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  • Integrating Clustering and Regression for Workload Estimation in the Cloud

    Rights statement: This is the peer reviewed version of the following article: Yu, Y, Jindal, V, Yen, I‐L, Bastani, F, Xu, J, Garraghan, P. Integrating clustering and regression for workload estimation in the cloud. Concurrency Computat Pract Exper. 2020; e5931. https://doi.org/10.1002/cpe.5931 which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5931 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

    Accepted author manuscript, 2.72 MB, PDF document

    Embargo ends: 20/07/21

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Integrating Clustering and Regression for Workload Estimation in the Cloud

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Article numbere5931
<mark>Journal publication date</mark>10/12/2020
<mark>Journal</mark>Concurrency and Computation Practice and Experience
Issue number23
Volume32
Number of pages20
Publication StatusPublished
Early online date20/07/20
<mark>Original language</mark>English

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.

Bibliographic note

This is the peer reviewed version of the following article: Yu, Y, Jindal, V, Yen, I‐L, Bastani, F, Xu, J, Garraghan, P. Integrating clustering and regression for workload estimation in the cloud. Concurrency Computat Pract Exper. 2020; e5931. https://doi.org/10.1002/cpe.5931 which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5931 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.