Pitfalls in Parallel Job Scheduling Evaluation

Frachtenberg, Eitan; Feitelson, Dror G.

doi:10.1007/11605300_13

Cited by 61 publications

(41 citation statements)

References 83 publications

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“…Choosing an appropriate performance metric is a hard decision to make [2]. This is because different scheduling scenarios can have different ways quality of service can be envisaged.…”

Section: Discussionmentioning

confidence: 99%

“…Other performance metrics include Average Waiting Time (AWT), Job Slow-down (JSD) and throughput. An appropriate metric depends on the scheduling environment [2]. In a real time scenario for example, AWT is not an appropriate metric.…”

Section: Performancementioning

confidence: 99%

“…The choice of an effective scheduler is hard to make. One of the reasons could be that the (performance) metric used is inappropriate [2]. The appropriateness of the metric depends on the scheduling set up.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characteristics of Fairness Metrics and Their Effect on Perceived Scheduler Effectiveness

Ngubiri

Vliet

2010

International Journal of Computers and Applications

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Parallel job schedulers are mostly evaluated using performance metrics. Deductions however can be misleading due to selective job starvation (unfairness). To choose a better scheduler, therefore, there is a need to compare schedulers for fairness as well. Performance and fairness, however, have mostly been studied independently. We examine characteristics of three approaches to fairness evaluation in parallel job scheduling. We examine how they represent job starvation and other aspects of discrimination. We show that the implied unfairness is not always starvation/discrimination in practice. We use simultaneous consideration of performance and fairness and compare deductions with scheduler effectiveness derived from group-wise performance evaluation. We observe that due to possible misrepresentation of starvation by fairness metrics, schedulers shown as superior may not be so in practice.

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“…Choosing an appropriate performance metric is a hard decision to make [2]. This is because different scheduling scenarios can have different ways quality of service can be envisaged.…”

Section: Discussionmentioning

confidence: 99%

Section: Performancementioning

confidence: 99%

See 1 more Smart Citation

Characteristics of Fairness Metrics and Their Effect on Perceived Scheduler Effectiveness

Ngubiri

Vliet

2010

International Journal of Computers and Applications

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“…Maximizing utilization, in job scheduling, however, can be at odds with responsiveness and fairness [9,11,25]. In particular, Web schedulers have the choice of collocating jobs from different SLA tiers on the same compute nodes, or of allocating dedicated nodes for each desired tier.…”

Section: Compute Node Sharingmentioning

confidence: 99%

Web-Scale Job Scheduling

Cirne

Frachtenberg

2013

Job Scheduling Strategies for Parallel Processing

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Abstract. Web datacenters and clusters can be larger than the world's largest supercomputers, and run workloads that are at least as heterogeneous and complex as their high-performance computing counterparts. And yet little is known about the unique job scheduling challenges of these environments. This article aims to ameliorate this situation. It discusses the challenges of running web infrastructure and describes several techniques to address them. It also presents some of the problems that remain open in the field.

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“…The choice of the measurement metric for parallel job scheduling evaluation has to be taken with a lot of care [14]. Some metrics may have different implications depending on the circumstance.…”

Section: Related Workmentioning

confidence: 99%

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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Pitfalls in Parallel Job Scheduling Evaluation

Cited by 61 publications

References 83 publications

Characteristics of Fairness Metrics and Their Effect on Perceived Scheduler Effectiveness

Characteristics of Fairness Metrics and Their Effect on Perceived Scheduler Effectiveness

Web-Scale Job Scheduling

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

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