Abstract
Cloud computing can provide strong processing capacity to tackle huge amounts of requests from many users. Task scheduling problem is the keystone to Cloud computing. In this paper we propose a task scheduling policy based on Ant Colony Optimization. This policy can minimize the makespan of the tasks submitted to the cloud system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25:599–616
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge
Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41:23–50
The CLOUDS (2012) Lab: CloudSim: A novel framework for modeling and simulation of cloud computing infrastructures and services. http://www.gridbus.org/cloudsim/. Accessed on 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Ai, L. (2013). Task Scheduling Policy Based on Ant Colony Optimization in Cloud Computing Environment. In: Zhang, Z., Zhang, R., Zhang, J. (eds) LISS 2012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32054-5_133
Download citation
DOI: https://doi.org/10.1007/978-3-642-32054-5_133
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32053-8
Online ISBN: 978-3-642-32054-5
eBook Packages: Business and EconomicsBusiness and Management (R0)