Model-Driven Simulation of Grid Scheduling Strategies

Li, Hui; Buyya, Rajkumar

doi:10.1109/e-science.2007.51

Cited by 24 publications

(21 citation statements)

References 21 publications

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“…These delayed jobs could then utilize resources that otherwise would have been left idle, as happens with EASY. This case study thus joins other work [3,9] in demonstrating that conservative workload modeling -in which workload features of a-priori unknown importance are included rather than being ignored -may lead to more reliable evaluations and have a significant effect on the outcome.…”

Section: Introductionsupporting

confidence: 57%

A case for conservative workload modeling: Parallel job scheduling with daily cycles of activity

Feitelson

Shmueli

2009

2009 IEEE International Symposium on Modeling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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Computer workloads have many attributes. When modeling these workloads it is often difficult to decide which attributes are important, and which can be abstracted away. In many cases, the modeler only includes attributes that are believed to be important, and ignores the rest. We argue, however, that this can lead to impaired workloads and unreliable system evaluations. Using parallel job scheduling as a case study, and daily cycles of activity as the attribute in dispute, we present two schedulers whose simulated performance seems identical without cycles, but then becomes significantly different when daily cycles are included in the workload. We trace this to the ability of one scheduler to prioritize interactive jobs, which leads to implicitly delaying less critical work to nighttime, when it can utilize resources that otherwise would have been left idle. Notably, this was not a design feature of this scheduler, but rather an emergent property that was not anticipated in advance.

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Section: Introductionsupporting

confidence: 57%

A case for conservative workload modeling: Parallel job scheduling with daily cycles of activity

Feitelson

Shmueli

2009

2009 IEEE International Symposium on Modeling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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“…Importantly, not every workload feature has the same effect: in some cases it is one specific workload feature that is Version 1.0.3, typeset on the most important. The problem is that it is not always obvious in advance which feature is the most important, and even if it seems obvious, we might be wrong [237,438,248]. This motivates the practice of conservative workload modeling, where an attempt is made to correctly model all known workload features, regardless of their perceived importance [248].…”

Section: Sensitivity To Workloadsmentioning

confidence: 99%

“…On the flip side, it seems that repetitive workloads result in performance degradations [438], and also cause performance evaluations to be more sensitive to small variations in conditions [695]. Thus workloads with significant locality of sampling may be harder to handle, and require more work to get reliable results.…”

Section: Importancementioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

197

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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“…In the literature, parallel applications whose tasks are independent are sometimes referred to as Bag-of-Tasks applications (BoT) (as in Anglano et al [4]) or parameter-sweep applications (as in Casanova et al [7]). Such applications are becoming predominant for clusters and grids (see Iosup et al [18] and Li and Buyya [23]). …”

Section: Workload Modelmentioning

confidence: 99%

Power-aware linear programming based scheduling for heterogeneous computer clusters

Al-Daoud

Al-Azzoni

Down

2012

Future Generation Computer Systems

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In the past few years, scheduling for computer clusters has become a hot topic. The main focus has been towards achieving better performance. It is true that this goal has been attained to a certain extent, but on the other hand, it has been at the expense of increased energy consumption and consequent economic and environmental costs. As these clusters are becoming more popular and complex, reducing energy consumption in such systems has become a necessity. Several power-aware scheduling policies have been proposed for homogeneous clusters. In this work, we propose a new policy for heterogeneous clusters. Our simulation experiments show that using our proposed policy results in significant reduction in energy consumption while performing very competitively in heterogeneous clusters.

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Model-Driven Simulation of Grid Scheduling Strategies

Abstract: Abstract

Cited by 24 publications

References 21 publications

A case for conservative workload modeling: Parallel job scheduling with daily cycles of activity

A case for conservative workload modeling: Parallel job scheduling with daily cycles of activity

Workload Modeling for Computer Systems Performance Evaluation

Power-aware linear programming based scheduling for heterogeneous computer clusters

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