Service replication with mapreduce in clouds

Wei Tek Tsai, Peide Zhong, Jay Elston, Xiaoying Bai, Yinong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

In a tupical cloud environment, services wait to serve users' request. If a service receives more requests than it can handle, it needs to acquire additional resources. This paper proposes a new service replications that allows a cloud to adjust its service instance deployments in response to existing and projected service request loads. This approach is called Service-Level MapReduce (SLMR) because itis based on MapReduce, a parallel processing mechanism commonly used in cloud environments such as GAE (Google App Engin. SLMRincludes dynamic service replication and pre-deployed service replication. Furthermore, a passive SLMR approach that depends on the cloud management service (CMS) and an active SLMR approach that does not need the support from CMS will be introduced.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 10th International Symposium on Autonomous Decentralized Systems, ISADS 2011
Pages381-388
Number of pages8
DOIs
StatePublished - May 12 2011
Event2011 10th International Symposium on Autonomous Decentralized Systems, ISADS 2011 - Tokyo and Hiroshima, Japan
Duration: Mar 23 2011Mar 27 2011

Publication series

NameProceedings - 2011 10th International Symposium on Autonomous Decentralized Systems, ISADS 2011

Other

Other2011 10th International Symposium on Autonomous Decentralized Systems, ISADS 2011
CountryJapan
CityTokyo and Hiroshima
Period3/23/113/27/11

Keywords

  • Cloud
  • Cloud management service
  • Service
  • Service level mapreduce
  • Service replication

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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