Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing

Abstract

Cloud computing benchmarks the dream of rendering computing as a utility, providing high agility and reachability from an existing set of technologies. It facilitates a wider dimension to architect and manage remote resources. Cloud technology with exponential growth is drilling towards issues that tend to lower its explored possibilities. As cloud systems by virtue deal with various virtualized resources, scheduling is opted as an important metric for measuring and leveraging performance. But scheduling efficiency is deteriorated by various parameters that pave scope for our research and projects immense need for improvising the overall makespan of the system. The proposed work aims at projecting a greater drift in the first phase by witnessing a sequence of phases like preprocessing the user tasks for improved accuracy, classifying the tasks with respect to resource demand and execution time using the improved density based clustering method (IDCM). The second phase deals with enhanced coot optimization algorithm for task scheduling (ECOA-TS) that proceeds and proves its novelty by adopting Cauchy mutation overcoming the convergence backdrop for generating an optimal mapping between clustered user tasks and VMs. The overall performance of the proposed work overrides by reduced makespan against existing state-of-the-art optimization algorithms like particle swam optimization (PSO), grey wolf optimization (GWO) and whale optimization algorithm (WOA) by 27.41%, 19.8%, and 15.33% respectively.