Optimizing Task Scheduling for Energy Aware Networks

Abstract

Cloud computing has evolved into well-known high-performance provider. Cloud computing offers on-request services, that provide some highly useful resources to its clients through the medium of internet. All this makes cloud computing dynamically scalable. In Cloud computing, task scheduling has very important part in increasing efficiency of several services, as well as it contributes to significant energy saving in cloud environment and Internet of Things (IoT) networks. Choosing the right and the most appropriate scheduling algorithm for allocation of resources and scheduling of tasks is very important. It required for the efficient and the most appropriate mapping of tasks or jobs to a resource. We need to consider and keep check on various parameters like resource utilization, makespan, accessibility, time, scalability, cost and etc., when we talk about assigning suitable resources to the tasks requested by the users. In this specific paper, the comparison of task scheduling using metaheuristic algorithms like Particle Swarm Optimization (PSO), Multiswarm PSO and Random scheduling is done. They have been compared on the basis of cost. With the correct use of one of these algorithms according to network and its specifications, significant amount of energy can be saved in the network.