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Detecting anomaly in cloud platforms using a wavelet-based framework

  • David O'Shea
  • , Vincent C. Emeakaroha
  • , Neil Cafferkey
  • , John Morrison
  • , Theo Lynn
  • University College Cork
  • Munster Technological University
  • Dublin City University
  • Irish Centre for Cloud Computing and Commerce

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

Cloud computing enables the delivery of compute resources as services in an on-demand fashion. The reliability of these services is of significant importance to their consumers. The presence of anomaly in Cloud platforms can put their reliability into question, since an anomaly indicates deviation from normal behaviour. Monitoring enables efficient Cloud service provisioning management; however, most of the management efforts are focused on the performance of the services and little attention is paid to detecting anomalous behaviour from the gathered monitoring data. In addition, the existing solutions for detecting anomaly in Clouds lacks a multi-dimensional approach. In this chapter, we present a wavelet-based anomaly detection framework that is capable of analysing multiple monitored metrics simultaneously to detect anomalous behaviour. It operates in both frequency and time domains in analysing …
Original languageEnglish (Ireland)
Title of host publicationCloser 2016
PublisherSpringer Nature
Pages131-150
Publication statusPublished - 23 Apr 2016

Publication series

Name Communications in Computer and Information Science
PublisherSpringer Nature

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