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Queuing theoretic and evolutionary deployment optimization with probabilistic SLAs for service oriented clouds

  • University of Massachusetts Boston
  • OGIS International, Inc.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper focuses on service deployment optimization in cloud computing environments. In a cloud, each service in an application is deployed as one or more service instances. Different service instances operate at different quality of service (QoS) levels. In order to satisfy given service level agreements (SLAs) as end-to-end QoS requirements of an application, the application is required to optimize its deployment configuration of service instances. E 3/Q is a multiobjective genetic algorithm to solve this problem. By leveraging queuing theory, E3/Q estimates the performance of an application and allows for defining SLAs in a probabilistic manner. Simulation results demonstrate that E3/Q efficiently obtains deployment configurations that satisfy given SLAs.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE Congress on Services, SERVICES 2009
Pages661-669
Number of pages9
EditionPART 1
DOIs
StatePublished - 2009
EventSERVICES 2009 - 5th 2009 World Congress on Services - Bangalore, India
Duration: Sep 21 2009 → Sep 25 2009

Publication series

NameSERVICES 2009 - 5th 2009 World Congress on Services
NumberPART 1

Conference

ConferenceSERVICES 2009 - 5th 2009 World Congress on Services
Country/TerritoryIndia
CityBangalore
Period9/21/09 → 9/25/09

ASJC Scopus Subject Areas

  • Computer Networks and Communications
  • Software
  • General Social Sciences

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