An Overview of Different Job Scheduling Heuristics Strategies for Cloud Computing Environment

Cloud Computing, the long-held vision of computing as a utility, has the prospective to renovate a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is premeditated and purchased. Developers with novel ideas for new Internet services no longer need the large capital outlays in hardware to install their service or the human expense to control it. They require not be concerned about overprovisioning for a service whose popularity does not gather their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly admired, thus missing prospective customers and returns. Job scheduling system problem is a nucleus and demanding issue in Cloud Computing. How to utilize Cloud Computing resources proficiently and gain the maximum profits with job scheduling system is one of the Cloud computing service providers’ ultimate objectives. This paper discusses the QoS requirements of Cloud computing resources users’ jobs and different strategies to gain the maximum profits by offering Cloud computing resources. In Cloud computing, users with dissimilar service requirements often need to bargain with service provider via Service Level Agreement (SLA). The unique pay-as-you-go billing approach in Cloud computing challenges resource provisioning for service providers. The cloud computational environment is very suit to meet the computational burdens of large, diverse groups of jobs. And the job scheduling problem in it has been a research hotspot in recent years. Some heuristic techniques have been used to optimize it However, selecting the finest one to use in a given environment remains a tricky problem, because comparisons are often clouded by different underlying assumptions in the original study of each algorithm. Some comparisons have been made to them, but some new algorithms are not included in the comparisons. So, in this paper, four popular researched algorithms are discussed. The four heuristics are Genetic Algorithm (GA), Ant Colony Optimization Algorithm (ACO), Particle Swarm Optimization (PCO) Algorithm and Simulated Annealing Algorithm (SA).