Load balancing optimization in cloud computing: Applying Endocrine-particale swarm optimization

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 2015 IEEE International Conference on Electro Information Technology, 2015, pp. 165 - 169
Issue Date:
2015-06-10
Full metadata record
Files in This Item:
Filename Description Size
07293424.pdfAccepted Manuscript2.33 MB
Adobe PDF
© 2015 IEEE. Load balancing optimization is categorized as NP-hard problem, playing an important role in enhancing the cloud utilization. Different methods have been proposed for achieving the system load balancing in cloud environment. VM migration is one of these techniques, proposed to improve the VMs' functionality. Despite of the advantageous of VM migration, there are still some drawbacks which urged researchers to improve VM migration methods. In this paper we propose a new load balancing technique, using Endocrine algorithm which is inspired from regulation behavior of human's hormone system. Our proposed algorithm achieves system load balancing by applying self-organizing method between overloaded VMs. This technique is structured based on communications between VMs. It helps the overloaded VMs to transfer their extra tasks to another under-loaded VM by applying the enhanced feed backing approach using Particle Swarm Optimization (PSO). To evaluate our proposed algorithm, we expanded the cloud simulation tool (Cloudsim) which is developed by University of Melbourne. The simulation result proves that our proposed load balancing approach significantly decreases the timespan compared to traditional load balancing techniques. Moreover it increases the Quality Of Service (QOS) as it minimizes the VMs' downtime.
Please use this identifier to cite or link to this item: