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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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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Abstract 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.
AbstractList 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.
Author Seleznjev, Oleg
Ali-Eldin, Ahmed
Tordsson, Johan
Elmroth, Erik
Sjostedt-de Luna, Sara
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  surname: Elmroth
  fullname: Elmroth, Erik
  email: elmroth@cs.umu.se
  organization: Dept. of Comput. Sci., Umea Univ., Umea, Sweden
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Snippet 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...
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SubjectTerms Electronic publishing
Encyclopedias
Entropy
Internet
Measurement
Servers
Title Measuring Cloud Workload Burstiness
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