An Energy and Deadline-Aware Scheduler with Hybrid Optimization in Virtualized Clouds

For virtualized cloud computing systems, energy conservation is a major challenge. It leads to several advantages like reducing running costs, protecting the environment, and bringing down running costs. Simultaneously, a strategy for energy-efficient task scheduling can be a suitable method to attain these objectives. Another major challenge during scheduling is mapping cloud resources to various user requests within a user-defined deadline with minimal consumption of cloud resources. In this work, using intelligent metaheuristic algorithms for processing the requests and tasks of users to maintain deadlines and to minimize the energy usage is proposed. This work proposed an energy and deadline aware scheduling method in a heterogeneous and virtualized cloud for resolving energy consumption issues. For this work, a mutated particle swarm optimization (PSO), mutated artificial bee colony (ABC), and hybrid ABC–PSO were used to reduce the average makespan, increasing the resource utilization under a constraint of a deadline. For this model, the tasks are arranged in an ascending order based on the priority of length, label the state of the VM, thus achieving the constraint of a deadline after which the tasks are mapped to a VM with a minimum processing time. The idea behind the proposed algorithm was improving both scheduling and deadline in cloud computing using both local and global algorithms. The outcome of the simulation shows that the method will help in attaining good performance compared to the current techniques.