QoS-Aware Cloud Resource Prediction for Computing Services

Abstract

Computing services are increasingly located in computing clouds, which allows for on-demand scalability but may also increase operating costs. It is believed that cloud expenses constitute a significant budget item in companies of all sizes. There is a considerable body of work dedicated to reducing the costs of cloud computing, which is mainly focused on optimizing the use of cloud resources. Such optimization, however, tends to result in the deterioration of computing service responsiveness and, as a result, quality of service parameters, especially when applied to real-world, noisy data which include anomalies. This article presents a novel approach which involves a six-stage optimization process incorporating load prediction supported by machine learning, the discovery of computing service characteristics and long-term planning of resource usage alongside anomaly detection and continuous monitoring with a self-adapting ability. The solution proposed works autonomously, builds knowledge about the optimized system and its load patterns, calculates cost-optimal resource provisioning plans and adapts to rapid environmental changes. Our evaluation using Microsoft's Azure cloud environment demonstrates savings ranging from 31% to 89% depending on the test scenario; cost reductions for other cloud computing providers were estimated as well.