Dynamic Load Balancing Based on Genetic Algorithm
S. Sandhya1 , N. K. Cauvery2

1Sandhya S, Computer Science and Engineering, R V College of Engineering, Bangalore, India.
2Dr. N. K. Cauvery, Information Science and Engineering, R V College of Engineering, Bangalore, India.
Manuscript received on 10 September 2019. | Revised Manuscript received on 25 September 2019. | Manuscript published on 30 September 2019. | PP: 176-179 | Volume-8 Issue-11, September 2019. | Retrieval Number: K12730981119/2019©BEIESP | DOI: 10.35940/ijitee.K1273.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Load balancing has been the focus of research over the current days in many domains but more importantly they are crucial for distributed computing. The research mainly focuses towards distributing load based on the current usage of nodes to facilitate effective resource utilization and obtain better performance from the system. Balancing load is to distribute the tasks on to the available or idle nodes so that resources are utilized fairly in a distributed environment. By developing strategies to assign the processes on a heavily loaded processor to an idle/under loaded processor in a way that balances out the load, the total processing time can be reduced hence achieving improved processor utilization. Genetic Algorithm(GA) is a search based approach that is robust and that can adapt to the search space for optimizing the solution are gaining immense popularity. GA in the proposed work considers the load as a parameter to evaluate fitness of the strings. The strings are also generated based on the load information of the nodes. The fitness evaluates the strings to identify only the underutilized or idle nodes which can take the transmitted load. Hence the work proposed explores and illustrates how GA could be employed to solve the problem of dynamic load-balancing.
Keywords: Distributed Computing, Dyanmic Load Balancing, Genetic Algorithm, Resource utilization.
Scope of the Article: