Multi-objective cuckoo optimizer for task scheduling to balance workload in cloud computing

Abstract

A cloud load balancer should be proficient to modify it’s approach to handle the various task kinds and the dynamic environment. In order to prevent situations where computing resources are excess or underutilized, an efficient task scheduling system is always necessary for optimum or efficient utilization of resources in cloud computing. Task Scheduling can be thought of as an optimization problem. As task scheduling in the cloud is an NP-Complete problem, the best solution cannot be found using gradient-based methods that look for optimal solutions to NP-Complete problems in a reasonable amount of time. Therefore, the task scheduling problem should be solved using evolutionary and meta-heuristic techniques. This study proposes a novel approach to task scheduling using the Cuckoo Optimization algorithm. With this approach, the load is effectively distributed among the virtual machines that are available, all the while keeping the total response time and average task processing time(PT) low. The comparative simulation results show that the proposed strategy performs better than state-of-the-art techniques such as Particle Swarm optimization, Ant Colony optimization, Genetic Algorithm and Stochastic Hill Climbing.