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Auto-Scaling Provision Basing on Workload Prediction in the Virtualized Data Center

Auto-Scaling Provision Basing on Workload Prediction in the Virtualized Data Center
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Author(s): Danqing Feng (Harbin Institute of Technology, Harbin, China & AirForce Communication NCO Academy, Dalian, China), Zhibo Wu (Harbin Institute of Technology, Harbin, China), Decheng Zuo (Harbin Institute of Technology, Harbin, China) and Zhan Zhang (Harbin Institute of Technology, Harbin, China)
Copyright: 2020
Volume: 12
Issue: 1
Pages: 17
Source title: International Journal of Grid and High Performance Computing (IJGHPC)
Editor(s)-in-Chief: Emmanuel Udoh (Sullivan University, USA), Ching-Hsien Hsu (Asia University, Taiwan) and Mohammad Khan (East Tennessee State University, USA)
DOI: 10.4018/IJGHPC.2020010104

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Abstract

With the development in the Cloud datacenters, the purpose of the efficient resource allocation is to meet the demand of the users instantly with the minimum rent cost. Thus, the elastic resource allocation strategy is usually combined with the prediction technology. This article proposes a novel predict method combination forecast technique, including both exponential smoothing (ES) and auto-regressive and polynomial fitting (PF) model. The aim of combination prediction is to achieve an efficient forecast technique according to the periodic and random feature of the workload and meet the application service level agreement (SLA) with the minimum cost. Moreover, the ES prediction with PSO algorithm gives a fine-grained scaling up and down the resources combining the heuristic algorithm in the future. APWP would solve the periodical or hybrid fluctuation of the workload in the cloud data centers. Finally, experiments improve that the combined prediction model meets the SLA with the better precision accuracy with the minimum renting cost.

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