Detection of time series patterns and periodicity of cloud computing workloads

Abstract

Workload pattern detection can be used as part of a proactive decision-making approach to optimize resource provisioning strategies and anticipate any performance problem in cloud computing environments. Existing workload pattern detection approaches suffer from two essential limitations which restrict their effectiveness in such environments. Specifically, they require massive human intervention, and are specific to particular types of workload data. To overcome these limitations, we propose in this paper a generic workload pattern and periodicity detection technique that employs the prefix transposition approach from the molecular biology domain to detect workload periodicity in node-specific and aggregated cloud environments. The strengths of the proposed technique compared to the state of the art lie in its ability to be applied to any type of workload, and also to detect patterns of varying lengths, amplitudes, and shapes. Experiments conducted on cloud server nodes and aggregated CPU and throughput workload datasets collected from Information Technology (IT) and Telecom domains reveal that our solution improves the accuracy of the detections, especially in harsh environments, where the lengths, shapes, and amplitudes of patterns vary, as compared to the autocorrelation technique. Other findings show that the proposed approach is also highly efficient at detecting multiple short-term and long-term periodic patterns on any type of time series-based cloud computing workloads of different time granularity.