A Bee Colony based Multi-Objective Load Balancing Technique for Cloud Computing Environment

With the recent development of open cloud systems a surge in outsourcing assignments from an internal server to a cloud supplier has been seen. The Cloud can facilitate its clients enormous resources hence even during heavy load conditions. Since the cloud needed to be handle multiple clients workload at same time and each client may have different resource requirements hence choosing proper resources for given workload in such a system, in any case, is a difficult problem. This paper addresses this streamlining issue in a cloud system with different client’s priority groups and resource requirements and proposes a bee colony based MultiObjective load balancing technique, to attain efficient load scheduling over virtual machines under cloud. The proposed algorithm assigns the workload on the virtual machines in such a way that it minimizes the total processing cost in cloud without sacrificing priority of tasks and load management performance. General Terms Cloud Computing, Load Balancing.

[1]  Dusan Teodorovic,et al.  Bee Colony Optimization (BCO) , 2009, Innovations in Swarm Intelligence.

[2]  Rajkumar Buyya,et al.  InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services , 2010, ICA3PP.

[3]  Kuo-Qin Yan,et al.  Towards a Load Balancing in a three-level cloud computing network , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[4]  Ratan Mishra,et al.  Ant colony Optimization: A Solution of Load balancing in Cloud , 2012 .

[5]  Thomas L. Casavant,et al.  A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems , 1988, IEEE Trans. Software Eng..

[6]  Ian Sommerville,et al.  Cloud Migration: A Case Study of Migrating an Enterprise IT System to IaaS , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[7]  Thomas Sandholm,et al.  What's inside the Cloud? An architectural map of the Cloud landscape , 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing.

[8]  L. D. Dhinesh Babu,et al.  Honey bee behavior inspired load balancing of tasks in cloud computing environments , 2013, Appl. Soft Comput..

[9]  Jan Broeckhove,et al.  Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[10]  Dejan S. Milojicic,et al.  Improving HPC Application Performance in Cloud through Dynamic Load Balancing , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[11]  Inderveer Chana,et al.  Cloud Load Balancing Techniques : A Step Towards Green Computing , 2012 .