An efficient clustering and load balancing of distributed cloud data centers using graph theory. (4th January 2019) - Devi, R. Kanniga; Murugaboopathi, G.
Summary: The prime focus of the Cloud Service Providers is enhancing the service delivery performance of the distributed cloud data centers. The clustering and load balancing of distributed cloud data centers have significant impact on its service delivery performance. Hence, this paper models distributed cloud data center environment as a network graph and proposes a two‐phase cluster‐based load balancing (CLB) algorithm based on a graph model. The first phase proposes a Cloud Data Center Clustering algorithm to cluster the distributed cloud data centers based on their proximity. The second phase proposes a Client‐Cluster Assignment algorithm to perform uniform distribution of the client requests across the clusters to enable load balancing. To assess the performance, the proposed algorithms are compared with other K ‐constrained graph‐based clustering algorithms namely, graph‐based K ‐means and K ‐spanning tree algorithms on a simulated distributed cloud data center environment. The experimental results reveal that the proposed CLB algorithm outperforms the compared algorithms in terms of the average clustering time, load distribution, and fairness index and hence improves the service delivery performance of the distributed cloud data centers. Abstract : This paper proposes solutions to clustering and load balancing of distributed cloud data centers using graph theory. It proposes a new cluster‐based load balancing algorithm, which runs in two phases, where Cloud Data Center Clustering for clustering cloud data centers and Client‐Cluster Assignment algorithm to distribute the client request across clusters uniformly. The proposed algorithm minimize average clustering time and improves load distribution and fairness index.