In a cloud computing environment, effective scheduling policies and load balancing have always been the aim. An efficient task scheduler must be proficient in a dynamically distributed environment and to the policy of efficient scheduling of jobs based upon the workload. In this research, a novel hybrid heuristic algorithm is developed for balancing the load among cloud nodes. This is achieved by hybridizing the existing ant colony optimization (ACO), artificial bee colony algorithm (ABC), and AHP (analytical hierarchy process) algorithm. The AHP algorithm and the artificial bee colony (ABC) algorithm is used for figuring out the best servers suitable for a particular job, and the ant colony algorithm is used to find the most efficient path to that particular server. The proposed algorithm is better in resource utilization. It also performs better load balancing, which keeps on improving with time. The result analysis shows better average response time and better average makespan time compared to other two existing algorithms.

Web technology has gained a huge momentum these days due to the digitization in almost all sectors. In cloud computing environment, the cloud data centers and the users of cloud computing are distributed extensively throughout the world and thus it is a huge challenge to manage these data in an orderly fashion. Load Balancing in this context means the distribution of load (data) in a smart manner such that the efficiency of the nodes are increased and the resources are used to their maximum potential which in turn would result in high user satisfaction. The algorithm proposed in this paper provides an optimal way of distributing load in servers connected to the Virtual Machines such that unnecessary overloading has been avoided in the cloud data centers. In this paper, the mixture of traditional algorithms like Weighted Round Robin and Weighted Least Connection have been used, which is showing better result in balancing the load among servers connected to the virtual machines.