RTSLPS: Real time server load prediction system for the ever-changing cloud computing environment

Abstract

Cloud Computing (CC) proposes a multi-tenant framework used by multiple concurrent users, each of which exhibits different and varied behavior. Such heterogeneity shapes a highly fluctuating load and creates new usage patterns overtime at the server level. Virtual Machines (VMs) interference also plays a big part in inducing changes at server load. Server load prediction is deemed crucial to ensure efficient resource usage. The execution of real-time interactive tasks constitutes an important part of CC. Thus, we propose, in this paper, a real-time server load prediction system based on incoming task classification and VM interference detection. The incoming task classification is used to capture the incoming workload trend and VM interference detection aims to capture the interference rate. Finally, load prediction considers server actual resources’ usage, VM interference rate, and incoming workload trend. We propose an improved version of Hoeffding Adaptive Tree (HAT), augmented by ensemble drift detectors. Results show that our Real-Time Server Load Prediction System (RTSLPS) was able to deliver great flexibility dealing with changes and very good accuracy with quick evaluation time and a small memory footprint.

Keywords

• Cloud computing
• Concept drift
• Server load
• Stream mining
• Real time prediction
• User behavior