دانلود رایگان مقاله انگلیسی GSA ابری برای زمانبندی بار در محاسبات ابری - الزویر 2018

Abstract

Scheduling of load and data plays an important role in the efficient utilization of the resources from one cloudlet to another cloudlet in the cloud computing environment. Cloud computing is an incremental paradigm to brighten the world with its great vision of providing the power of distributed computing through virtual approach. Resource allocation plays an important role in the optimal handling of the load scheduling problem using static and meta-heuristic approaches. The Gravitational Search Algorithm (GSA) is a nature-inspired meta-heuristic optimization technique which is used for solving the load scheduling problem in the cloud computing environment and is based on Newton’s gravitational law dealing with gravity. This paper proposes a near optimal load scheduling algorithm named Cloudy-GSA to minimize the transfer time and the total cost incurred in scheduling the cloudlets to the VMs. These are achieved by increased exploitation of VMs using the particles based on fitness values. The Cloudy-GSA algorithm is implemented on the CloudSim and has been compared with the existing popular algorithms. The results of the algorithm are converged and statistically analysed over a set of iterations. As evident from the results, the proposed Cloudy-GSA algorithm minimizes the transfer time and the total cost for scheduling the load than the existing algorithms.

Conclusion And Future Work

This paper elaborated the major problem of load scheduling in the cloud computing environment between cloudlets and VMs. The meta-heuristic based swarm intelligence technique for load scheduling in the cloud is discussed. The concepts of load scheduling algorithms with proposed Cloudy-GSA on load scheduling has been explained. The proposed Cloudy-GSA approach reduces the transfer time and total cost of the system along with maximum utilization of VMs. This has been achieved using the fitness values of particles and force acting on the particles in search space. At a bigger context, proposed approach provides higher cost optimization than the existing Segmented Min-Min, Tabu Search, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, FCFS, Min-Min and Particle Swarm Optimization scheduling algorithms. The results of the proposed approach have been compared with the existing discussed scheduling algorithms which are heuristic and non-heuristic in nature in a detailed manner on a large set of iterations. Thus, the proposed CloudyGSA optimization method generates far better results in terms of transfer time and total cost of execution time based on the convergence statistical analysis. The future work aims to minimize the total cost by using improved fitness function considering other parameters for better minimized results in the cloud computing environment, based on swarm intelligence to further reduce the total cost.