Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

Keywords: optimization, ions motion, cloud, job scheduling

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.

Downloads

Download data is not yet available.

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

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

P. Mell, T. Grance, The NIST definition of cloud computing, National Institute of Standards and Technology, 2011

A. T. Velte, T. J. Velte, R. Elsenpeter, Cloud computing: A practical approach, McGraw-Hill, 2009

B. Furht, Cloud computing fundamentals, Springer, 2010

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

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

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

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

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

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

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

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

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

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

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

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

C. Li, C. Wang, Y. Luo, “An efficient scheduling optimization strategy for improving consistency maintenance in edge cloud environment”, The Journal of Supercomputing, available at: https://doi.org/10.1007/s11227-019-03133-9, 2020

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

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

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

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

M. Aruna, D. Bhanu, S. Karthik, “An improved load balanced metaheuristic scheduling in cloud”, Cluster Computing, Vol. 22, pp. 10873-10881, 2019

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

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

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

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

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

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

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

E. Aloboud, H. Kurdi, “Cuckoo-inspired job scheduling algorithm for cloud computing”, Procedia Computer Science, Vol. 151, pp. 1078-1083, 2019

B. Javidy, A. Hatamlou, S. Mirjalili, “Ions motion algorithm for solving optimization problems”, Applied Soft Computing, Vol. 32, pp. 72-79, 2015

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

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

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

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

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

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

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

Metrics

Abstract Views: 180
PDF Downloads: 60

Metrics Information
Bookmark and Share