A new approach for scheduling jobs in cloud computing environment

Abstract. Job dscheduling in cloud computing environment is one of the most important issues that must be considered by cloud computing service providers. Optimal job scheduling enables more efficient utilization of resources, which in turn leads to more customers satisfaction. Solution procedures to the problem of job scheduling in cloud computing environment have mainly focused on optimizing one quality criterion. In this paper, we propose a static solution procedure for the scheduling of jobs in cloud computing environment which is based on particle swarm optimization technique (PSO). Considering the virtual machine capabilities and having secured an appropriate method for request assignments, this solution procedure not only reduces the amount of memory needed, but minimizes the maximum job's makespan. The simulation results show that our proposed method reduces the maximum job makespan by a larger amount when compared to the other PSO based methods.

Keywords:

Processing in cloud environment, scheduling Particle swarm optimization, Computational complexity,

PDF

___

Hu, J., Gu, J., Sun, G., & Zhao, T. (2010, December). A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In Parallel Architectures, Algorithms and Programming (PAAP), 2010 Third International Symposium on (pp. 89-96). IEEE.
Wang, S., & Meng, B. (2007). Resource allocation and scheduling problem based on genetic algorithm and ant colony optimization. In Advances in Knowledge Discovery and Data Mining (pp. 879-886). Springer Berlin Heidelberg.
Abdullah, M., & Othman, M. (2014). Simulated Annealing approach to Cost-Based Multi-Quality of service job scheduling in cloud computing enviroment. American Journal of Applied Sciences, 11(6), 872-877.
Krishnasamy, K. (2013). Task scheduling algorithm based on hybrid partical swarm optimization in cloud computing environment. Journal of Theoretical & Applied Information Technology, 54(1).
Cao, Q., Wei, Z. B., & Gong, W. M. (2009, June). An optimized algorithm for task scheduling based on activity based costing in cloud computing. In Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on (pp. 1-3). IEEE.
Nan, X., He, Y., & Guan, L. (2013, May). Optimal resource allocation for multimedia application providers in multi-site cloud. In Circuits and Systems (ISCAS), 2013 IEEE International Symposium on (pp. 449-452). IEEE.
Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010, April). A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on (pp. 400-407). IEEE.
Li, K., Xu, G., Zhao, G., Dong, Y., & Wang, D. (2011, August). Cloud task scheduling based on load balancing ant colony optimization. In Chinagrid Conference (ChinaGrid), 2011 Sixth Annual (pp. 3-9). IEEE.
Zhu, L., Li, Q., & He, L. (2012). Study on cloud computing resource scheduling strategy based on the ant colony optimization algorithm. IJCSI International Journal of Computer Science Issues, 9(5), 54-58.
Eberhart, R. C., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science (Vol. 1, pp. 39-43).
Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. Evolutionary Computation, IEEE Transactions on, 6(1), 58-73.
Dréo, J. (Ed.). (2006). Metaheuristics for hard optimization: methods and case studies. Springer Science & Business Media. [13]
Chiu, C. F., Hsu, S. J., Jan, S. R., & Chen, J. A. (2014). Task Scheduling Based on Load Approximation in Cloud Computing Environment. In Future Information Technology (pp. 803-808). Springer Berlin Heidelberg.
Lin, W., Liang, C., Wang, J. Z., & Buyya, R. (2014). Bandwidth-aware divisible task scheduling for cloud computing. Software: Practice and Experience, 44(2), 163-174.
Rahman, M., Hassan, R., Ranjan, R., & Buyya, R. (2013). Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency and Computation: Practice and Experience, 25(13), 1816-1842.
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23-50.