Modeling User Runtime Estimates

Tsafrir, Dan; Etsion, Yoav; Feitelson, Dror G.

doi:10.1007/11605300_1

Cited by 92 publications

(60 citation statements)

References 23 publications

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“…However, this model still retained information regarding the relative length of each job, and therefore enabled schedulers to achieve better results than they would in reality [696]. Thus, Tsafrir et al developed an intricate model to mimic the real relationship between job runtimes and user estimates [693]. A central feature of this model is the discreteness of estimates: users tend to use round values in their estimates, such as 10 minutes or 1 hour, and doing so causes a loss of information.…”

Section: Job Runtimesmentioning

confidence: 99%

“…Another problem is its randomization, and in particular, the fact that there is no correlation between runtime and accuracy. Such a correlation actually does exist, for two reasons [693]. First, long jobs generally enjoy much more accurate estimates.…”

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confidence: 99%

“…Based on these observations, a better model has been proposed to generate synthetic runtime estimates that mimic those chosen by real users [693]. This is somewhat involved, but the five main components are as follows:…”

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confidence: 99%

“…The original study was also tainted by using "statistically bad" estimates that, as noted earlier, still contain significant information. A better model for bad estimates is that more of them use popular modal values and thus lose information [693].…”

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confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

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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: Job Runtimesmentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

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191

131

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“…Over the past decades, a variety of studies were conducted on workload analysis and modeling of parallel computers to evaluate scheduler performance [5] [11], and to predict job performance [6] [20]. Using historical data of workload traces that were recorded on real machines, statistical analysis of workloads was performed to understand the characteristics, such as distributions of job runtime and memory usage, of a single HPC system [3], a multi-cluster supercomputer [8], or a Grid computing environment [2].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Workload Analysis and Modeling of a Petascale Supercomputer

You

Zhang

2013

Job Scheduling Strategies for Parallel Processing

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Abstract. The performance of supercomputer schedulers is greatly affected by the characteristics of the workload it serves. A good understanding of workload characteristics is always important to develop and evaluate different scheduling strategies for an HPC system. In this paper, we present a comprehensive analysis of the workload characteristics of Kraken, the world's fastest academic supercomputer and 11th on the latest Top500 list, with 112,896 compute cores and peak performance of 1.17 petaflops. In this study, we use twelve-month workload traces gathered on the system, which include around 700 thousand jobs submitted by more than one thousand users from 25 research areas. We investigate three categories of the workload characteristics: 1) general characteristics, including distribution of jobs over research fields and different queues, distribution of job size for an individual user, job cancellation rate, job termination rate, and walltime request accuracy; 2) temporal characteristics, including monthly machine utilization, job temporal distributions for different time periods, job inter-arrival time between temporally adjacent jobs and jobs submitted by the same user; 3) execution characteristics, including distributions of each job attribute, such as job queuing time, job actual runtime, job size, and memory usage, and the correlations between these job attributes. This work provides a realistic basis for scheduler design and comparison by studying the supercomputer's workload with new approaches such as using Gaussian mixture model, and new viewpoints such as from the perspective of user community. To the best of our knowledge, it's the first research to systematically investigate the workload characteristics of a petascale supercomputer that is dedicated to open scientific research.

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A standards‐based Grid resource brokering service supporting advance reservations, coallocation, and cross‐Grid interoperability

Elmroth

Tordsson

2009

Concurrency and Computation

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SUMMARYThe problem of Grid-middleware interoperability is addressed by the design and analysis of a featurerich, standards-based framework for all-to-all cross-middleware job submission. The service implements a decentralized brokering policy, striving to optimize the performance for an individual user by minimizing the response time for each job submitted. The architecture is designed with focus on generality and flexibility and builds on extensive use, internally and externally, of (proposed) Web and Grid services standards such as WSRF, JSDL, GLUE and WS-Agreement. The external use provides the foundation for easy integration into specific middlewares, which is performed by the design of a small set of plugins for each middleware. Currently, plugins are provided for integration into Globus Toolkit 4, NorduGrid/ARC, and LCG2. The internal use of standard formats facilitates customization of the job submission service by replacement of custom components for performing specific well-defined tasks. Most importantly, this enables the easy replacement of resource selection algorithms by algorithms that addresses the specific needs for a particular Grid environment and job submission scenario. The algorithms in our implementation perform resource selection based on performance predictions, and provide support for advance reservations as well as coallocation of multiple resources for simultaneous job start. The performance of the system is analyzed with focus on overall job service throughput (up to over 250 jobs/minute) and individual job response time (down to under one second).

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Modeling User Runtime Estimates

Cited by 92 publications

References 23 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

Comprehensive Workload Analysis and Modeling of a Petascale Supercomputer

A standards‐based Grid resource brokering service supporting advance reservations, coallocation, and cross‐Grid interoperability

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