Andrej Podzimek, L. Chen, L. Bulej, Walter Binder, P. Tůma
{"title":"Showstopper: The Partial CPU Load Tool","authors":"Andrej Podzimek, L. Chen, L. Bulej, Walter Binder, P. Tůma","doi":"10.1109/MASCOTS.2014.75","DOIUrl":null,"url":null,"abstract":"Provisioning strategies relying on CPU load may be suboptimal for many applications, because the relation between CPU load and application performance can be non-linear and complex. With the knowledge of the relation between CPU load and application performance, resource provisioning strategies could be tuned to a particular application, but the required knowledge is difficut to obtain, because classic benchmarking is not suited for performance evaluation of partial-load scenarios. As a remedy, we present Showstopper, a tool capable of achieving and sustaining a predefined partial CPU load (or replay a load trace) by controlling the execution of arbitrary CPU-bound workloads. By analyzing performance interference among applications running in colocated virtual machines, we demonstrate how Showstopper enables systematic and reproducible exploration of the platform- and application-specific relation between CPU load and application performance.","PeriodicalId":345311,"journal":{"name":"2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MASCOTS.2014.75","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Provisioning strategies relying on CPU load may be suboptimal for many applications, because the relation between CPU load and application performance can be non-linear and complex. With the knowledge of the relation between CPU load and application performance, resource provisioning strategies could be tuned to a particular application, but the required knowledge is difficut to obtain, because classic benchmarking is not suited for performance evaluation of partial-load scenarios. As a remedy, we present Showstopper, a tool capable of achieving and sustaining a predefined partial CPU load (or replay a load trace) by controlling the execution of arbitrary CPU-bound workloads. By analyzing performance interference among applications running in colocated virtual machines, we demonstrate how Showstopper enables systematic and reproducible exploration of the platform- and application-specific relation between CPU load and application performance.