A Multi-Objective ACS Algorithm to Optimize Cost, Performance, and Reliability in the Cloud

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

In this paper, we present a novel Multi-Objective Ant Colony System algorithm to optimize Cost, Performance, and Reliability (MOACS-CoPeR) in the cloud. The proposed algorithm provides a metaheuristic-based approach for the multi-objective cloud-based software component deployment problem. MOACS-CoPeR explores the search-space of architecture design alternatives with respect to several architectural degrees of freedom and produces a set of Pareto-optimal deployment configurations. We also present a Java-based implementation of our proposed algorithm and compare its results with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). We evaluate the two algorithms against a cloud-based storage service, which is loosely based on a real system.
Original languageUndefined/Unknown
Title of host publication8th IEEE/ACM International Conference on Utility and Cloud Computing
EditorsOmer Rana, Manish Parashar
PublisherACM
Pages341–347
ISBN (Print)978-1-4503-3890-5
DOIs
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
EventIEEE/ACM International Conference on Utility and Cloud Computing - 8th IEEE/ACM International Conference on Utility and Cloud Computing
Duration: 7 Dec 201510 Dec 2015

Conference

ConferenceIEEE/ACM International Conference on Utility and Cloud Computing
Period07/12/1510/12/15

Keywords

  • Cloud computing
  • Multi-objective optimization
  • Software component deployment
  • ant colony optimization (ACO)

Cite this