Integrating clustering and regression for workload estimation in the cloud. (20th July 2020)

• Record Type: Journal Article
• Authors: Yu, Yongjia; Jindal, Vasu; Yen, I‐Ling; Bastani, Farokh; Xu, Jie; Garraghan, Peter
• Other Names: Xu Zheng guestEditor.; Zhou Qingyuan guestEditor.; Dong Fang guestEditor.; Yong Jianming guestEditor.; Fei Xiang guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 23(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-20
• Subjects: cloud computing -- dynamic time warp distance -- workload clustering -- workload estimation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5931
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Workload prediction has been widely researched in the literature. However, existing techniques are per‐job based and useful for service‐like tasks whose workloads exhibit seasonality and trend. But cloud jobs have many different workload patterns and some do not exhibit recurring workload patterns. We consider job‐pool‐based workload estimation, which analyzes the characteristics of existing tasks' workloads to estimate the currently running tasks' workload. First cluster existing tasks based on their workloads. For a new task J, collect the initial workload of J and determine which cluster J may belong to, then use the cluster's characteristics to estimate J ′s workload. Based on the Google dataset, the algorithm is experimentally evaluated and its effectiveness is confirmed. However, the workload patterns of some tasks do have seasonality and trend, and conventional per‐job‐based regression methods may yield better workload prediction results. Also, in some cases, some new tasks may not follow the workload patterns of existing tasks in the pool. Thus, develop an integrated scheme which combines clustering and regression and utilize the best of them for workload prediction. Experimental study shows that the combined approach can further improve the accuracy of workload prediction.