Optimal task scheduling in cloud computing environment: Meta heuristic approaches

Abstract

Cloud computing is the latest continuation of parallel computing, distributed computing and grid computing. In this system, user can make use of different services like storage, servers and other applications. Cloud resources are not only used by numerous users but are also dynamically redistributed on demand. Requested services are delivered to user's computers and devices through the Internet. The fundamental issue in cloud computing system is related to task scheduling where a scheduler finds an optimal solution in cost-effective manner. Task scheduling issue is mainly focus on to find the best or optimal resources in order to minimize the total processing time of Virtual Machines (VMs). Cloud task scheduling is an NP-hard problem. The focus is on increasing the efficient use of the shared resources. A number of meta-heuristic algorithms have been implemented to solve this issue. In this work three meta-heuristic techniques such as Simulated Annealing, Firefly Algorithm and Cuckoo Search Algorithm have been implemented to find an optimal solution. The main goal of these algorithms is to minimize the overall processing time of the VMs which execute a set of tasks. The experimental result shows that Firefly Algorithm (FFA) performs better than Simulated Annealing and Cuckoo Search Algorithm.