Load Balancing using Cluster and Heuristic Algorithms in Cloud Domain

Objectives: Enterprises are using cloud services to improve the performance as well as to reduce the overall cost. As access to cloud resources increases, the load on servers increases exponentially. In enhancing the usage and utilization of cloud services, load balancing is essential. In the proposed system, cluster and heuristic algorithms are combined to reduce the makespan of tasks and to balance the load in cloud environment. Methods/Statistical Analysis: Initially in cloud environment, traditional load balancing algorithms are used to balance the load on servers. The solution or result obtained by such algorithms is not the perfect one. Glaring algorithms are required for load balancing as well as to obtain most favorable solution or solution which is most appropriate for the given problem. Heuristic algorithms significantly balance the milestone on the servers by giving a more optimal solution quickly and efficiently. Findings: Proposed system simulates the load balancing system that partitions the virtual machines and schedules the task using heuristic algorithms by considering task requirements. Application: This paper summarizes the survey of various heuristic algorithms. We have proposed the task based approach towards load balancing (TB-LB) in cloud environment that uses k-means clustering approach to organize Virtual Machines (VMs) into groups and it combines the features of three heuristic algorithms to increase the speed and to keep the makespan small as possible.