دانلود رایگان مقاله انگلیسی بررسی تکنیک های زمانبندی فوق ابتکاری در محاسبات ابر - الزویر 2015

Abstract

Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a selfservice, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and two novel techniques: League Championship Algorithm (LCA) and BAT algorithm.

11. Conclusion

The paper widely reviews the application of metaheuristic techniques in the area of scheduling in cloud and grid environments. Metaheuristic techniques are usually slower than deterministic algorithms and the generated solutions may not be optimal, thus most of the research done is toward improving the convergence speed and quality of the solution. These issues have been undertaken by modifying the transition operator, preprocessing the input population or taking hybrid approach in metaheuristic techniques.

Moreover different scheduling algorithms have focused on diverse optimization criteria. In the studied literature, most of the authors have focused on reduction of makespan and execution cost whereas others have given significance to response time, throughput, flowtime and average resource utilization. Comparative analysis of algorithms based on each metaheuristic technique mainly compares the technique used for improving metaheuristics, optimization criteria, nature of tasks and the environment in which the algorithm is implemented. The recent research efforts are done in the direction of energy-aware scheduling as data centers have become energy-hungry and a major source of CO2 emissions. The challenge is to reduce energy consumption of data centers without degrading performance and violating SLA constraints. Various open issues are also discussed in the paper which can be taken up for future research.