Resampling with Feedback: A New Paradigm of Using Workload Data for Performance Evaluation: (Extended Version)

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Reliable performance evaluations require representative workloads. This has led to the use of accounting logs from production systems as a source for workload data in simulations. But using such logs directly suffers from various deficiencies, such as providing data about only one specific situation, and lack of flexibility, namely the inability to adjust the workload as needed. Creating workload models solves some of these problems but creates others, most notably the danger of missing out on important details that were not recognized in advance, and therefore not included in the model. Resampling solves many of these deficiencies by combining the best of both worlds. It is based on partitioning real workloads into basic components (specifically the job streams contributed by different users), and then generating new workloads by sampling from this pool of basic components. The generated workloads are adjusted dynamically to the conditions of the simulated system using a feedback loop, which may change the throughput. Using this methodology analysts can create multiple varied (but related) workloads from the same original log, all the time retaining much of the structure that exists in the original workload. Resampling with feedback thus provides a new way to use workload logs which benefits from the realism of logs while eliminating many of their drawbacks. In addition, it enables evaluations of throughput effects that are impossible with static workloads. This paper reflects a keynote address at JSSPP 2021, and provides more details than a previous version from a keynote at Euro-Par 2016 [18]. It summarizes my and my students’ work and reflects a personal view. The goal is to show the big picture and the building and interplay of ideas, at the possible expense of not providing a full overview of and comparison with related work.

Original languageAmerican English
Title of host publicationJob Scheduling Strategies for Parallel Processing - 24th International Workshop, JSSPP 2021, Revised Selected Papers
EditorsDalibor Klusáček, Walfredo Cirne, Gonzalo P. Rodrigo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-32
Number of pages30
ISBN (Print)9783030882235
DOIs
StatePublished - 2021
Event24th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2021 - Virtual, Online
Duration: 21 May 202121 May 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12985 LNCS

Conference

Conference24th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2021
CityVirtual, Online
Period21/05/2121/05/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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