in a Heterogeneous Multi-site Environment

Sabin, Gerald; Kettimuthu, Rajkumar; Rajan, Arun Kumar Sundar; Sadayappan, P.

doi:10.1007/10968987_5

Cited by 73 publications

(42 citation statements)

References 28 publications

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“…-Min-Completion-Time (MCT). In contrast to MLB, the earliest possible completion time is determined based on a partial schedule of already assigned jobs [9,7]. For instance, Moab [3] can estimate the completion time of all jobs in the local queue because jobs and reservations possess a start time and a wallclock limit.…”

Section: Two Level Hierarchy Schedulingmentioning

confidence: 99%

Two Level Job-Scheduling Strategies for a Computational Grid

Tchernykh

Ramírez

Avetisyan

et al. 2006

Parallel Processing and Applied Mathematics

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Abstract. We address parallel jobs scheduling problem for computational GRID systems. We concentrate on two-level hierarchy scheduling: at the first level broker allocates computational jobs to parallel computers. At the second level each computer generates schedules of the parallel jobs assigned to it by its own local scheduler. Selection, allocation strategies, and efficiency of proposed hierarchical scheduling algorithms are discussed.

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Section: Two Level Hierarchy Schedulingmentioning

confidence: 99%

Two Level Job-Scheduling Strategies for a Computational Grid

Tchernykh

Ramírez

Avetisyan

et al. 2006

Parallel Processing and Applied Mathematics

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“…The K-Distributed and K-Dual Queue Models [15,21] propose a distributed scheduling algorithm which redundantly distributes jobs to different sites simultaneously, instead of only sending jobs to the most lightly loaded sites. The K-Distributed model enables each metascheduler of each site to distribute their jobs to the K least loaded sites, whereby such jobs will be scheduled by all K sites respectively.…”

Section: Related Workmentioning

confidence: 99%

Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm

Huang

Bessis

Norrington

et al. 2013

Future Generation Computer Systems

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“…There has been a large body [1,2,3,4,5] of research focused on the performance of job schedulers for parallel machines, much of it being reported at the annual Workshop on Job Scheduling Strategies for Parallel Processing [6]. These studies have generally focused on user metrics (such as turnaround time and slowdown) and system metrics (such as utilization and loss of capacity).…”

Section: Introductionmentioning

confidence: 99%

Unfairness Metrics for Space-Sharing Parallel Job Schedulers

Sabin

Sadayappan

2005

Job Scheduling Strategies for Parallel Processing

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Sociology, computer networking and operations research provide evidence of the importance of fairness in queuing disciplines. Currently, there is no accepted model for characterizing fairness in parallel job scheduling. We introduce two fairness metrics intended for parallel job schedulers, both of which are based on models from sociology, networking, and operations research. The first metric is motivated by social justice and attempts to measure deviation from arrival order, which is perceived as fair by the end user. The second metric is based on resource equality and compares the resources consumed by a job with the resources deserved by the job. Both of these metrics are orthogonal to traditional metrics, such as turnaround time and utilization.The proposed fairness metrics are used to measure the unfairness for some typical scheduling policies via simulation studies. We analyze the fairness of these scheduling policies using both metrics, identifying similarities and differences.

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Scheduling of Parallel Jobs in a Heterogeneous Multi-site Environment

Abstract: Abstract

Cited by 73 publications

References 28 publications

Two Level Job-Scheduling Strategies for a Computational Grid

Two Level Job-Scheduling Strategies for a Computational Grid

Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm

Unfairness Metrics for Space-Sharing Parallel Job Schedulers

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