Measuring Cloud Workload Burstiness

Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate autonomic resource management of datacenters. In this paper, we review the state-of-the-art in online identification of...

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Bibliographic Details
Published in:Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing pp. 566 - 572
Main Authors: Ali-Eldin, Ahmed, Seleznjev, Oleg, Sjostedt-de Luna, Sara, Tordsson, Johan, Elmroth, Erik
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2014
Subjects:
Electronic publishing
Encyclopedias
Entropy
Internet
Measurement
Servers
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Summary:Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate autonomic resource management of datacenters. In this paper, 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 various workloads are co-hosted on the same platform. We discuss Sample Entropy (Samp En), 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.
DOI:10.1109/UCC.2014.87