Predictability of process resource usage: a measurement-based study on UNIX

Devarakonda, Murthy V.; Iyer, Ravishankar K.

doi:10.1109/32.58769

Cited by 80 publications

(48 citation statements)

References 9 publications

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“…All the previous program attributes -runtime, branching behavior, memory behavior, and phases -may be predicted to some degree [175]. The same goes for other types of program behavior considered later, such as I/O behavior [288].…”

Section: Predictabilitymentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

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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Section: Predictabilitymentioning

confidence: 99%

“…LaTeX, BibTeX, LaTeX, LaTeX). Such sequences can be modeled using Markov chains in which the different states represent different applications -in effect creating a user behavior graph [258,175,104].…”

Section: Predictabilitymentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

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“…The Shortest Job First (SJF) algorithm sorts the waiting jobs by increasing estimated runtime and executes the jobs with the shortest runtime first. A job's runtime can be estimated through repeated executions of the job [6] or through compile-time analysis [20,3]. The opposite algorithm, Longest Job First, executes the jobs with the longest processing time first.…”

Section: Scheduling With Backfillingmentioning

confidence: 99%

Backfilling with lookahead to optimize the packing of parallel jobs

Shmueli

Feitelson

2005

Journal of Parallel and Distributed Computing

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The utilization of parallel computers depends on how jobs are packed together: if the jobs are not packed tightly, resources are lost due to fragmentation. The problem is that the goal of high utilization may conflict with goals of fairness or even progress for all jobs. The common solution is to use backfilling, which combines a reservation for the first job in the interest of progress with packing of later jobs to fill in holes and increase utilization. However, backfilling considers the queued jobs one at a time, and thus might miss better packing opportunities. We propose the use of dynamic programming to find the best packing possible given the current composition of the queue, thus maximizing the utilization on every scheduling step. Simulations of this algorithm, called LOS (Lookahead Optimizing Scheduler), using trace files from several IBM SP parallel systems, show that LOS indeed improves utilization, and thereby reduces the mean response time and mean slowdown of all jobs. Moreover, it is actually possible to limit the lookahead depth to about 50 jobs and still achieve essentially the same results. Finally, we experimented with selecting among alternative sets of jobs that achieve the same utilization. Surprising results indicate that choosing the set at the head of the queue does not necessarily guarantee best performance. Instead, repeatedly selecting the set with the maximal overall expected slowdown boosts performance when compared to all other alternatives checked.

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“…A large section of this work focuses on platform-specific approaches [1,2,3,4,5], which does not support resource usage prediction on previously "unseen" target execution environments and very few of these approaches address performance prediction across different hardware configurations. This is important since the set of available resources on the Grid could have an arbitrary configuration.…”

Section: Introductionmentioning

confidence: 99%