Hybrid Fuzzy C Means Clustering (Fcm) And Improved Bat Optimization Algorithm For Multi-Servers Load Balancing In The Cloud Environment
Cloud computing empowers information sharing and furthermore gives the clients numerous resources. The clients are approached to pay just for the measure of resources they have utilized. Cloud computing is intended to store the conveyed resources and information in the open condition. Load balancing is one of the significant issues in cloud computing condition. Despite the fact that the recent work in load balancing has been focusing on request migration strategies utilizing multi-servers, the multi-server based load balancing algorithms doesn't understand the task scheduling parallel. The general execution time is additionally consumed to the greatest. The proposed work points in the reducing the general delay time for multi-server subordinate load balancing. Fuzzy C Means Clustering (FCM) technique can be utilized by clustering. The entirety of the interims between the gathering tasks around rises to the general task computation time. A Notable optimization algorithm is utilized for bunch based multi servers called the improved bat optimization (IBO) algorithm .The algorithm was implemented successfully and the results were compared to two popular existing algorithms, namely Meta –Heuristic Firefly Optimization Algorithm and Fuzzy C means Clustering & Improvised Bat Algorithm (FCMIBO).The proposed algorithm proves to be better as the communication cost, resource utilization and execution time is lower, also an increased throughput, when compared to the Iterative Proximal calculation with Meta heuristic Firefly Optimization Algorithm (IPMFOA) algorithm.