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

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Adnan Ashraf, Benjamin Byholm, Ivan Porres
Editors: Omer Rana, Manish Parashar
Publication year: 2015
Publisher: ACM
Book title: 8th IEEE/ACM International Conference on Utility and Cloud Computing
Start page: 341
End page: 347
ISBN: 978-1-4503-3890-5


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.


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


Last updated on 2020-21-09 at 05:44