Modified Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing Systems

Abstract

Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as commercial implementation of such computer science concepts. One among the day-to-day challenges in cloud computing environment is task scheduling (TS). TS is the process of allocating cloudlets to virtual machines (VM) in a cloud architecture with a concern of effective load balance and efficient utilization of resources. With the aim of facing challenges in cloud task scheduling, many non-deterministic polynomial time-hard optimization problem-solving techniques and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on modified ant colony optimization (MACO) algorithm. Main contribution of recommended scheme is to minimize makespan and to perform multi-objective task scheduling (MOTS) process. MACO algorithm will improve performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than basic ACO algorithm.