Data-Driven Adaptive Prediction of Cloud Resource Usage

Abstract

Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.

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“…It is difficult to use traditional optimization mechanisms that are based on static optimization, which may not be suitable for the dynamic and complex features of distributed environments such as cloud computing [14]. Optimizing the performance on the long term requires designing an efficient policy that considers the challenges related to the dynamicity of the system, like workload nature, QoS requirements, and the characteristics of candidates (potential executors), which usually have an enormous state space.…”