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RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center

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4 Author(s)
Wei Fang ; Sch. of Comput. Sci., Fudan Univ., Shanghai, China ; ZhiHui Lu ; Jie Wu ; ZhenYin Cao

Cloud data centers and virtualization are being highly considered for enterprises and industries. However, elastic fine-grained resource provision while ensuring performance and SLA guarantees for applications requires careful consideration of important and extremely challenging tradeoffs. In this paper, we present RPPS (Cloud Resource Prediction and Provisioning scheme), a scheme that automatically predict future demand and perform proactive resource provisioning for cloud applications. RPPS employs the ARIMA model to predict the workloads in the future, combines both coarse-grained and fine-grained resource scaling under different situations, and adopts a VM-complementary migration strategy. RPPS can resolve predictive resource provisioning problem when enterprises confront demand fluctuations in cloud data center. We evaluate a prototype of RPPS with traces collected by ourselves using typical CPU intensive applications and as well as workloads from a real data center. The results show that it not only has high prediction accuracy (about 90% match in most time) but also scales the resource well.

Published in:

Services Computing (SCC), 2012 IEEE Ninth International Conference on

Date of Conference:

24-29 June 2012