Job Scheduling Within Cloud Environment : A Critical Analysis of Meta Heuristic Approaches

Abstract

Cloud computing becomes the media speak in the time of distributed systems due to its advantage of on demand service, poling of resources over internet, dynamic and scalable nature. Due to its convenience we have to still face the issues like efficient performance in terms of scheduling. The word scheduling refers to schedule the jobs over the cloud so that it can be processed among number of jobs in short span of time as well as in minimal charges. But, we have no algorithms to achieve optimal solution in polynomial times. In cloud computing scheduling belongs to the category of problems known as NP-hard problem just because of its large solution space which causes long time to achieve an optimal solution. In cloud computing it’s a big task to achieve optimal solution but in other vein we can achieve sub optimal solution by using Multiheuristic based techniques. These techniques can provide near optimal solutions within short span of time to resolve such problems. In this research paper, we review number of research papers and comparative its analysis for various scheduling algorithms. Multiheuristic algorithms are: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and two novel techniques: BAT algorithm League Championship Algorithm (LCA).