Extended Hoeffding Adaptive Tree based-Server Load Prediction in Cloud Computing environment

Abstract

Cloud Computing (CC) enables client-server relationship in order to release users from computational and storage responsibility. As multi-tenant environment, Cloud providers are dealing, in one hand, with multiple concurrent users each of which exhibits a different and variable behavior over time and in the other hand, with a performance interference due to the co-location of multiple virtual machines (VMs) in the same server. Therefore, a real time server load prediction is needed in order to ensure efficient resource provisioning. While classical data mining based techniques suffer from important evaluation time and are enable to react to changes as it arrives, stream mining techniques can provide a real time prediction and changes detection. Thus, in this paper we used a well known stream mining technique, Hoeffding Adaptive Tree (HAT), in order to provide real time server load prediction. The aim of our proposed technique is to detect and react on the fly to different kind of changes that can affect the server load. Therefore, we augmented HAT by ensemble drift detectors in order to produce more accurate prediction. In order to evaluate our proposed technique HAT-ADS, we first compared it with a well known load prediction technique based on Bayesian approach. Then we compared our solution with another HAT based techniques. Overall, The experimentation showed that HAT-ADS proved important flexibility to various types of changes providing high accuracy with quick evaluation time and small memory footprint.