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Online Spike Detection in Cloud Workloads

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Author

  • Amardeep Mehta
  • Jonas Dürango
  • Johan Tordsson
  • Erik Elmroth

Summary, in English

We investigate methods for detection of rapid workload increases (load spikes) for cloud workloads. Such rapid and unexpected workload spikes are a main cause for poor performance or even crashing applications as the allocated cloud resources become insufficient. To detect the spikes early is fundamental to perform corrective management actions, like allocating additional resources, before the spikes become large enough to cause problems. For this, we propose a number of methods for early spike detection, based on established techniques from adaptive signal processing. A comparative evaluation shows, for example, to what extent the different methods manage to detect the spikes, how early the detection is made, and how frequently they falsely report spikes.

Publishing year

2015

Language

English

Pages

446-451

Publication/Series

[Host publication title missing]

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Control Engineering

Keywords

  • cloud
  • cloud workload
  • workload spike
  • spike detection

Conference name

2nd IEEE Workshop on Cloud Analytics

Conference date

2015-03-12

Conference place

Tempe, AZ, United States

Status

Published

Project

  • EIT_VR CLOUD Cloud Control

Research group

  • LCCC