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Enhanced honeybee inspired load balancing algorithm for cloud environment

Publication Type : Conference Paper

Publisher : 2017 International Conference on Communication and Signal Processing (ICCSP)

Source : 2017 International Conference on Communication and Signal Processing (ICCSP), 1649-1653. (2018)

Url : https://ieeexplore.ieee.org/document/8286670

Keywords : cloud analyst, Cloud computing, data center, Load balancing, virtual machine

Campus : Mysuru

School : School of Arts and Sciences

Department : Computer Science

Year : 2018

Abstract : Cloud computing is one of the most important fields in the area of high performance computing. It enables the users to access the resources any time anywhere over the internet. As the number of users of cloud computing services are growing tremendously the need of load balancing becomes important. Load balancing is a mechanism, which mainly focuses on dividing the workload among the available resources equally so that we can achieve more throughput, less response time and reduce the overload on individual systems. Even though there are many existing algorithms to resolve the load balancing issues in cloud computing environment most of them lack significant amount of output while considering the response time and data center processing time. Maintenance of data in cloud infrastructure will be an easier task compared to traditional database management system. So in our work we minimize the response time, data center processing time of virtual machines and balance the load of the virtual machines with the help of Enhanced Honeybee Inspired load balancing algorithm, which will assign the task to virtual machines based on their priority, resource requirement and by evaluating the computing power of the virtual machines. © 2017 IEEE.

Cite this Research Publication : George, M.S., Nithin Das, K.C., Pushpa, B.R., "Enhanced honeybee inspired load balancing algorithm for cloud environment," Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017, pp. 1649-1653, 2018-January.

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