Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures

Cloud providers aim at guaranteeing Service Level Agreements (SLAs) in a resource-efficient way. This, amongst others, means that resources of virtual (VMs) and physical machines (PMs) have to be autonomically allocated responding to external influences as workload or environmental changes. Thereby, workload volatility (WV) is one of the crucial factors that influence the quality of suggested allocations. In this paper we devise a novel approach for self-adaptive and resource-efficient decision-making considering the three conflicting goals of minimizing the number of SLA violations, maximizing resource utilization, and minimizing the number of necessary time- and energy-consuming reconfiguration actions. We propose self-adaptive rule-based knowledge management for autonomic VM reconfiguration considering the rapidness of changes in the workload, i.e., WV. We introduce a novel WV categorization and present cost and volatility based methods for self-tuning. We evaluate these methods by a large variety of synthetically generated workloads, and by real-world measurements gathered from an image rendering application and a scientific workflow for RNA sequencing. Evaluation shows that in most cases the self-adaptive approach outperforms the static approach.