PRIORITY-AWARE JOB SCHEDULING ALGORITHM IN CLOUD COMPUTING: A MULTI-CRITERIA APPROACH

Details

Hits: 2469

Volume 2 (1), June 2019, Pages 29-38

Shamsollah Ghanbari

Islamic Azad University, Ashtian Branch, Ashtian, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Job scheduling is one of the most problematic theoretical issues in the area of cloud computing. The existing scheduling methods attempt to consider only a few criteria of scheduling without covering other sufficient criteria. Since, cloud computing faces a large scale resource for allocating to a large number of jobs, due to optimizing the users’ requirements; therefore, a suitable cloud-based job scheduling method must satisfy a wide range of criteria. Besides, in cloud computing, the jobs come with different priorities. Thus, in the cloud environment, a suitable job scheduling algorithm should be able to combine several priorities. This paper proposes a new multi-criteria priority-aware job scheduling algorithm in cloud computing. Experimental results indicate that the proposed method is able to consider different criteria for scheduling.

Keywords:

cloud computing, multi-criteria, priority-aware job scheduling.

DOI: https://doi.org/10.32010/26166127.2019.2.1.29.38

Reference 

[1] Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008). Cloud computing and grid computing 360-degree compared.arXiv preprint arXiv:0901.0131.

[2] Cobham, A. (1954). Priority assignment in waiting line problems.Journal of the Operations Research Society of America, 2(1), 70-76.

[3] Phipps Jr, T. E. (1956). Machine repair as a priority waiting-line problem.Operations Research, 4(1), 76-85.

[4] Kleinrock, L. (1964). Analysis of A time‐shared processor. Naval research logistics quarterly, 11(1), 59-73.

[5] Coffman Jr, E. G., & Kleinrock, L. (1968, April). Computer scheduling methods and their countermeasures. In Proceedings of the April 30--May 2, 1968, spring joint computer conference(pp. 11-21). ACM.

[6] Lee, Y. H., Leu, S., & Chang, R. S. (2011). Improving job scheduling algorithms in a grid environment. Future generation computer systems, 27(8), 991-998.

[7] Lee, M. C., Lin, J. C., & Yahyapour, R. (2015). Hybrid job-driven scheduling for virtual mapreduce clusters.IEEE Transactions on Parallel and Distributed Systems, 27(6), 1687-1699.

[8] Hwang, K., Dongarra, J., & Fox, G. C. (2018). Cloud Computing and Distributed Systems: From Parallel Processing to the Internet of Things. Morgan Kaufmann.

[9] Marozzo, F., Carretero Pérez, J., Duro, R., García Blas, J., Talia, D., & Trunfio, P. (2016). A data-aware scheduling strategy for dmcf workflows over hercules.

[10] Baranowski, M., Bubak, M., & Belloum, A. (2015, July). Data and process abstractions for cloud computing. In2015 International Conference on High Performance Computing & Simulation (HPCS). (pp. 646-649). IEEE.

[11] Aghababaeipour, A., & Ghanbari, S. (2018, February). A New Adaptive Energy-Aware Job Scheduling in Cloud Computing. In International Conference on Soft Computing and Data Mining(pp. 308-317). Springer, Cham.

[12] Ghanbari, S., & Othman, M. (2018). Time Cheating in Divisible Load Scheduling: Sensitivity Analysis, Results and Open Problems.Procedia Computer Science, 125, 935-943.

[13] Singh, S., & Chana, I. (2015). QRSF: QoS-aware resource scheduling framework in cloud computing. The Journal of Supercomputing, 71(1), 241-292.

[14] Suresh, S., Huang, H., & Kim, H. J. (2015). Scheduling in compute cloud with multiple data banks using divisible load paradigm. IEEE Transactions on Aerospace and Electronic Systems, 51(2), 1288-1297.

[15] Ghanbari, S., & Othman, M. (2012). A priority based job scheduling algorithm in cloud computing.Procedia Engineering, 50(0), 778-785.

[16] Kong, X., Lin, C., Jiang, Y., Yan, W., & Chu, X. (2011). Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction.Journal of network and Computer Applications, 34(4), 1068-1077.

[17] Ficco, M., Di Martino, B., Pietrantuono, R., & Russo, S. (2017). Optimized task allocation on private cloud for hybrid simulation of large-scale critical systems.Future Generation Computer Systems, 74, 104-118.

[18] Narman, H. S., Hossain, M. S., Atiquzzaman, M., & Shen, H. (2017). Scheduling internet of things applications in cloud computing.Annals of Telecommunications, 72(1-2), 79-93.

[19] Wang, W., Zeng, G., Tang, D., & Yao, J. (2012). Cloud-DLS: Dynamic trusted scheduling for Cloud computing.Expert Systems with Applications, 39(3), 2321-2329.

[20] Xu, B., Zhao, C., Hu, E., & Hu, B. (2011). Job scheduling algorithm based on Berger model in cloud environment. Advances in Engineering Software, 42(7), 419-425.

[21] Juarez, F., Ejarque, J., & Badia, R. M. (2018). Dynamic energy-aware scheduling for parallel task-based application in cloud computing.Future Generation Computer Systems, 78, 257-271.

[22] Ghanbari, S., Othman, M., Bakar, M. R. A., & Leong, W. J. (2016). Multi-objective method for divisible load scheduling in multi-level tree network.Future Generation Computer Systems, 54, 132-143.

[23] Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process.European journal of operational research, 48(1), 9-26.

[24] Saaty, T. L. (2013). The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach. Operations Research, 61(5), 1101-1118.

[25] Ghanbari, S., Othman, M., Bakar, M. R. A., & Leong, W. J. (2015). Priority-based divisible load scheduling using analytical hierarchy process.Applied Mathematics & Information Sciences, 9(5), 2541.

[26] Strassen, V. (1969). Gaussian elimination is not optimal. Numerische mathematik, 13(4), 354-356.