Cloudy GSA for load scheduling in cloud computing

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.

[1]  Ali R. Yildiz,et al.  Structural design of vehicle components using gravitational search and charged system search algorithms , 2015 .

[2]  Hossein Nezamabadi-pour,et al.  Filter modeling using gravitational search algorithm , 2011, Eng. Appl. Artif. Intell..

[3]  Ali Rıza Yıldız,et al.  Optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm , 2015 .

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Divya Chaudhary,et al.  Analytical study of load scheduling algorithms in cloud computing , 2014, 2014 International Conference on Parallel, Distributed and Grid Computing.

[6]  Rajkumar Buyya,et al.  Multi-Objective Problem Solving With Offspring on Enterprise Clouds , 2009, ArXiv.

[7]  Yue Li,et al.  Herd Clustering: A synergistic data clustering approach using collective intelligence , 2014, Appl. Soft Comput..

[8]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[9]  A. Abraham,et al.  Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm , 2010, Future Gener. Comput. Syst..

[10]  Ali Rıza Yıldız,et al.  A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects , 2017 .

[11]  Hirotaka Yoshida,et al.  A PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER AND VOLTAGE CONTROL CONSIDERING VOLTAGE STABILITY , 2000 .

[12]  S. N. Sivanandam,et al.  GRID SCHEDULING USING ENHANCED PSO ALGORITHM , 2010 .

[13]  Mehmet Fatih Tasgetiren,et al.  A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem , 2007, Eur. J. Oper. Res..

[14]  Hong He,et al.  A novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems , 2011, Microprocess. Microsystems.

[15]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[16]  Yuehui Chen,et al.  A Task Scheduling Algorithm Based on PSO for Grid Computing , 2008 .

[17]  Joel J. P. C. Rodrigues,et al.  Metaheuristic Scheduling for Cloud: A Survey , 2014, IEEE Systems Journal.

[18]  A. Khiyaita,et al.  Load balancing cloud computing: State of art , 2012, 2012 National Days of Network Security and Systems.

[19]  Cristian Mateos,et al.  Distributed job scheduling based on Swarm Intelligence: A survey , 2014, Comput. Electr. Eng..

[20]  Ali R. Yildiz,et al.  Comparison of evolutionary-based optimization algorithms for structural design optimization , 2013, Eng. Appl. Artif. Intell..

[21]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[22]  Rajkumar Buyya,et al.  Cloudbus Toolkit for Market-Oriented Cloud Computing , 2009, CloudCom.

[23]  Rajkumar Buyya,et al.  Workflow scheduling algorithms for grid computing , 2008 .

[24]  Rajender Singh Chhillar,et al.  A New Load Balancing Technique for Virtual Machine Cloud Computing Environment , 2013 .

[25]  Divya Chaudhary,et al.  An analysis of the load scheduling algorithms in the cloud computing environment: A survey , 2014, 2014 9th International Conference on Industrial and Information Systems (ICIIS).

[26]  Zahid Raza,et al.  A PSO Based VM Resource Scheduling Model for Cloud Computing , 2015, 2015 IEEE International Conference on Computational Intelligence & Communication Technology.

[27]  Morteza Kiani,et al.  A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and NVH Optimization , 2016 .