Load Balancing in Peer-to-Peer System Using Fuzzy C-Means Clustering

Literature Review

Timothy (1995) proposed that fuzzy logic is very useful to solve uncertain problems. Karimi et al. (2009) presents a new dynamic load balancing algorithm with fuzzy logic and it show better response time than Round Robin algorithm by 30.84%. Ally et al. (2002) presents load balancing in distributed computing systems with help of fuzzy functions and solve uncertainty problem in state information and in task selection with the help of fuzzy functions of variable membership values. Clustering motive is to distribute cases (people, objects, events etc.) into groups, so that the degree of association to be strong between objects of the same cluster and weak between objects of different clusters (Olson et al., 1996). Bezdek et al. (1980, 1981, 1987) present some fuzzy techniques for clustering numerical data. Horng et al. (2002) proposed convergence theorem for a fuzzy ISODATA clustering algorithm. Ruspini (1970) presents numerical methods for fuzzy clustering. Huang et al. (2003) improves performance of computer network by balancing load among computers with the help of receiver-initiated fuzzy logic control method. Ahn et al. (2007) presents load balancing in distributed systems using an intelligent fuzzy grouping approach. In the round robin algorithm (Pradeep et al., 1996), processes are equally divided among all processors. Each new process is assigned to a new processor in a round robin fashion. Whenever number of processes larger than number of processors, round robin algorithm works well. No need of inter-process communication in case of round robin algorithm. In the bidding algorithm (Xu et al., 2003), when a node is heavy loaded, it multicasts a request for bids to the other nodes in the system. After collection of all bids by heavy loaded node, the best bid is chosen as light weighted node. If none of the node is found in the group for load transfer, the bidding procedure starts over again.