A Load Balancing Model in Public Cloud Using ANFIS and GSO

Recent days, cloud computing is an improving area in research and industry, which includes virtualization, distributed computing, internet, and web services. A cloud contains elements such as data centers, clients, distributed servers, internet which includes fault tolerance, high accessibility, efficiency, scalability, flexibility, reduced overhead for users, less cost of ownership, on demand services and etc. Cloud computing services are becoming omnipresent and serve as the primary source of calculating power for different applications like activity and personal computing applications. It has many benefits all along with some fundamental problems to be resolved in order to improve reliability of cloud environment. Also that, these problems is associated with load management, tolerance and different security issues in cloud environment. In this paper introduces a better load balancing model for the public cloud based on the cloud partitioning concept with a switch mechanism to select different strategies for different situations. Adaptive neuro-fuzzy inference system (ANFIS) based load balancing algorithm and Glowworm swarm optimization (GSO) based load balancing algorithm are proposed to the load balancing strategy to improve the efficiency in the public cloud environment. Experimental result shows that the proposed method gives better results when compared with other traditional methods.