Cloud computing as a service promises many business benefits. The cost to pay is that it also faces many technique challenges. One of the challenges is to effectively manage cloud capacity in response to the increased demand changes in clouds, as computing customers now can provision and de-provision virtual machines more frequently. This paper studies cloud capacity prediction as a response to the challenge. We propose an integrated solution for intelligent cloud capacity estimation. In this solution, a novel measure is introduced to quantify and guide the prediction process. Then an ensemble method is utilized to predict the future provisioning/de-provisioning demands respectively. The cloud capacity is estimated using the active virtual machines and the future provisioning/de-provisioning demands altogether. Our proposed solution is simple and with low computational cost. The experiments on the IBM Smart Cloud Enterprise trace data shows our solution is effective.
['Yexi Jiang', 'Chang-shing Perng', 'Tao Li', 'Rong N. Chang']
Intelligent cloud capacity management