Cloud Job Scheduling with Ions Motion Optimization Algorithm
Abstract
Cloud computing technology success comes from its manner of delivering information technology services, how they are designed, propagated, maintained and scaled. Job Scheduling on cloud computing is a crucial research area and is known to be an NP-complete problem. Scheduling refers to assigning user requests to underlying resources effectively. This paper proposes a new Job Scheduling mechanism for cloud computing environment. The proposed mechanism is based on the Ions Motion Optimization (IMO) algorithm. IMO has two phases, liquid, and crystal. These two phases balance the algorithm behavior between convergence and local optima avoidance. To evaluate the proposed mechanism, a simulation with different scenarios using the CloudSim simulator is conducted. The performance of the proposed algorithm is compared with two metaheuristic algorithms known as Cat Swarm Optimization (CSO) and Glowworm Swarm Optimization (GSO). Furthermore, the proposed IMO mechanism is compared with First Come First Served and random solution. The experimental results demonstrated that the proposed mechanism outperformed both CSO and GSO and produced the shortest execution time in all experimental scenarios.
Keywords:
optimization, ions motion, cloud, job schedulingDownloads
References
B. K. Rani, B. P. Rani, A. V. Babu, “Cloud computing and inter-clouds–types, topologies and research issues”, Procedia Computer Science, Vol. 50, pp. 24-29, 2015 DOI: https://doi.org/10.1016/j.procs.2015.04.006
T. Erl, R. Puttini, Z. Mahmood, Cloud computing: Concepts, technology and architecture, Prentice Hall, 2013
T. Mathew, K. C. Sekaran, J. Jose, “Study and analysis of various task scheduling algorithms in the cloud computing environment”, International Conference on Advances in Computing, Communications and Informatics, New Delhi, India, September 24-27, 2014 DOI: https://doi.org/10.1109/ICACCI.2014.6968517
P. Mell, T. Grance, The NIST definition of cloud computing, National Institute of Standards and Technology, 2011 DOI: https://doi.org/10.6028/NIST.SP.800-145
A. T. Velte, T. J. Velte, R. Elsenpeter, Cloud computing: A practical approach, McGraw-Hill, 2009
B. Furht, Cloud computing fundamentals, Springer, 2010 DOI: https://doi.org/10.1007/978-1-4419-6524-0_1
S. F. Issawi, A. A. Halees, M. Radi, “An efficient adaptive load balancing algorithm for cloud computing under Bursty workloads”, Engineering, Technology & Applied Science Research, Vol. 5, No. 3, pp. 795-800, 2015 DOI: https://doi.org/10.48084/etasr.554
A. Khattara, W. R. C. Khettaf, M. Mostefai, “An efficient metaheuristic approach for the multi-period technician routing and scheduling problem”, Engineering, Technology & Applied Science Research, Vol. 9, No. 5, pp. 4718-4723, 2019 DOI: https://doi.org/10.48084/etasr.3091
A. R. Arunarani, D. Manjula, V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey”, Future Generation Computer Systems, Vol. 91, pp. 407-415, 2019 DOI: https://doi.org/10.1016/j.future.2018.09.014
B. Jana, M. Chakraborty, T. Mandal, “A task scheduling technique based on particle swarm optimization algorithm in cloud environment”, in: Soft Computing: Theories and Applications, Proceedings of SoCTA 2017, pp. 525-536, Springer, 2018 DOI: https://doi.org/10.1007/978-981-13-0589-4_49
M. Haque, R. Islam, M. R. Kabir, F. N. Nur, N. N. Moon, “A priority-based process scheduling algorithm in cloud computing”, in: Emerging Technologies in Data Mining and Information Security, Advances in Intelligent Systems and Computing, Vol. 755, pp. 239-248, Springer, 2018 DOI: https://doi.org/10.1007/978-981-13-1951-8_22
R. Somula, S. Nalluri, M. NallaKaruppan, S. Ashok, G. Kannayaram, “Analysis of CPU scheduling algorithms for cloud computing”, in: Smart Intelligent Computing and Applications, Smart Innovation, Systems and Technologies, Vol 105, pp. 375-382, Springer, 2018 DOI: https://doi.org/10.1007/978-981-13-1927-3_40
O. J. Shirazi, G. Dastghaibyfard, M. M. Raja, “Task scheduling with firefly algorithm in cloud computing”, Science International, Vol. 27, No. 1, pp. 167-172, 2014
Y. Miao, “Resource scheduling simulation design of firefly algorithm based on chaos optimization in cloud computing”, International Journal of Grid Distributed Computing, Vol. 7, No. 6, pp. 221-228, 2014 DOI: https://doi.org/10.14257/ijgdc.2014.7.6.18
M. Aboalama, A. Yousif, “Enhanced job scheduling algorithm for cloud computing using shortest remaining job first”, International Journal of Computer Science & Management Studies, Vol. 15, No. 6, pp. 65-68, 2015
Y. P. Dave, A. S. Shelat, D. S. Patel, R. H. Jhaveri, “Various job scheduling algorithms in cloud computing: A survey”, International Conference on Information Communication and Embedded Systems, Chennai, India, February 27-28, 2014 DOI: https://doi.org/10.1109/ICICES.2014.7033909
D. Oliveira, A. Brinkmann, N. Rosa, P. Maciel, “Performability evaluation and optimization of workflow applications in cloud environments”, Journal of Grid Computing, Vol. 17, pp. 749-770, 2019 DOI: https://doi.org/10.1007/s10723-019-09476-0
L. Zhou, L. Zhang, L. Ren, J. Wang, “Real-time scheduling of cloud manufacturing services based on dynamic data-driven simulation”, IEEE Transactions on Industrial Informatics, Vol. 15, No. 9, pp. 5042-5051, 2019 DOI: https://doi.org/10.1109/TII.2019.2894111
L. Mei, W. K. Chan, T. H. Tse, “A tale of clouds: Paradigm comparisons and some thoughts on research issues”, IEEE Asia-Pacific Services Computing Conference, Yilan, Taiwan, December 9-12, 2008 DOI: https://doi.org/10.1109/APSCC.2008.168
S. Mohanty, S. C. Moharana, H. Das, S. C. Satpathy, “QoS aware group-based workload scheduling in cloud environment”, in: Data Engineering and Communication Technology: Proceedings of 3rd ICDECT-2K19, pp. 953-960, Springer, 2020 DOI: https://doi.org/10.1007/978-981-15-1097-7_81
C. Li, C. Wang, Y. Luo, “An efficient scheduling optimization strategy for improving consistency maintenance in edge cloud environment”, The Journal of Supercomputing, 2020 DOI: https://doi.org/10.1007/s11227-019-03133-9
Z. Tong, H. Chen, X. Deng, K. Li, K. Li, “A scheduling scheme in the cloud computing environment using deep Q-learning”, Information Sciences, Vol. 512, pp. 1170-1191, 2020 DOI: https://doi.org/10.1016/j.ins.2019.10.035
B. Nayak, S. K. Padhi, P. K. Pattnaik, “Optimization of cloud datacenter using heuristic strategic approach”, in: Soft Computing and Signal Processing: Proceedings of ICSCSP 2018, Vol. 1, pp. 91-100, Springer, 2019 DOI: https://doi.org/10.1007/978-981-13-3600-3_9
S. Ijaz, E. U. Munir, “MOPT: List-based heuristic for scheduling workflows in cloud environment”, The Journal of Supercomputing, Vol. 75, pp. 3740-3768, 2019 DOI: https://doi.org/10.1007/s11227-018-2726-6
R. Singh, “Hybrid metaheuristic based scheduling with job duplication for cloud data centers”, in: Harmony Search and Nature Inspired Optimization Algorithms: Theory and Applications, ICHSA 2018, pp. 989-997, Springer, 2018 DOI: https://doi.org/10.1007/978-981-13-0761-4_93
M. Aruna, D. Bhanu, S. Karthik, “An improved load balanced metaheuristic scheduling in cloud”, Cluster Computing, Vol. 22, pp. 10873-10881, 2019 DOI: https://doi.org/10.1007/s10586-017-1213-9
H. Singh, S. Tyagi, P. Kumar, “Scheduling in cloud computing environment using metaheuristic techniques: A survey”, in: Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018, pp. 753-763, Springer, 2019 DOI: https://doi.org/10.1007/978-981-13-7403-6_66
D. I. Esa, A. Yousif, “Glowworm swarm optimization (GSO) for cloud jobs scheduling”, International Journal of Advanced Science and Technology, Vol. 96, pp. 71-82, 2016 DOI: https://doi.org/10.14257/ijast.2016.96.07
D. Gabi, A. S. Ismail, A. Zainal, Z. Zakaria, A. Al-Khasawneh, “Hybrid cat swarm optimization and simulated annealing for dynamic task scheduling on cloud computing environment”, Journal of Information and Communication Technology, Vol. 17, No. 3, pp. 435-467, 2018 DOI: https://doi.org/10.32890/jict2018.17.3.3
D. I. Esa, A. Yousif, “Scheduling jobs on cloud computing using firefly algorithm”, International Journal of Grid and Distributed Computing, Vol. 9, No. 7, pp. 149-158, 2016 DOI: https://doi.org/10.14257/ijgdc.2016.9.7.16
S. Sotiriadis, N. Bessis, A. Anjum, R. Buyya, “An Inter-Cloud Meta-Scheduling (ICMS) simulation framework: Architecture and evaluation”, IEEE Transactions on Services Computing, Vol. 11, No. 1, pp. 5-19, 2018 DOI: https://doi.org/10.1109/TSC.2015.2399312
A. V. Krishna, S. Ramasubbareddy, K. Govinda, “Task scheduling based on hybrid algorithm for cloud computing”, International Conference on Intelligent Computing and Smart Communication, Tehri, India, April 20-21, 2019
A. M. Zain, A. Yousif, “Chemical Reaction Optimization (CRO) for cloud job scheduling”, SN Applied Sciences, Vol. 2, Article ID 53, 2020 DOI: https://doi.org/10.1007/s42452-019-1758-8
Y. M. Suliman, A. Yousif, M. B. Bashir, “Shark smell optimization (SSO) algorithm for cloud jobs scheduling”, International Conference on Computing, Riyadh, Saudi Arabia, December 10-12, 2019 DOI: https://doi.org/10.1007/978-3-030-36368-0_7
E. Aloboud, H. Kurdi, “Cuckoo-inspired job scheduling algorithm for cloud computing”, Procedia Computer Science, Vol. 151, pp. 1078-1083, 2019 DOI: https://doi.org/10.1016/j.procs.2019.04.153
B. Javidy, A. Hatamlou, S. Mirjalili, “Ions motion algorithm for solving optimization problems”, Applied Soft Computing, Vol. 32, pp. 72-79, 2015 DOI: https://doi.org/10.1016/j.asoc.2015.03.035
T. T. Nguyen, M. J. Wang, J. S. Pan, T. K. Dao, T. G. Ngo, “A load economic dispatch based on ion motion optimization algorithm”, in: Advances in Intelligent Information Hiding and Multimedia Signal Processing, pp. 115-125, Springer, 2019 DOI: https://doi.org/10.1007/978-981-13-9710-3_12
C. H. Yang, K. C. Wu, Y. S. Lin, L. Y. Chuang, H. W. Chang, “Protein folding prediction in the HP model using ions motion optimization with a greedy algorithm”, BioData Mining, Vol. 11, Article ID 17, 2018 DOI: https://doi.org/10.1186/s13040-018-0176-6
M. Kumar, J. S. Dhillon, “An experimental study of ion motion optimization for constraint economic load dispatch problem”, International Conference on Power Energy, Environment and Intelligent Control, Greater Noida, India, April 13-14, 2018 DOI: https://doi.org/10.1109/PEEIC.2018.8665447
S. Das, A. Bhattacharya, A. K. Chakraborty, “Quasi-reflected ions motion optimization algorithm for short-term hydrothermal scheduling”, Neural Computing and Applications, Vol. 29, pp. 123-149, 2018 DOI: https://doi.org/10.1007/s00521-016-2529-8
G. Kong, Y. Zhang, A. J. M. Khalaf, S. Panahi, I. Hussain, “Parameter estimation in a new chaotic memristive system using ions motion optimization”, The European Physical Journal Special Topics, Vol. 228, pp. 2133-2145, 2019 DOI: https://doi.org/10.1140/epjst/e2019-900023-6
J. S. Pan, T. T. Nguyen, S. C. Chu, T. K. Dao, T. G. Ngo, “A multi-objective ions motion optimization for robot path planning”, International Conference on Engineering Research and Applications, Thai Nguyen, Vietnam, December 1-2, 2019
B. Wang, C. Wang, L. Wang, N. Xie, W. Wei, “Recognition of sEMG hand actions based on cloud adaptive quantum chaos ions motion algorithm optimized SVM”, Journal of Mechanics in Medicine and Biology, Vol. 19, No. 6, Article ID 1950047, 2019 DOI: https://doi.org/10.1142/S0219519419500477
T. T. Nguyen, J. S. Pan, T. Y. Wu, T. K. Dao, T. D. Nguyen, “Node coverage optimization strategy based on ions motion optimization”, Journal of Network Intelligence, Vol. 4, No. 1, pp. 1-9, 2019
Downloads
How to Cite
License
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
