B1769
Topic:
Title: Measuring cloud workload burstiness
Authors: Sara Sjostedt de Luna - Umea University (Sweden) [presenting]
Abstract: Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality- of-Service (QoS) of online-services. It is a hurdle that complicates autonomic resource management of data centers. We review the state-of-the-art in online identification of workload spikes and quantifying burstiness. The applicability of some of the proposed techniques is examined for cloud systems where different workloads are co-hosted on the same platform. We discuss Sample Entropy (SampEn), a measure used in biomedical signal analysis, as a potential measure for burstiness. A modification to the original measure is introduced to make it more suitable for cloud workloads.