Measuring cloud workload burstiness
2014 (English)In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing UCC 2014, IEEE conference proceedings, 2014, 566-572 p.Conference paper (Refereed)
Workload burstiness and spikes are among the main reasons for service disruptionsand decrease in the Quality-of-Service (QoS) of online services. They are hurdlesthat complicate autonomic resource management of datacenters. In this paper, wereview the state-of-the-art in online identification of workload spikes and quantifyingburstiness. The applicability of some of the proposed techniques is examined forCloud systems where various workloads are co-hosted on the same platform. Wediscuss Sample Entropy (SampEn), a measure used in biomedical signal analysis, as apotential measure for burstiness. A modification to the original measure is introducedto make it more suitable for Cloud workloads.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 566-572 p.
IdentifiersURN: urn:nbn:se:umu:diva-108397DOI: 10.1109/UCC.2014.87ISBN: 978-1-4799-7881-6OAI: oai:DiVA.org:umu-108397DiVA: diva2:852781
7th International Conference on Utility and Cloud Computing (UCC), 8-11 December 2014, London, England, United Kingdom
FunderEU, European Research CouncilSwedish Research Council