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Integrating clustering and regression for workload estimation in the cloud
Author(s) -
Yu Yongjia,
Jindal Vasu,
Yen ILing,
Bastani Farokh,
Xu Jie,
Garraghan Peter
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5931
Subject(s) - workload , computer science , cluster analysis , task (project management) , cloud computing , cluster (spacecraft) , regression analysis , estimation , regression , data mining , statistics , machine learning , operating system , engineering , mathematics , systems engineering
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.