Workload Modeling for Performance Evaluation

Feitelson, Dror G.

doi:10.1007/3-540-45798-4_6

Cited by 96 publications

(64 citation statements)

References 77 publications

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“…However, real arrivals are actually self-similar [652,669,234], as demonstrated in Figure 9.36. In fact, this dataset was the main example used to demonstrate various ways to quantify selfsimilarity and long-range dependence in Section 7.4.…”

Section: Arrivalsmentioning

confidence: 97%

“…Locality of sampling is a generalization of both temporal and spatial locality [234,239]. It refers to the fact that workloads often display an internal structure: successive samples are not independent of each other, but rather tend to be similar to each other.…”

Section: Locality Of Samplingmentioning

confidence: 99%

“…Another good read is the classic paper by Ferrari [258]. This book has its roots in a tutorial presented at Performance 2002 [234].…”

Section: Introductionmentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

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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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Section: Arrivalsmentioning

confidence: 97%

Section: Locality Of Samplingmentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

Self Cite

197

131

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“…Such studies have been fundamental since they have allowed an understanding how the HPC centers users behave and how the resources of such centers are being used. Feitelson has presented several works concerning this topic, among others, he has published papers on log analysis for specific centers [15][27], general job and workload modeling [13][17] [14], and, together with Tsafrir, papers on detecting workload anomalies and flurries [38]. Calzarossa has also contributed with several workload modellization surveys [4] [5].…”

Section: Backfilling Policiesmentioning

confidence: 99%

A Job Self-scheduling Policy for HPC Infrastructures

Guim

Corbalan

Job Scheduling Strategies for Parallel Processing

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Abstract. The number of distributed high performance computing architectures has increased exponentially these last years. Thus, systems composed by several computational resources provided by different Research centers and Universities have become very popular. Job scheduling policies have been adapted to these new scenarios in which several independent resources have to be managed. New policies have been designed to take into account issues like multi-cluster environments, heterogeneous systems and the geographical distribution of the resources. Several centralized scheduling solutions have been proposed in the literature for these environments, such as centralized schedulers, centralized queues and global controllers. These approaches use a unique scheduling entity responsible for scheduling all the jobs that are submitted by the users. In this paper we propose the usage of self-scheduling techniques for dispatching the jobs that are submitted to a set of distributed computational hosts that are managed by independent schedulers (such as MOAB or LoadLeveler). It is a non-centralized and job-guided scheduling policy whose main goal is to optimize the job wait time. Thus, the scheduling decisions are done independently for each job instead of using a global policy where all the jobs are considered. On top of this, as a part of the proposed solution, we also demonstrate how the usage of job wait time prediction techniques can substantially improve the performance obtained in the described architecture.

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“…[12][13][14]). A recent focus area is, for example, statistical simulation for microarchitectural evaluation [15].…”

Section: Related Workmentioning

confidence: 99%

Towards Multi-application Workload Modeling in Sesame for System-Level Design Space Exploration

Thompson

Pimentel

Lecture Notes in Computer Science

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Abstract. The Sesame modeling and simulation framework aims at early and thus efficient system-level design space exploration of embedded multimedia system architectures. So far, Sesame only supported performance evaluation when mapping a single application onto a (multi-processor) architecture at the time. But since modern multimedia embedded systems are increasingly multi-tasking, we need to address the modeling of effects of executing multiple applications concurrently in our system-level performance models. To this end, this paper conceptually describes two multi-application workload modeling techniques for the Sesame framework. One technique is based on the use of synthetic application workloads while the second technique deploys only real application workloads to model concurrent execution of applications. For illustrative purposes, we also present a preliminary case study in which a Motion-JPEG encoder application is executed concurrently with a small synthetic producer-consumer application.

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Workload Modeling for Performance Evaluation

Cited by 96 publications

References 77 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

A Job Self-scheduling Policy for HPC Infrastructures

Towards Multi-application Workload Modeling in Sesame for System-Level Design Space Exploration

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