{"title":"CLOUDGEN: Workload Generation for the Evaluation of Cloud Computing Systems","authors":"Furkan Koltuk, Alper Yazar, E. G. Schmidt","doi":"10.1109/SIU.2019.8806358","DOIUrl":null,"url":null,"abstract":"In this paper, we propose CLOUDGEN workflow that produces synthetic workloads for Infrastructure and Platform as a Service for the evaluation of resource management approaches in cloud computing systems. To this end, CLOUDGEN systematically processes and clusters records in a given workload trace and fits distributions for different workload parameters within the clusters. Different than the previous work, clustering is carried out to produce different virtual machine types for achieving models that are suitable for producing Infrastructure and Platform as a Service workload models. Finally, we demonstrate CLOUDGEN by modeling recent Azure traces with enough detail to enable researchers to use these models and generating synthetic traces that are statistically similar to the Azure traces.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 27th Signal Processing and Communications Applications Conference (SIU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2019.8806358","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we propose CLOUDGEN workflow that produces synthetic workloads for Infrastructure and Platform as a Service for the evaluation of resource management approaches in cloud computing systems. To this end, CLOUDGEN systematically processes and clusters records in a given workload trace and fits distributions for different workload parameters within the clusters. Different than the previous work, clustering is carried out to produce different virtual machine types for achieving models that are suitable for producing Infrastructure and Platform as a Service workload models. Finally, we demonstrate CLOUDGEN by modeling recent Azure traces with enough detail to enable researchers to use these models and generating synthetic traces that are statistically similar to the Azure traces.