Presenting an Optimal Algorithm for Resource Scheduling and Code Partition in Mobile Cloud Computing

Document Type : Original Article

Authors

1 Computer,engineering,islamic azad unit zahedshahr,iran

2 Academic member of Azad University of Zahedashehr Branch

Abstract

Through virtualization technology, current cloud data centers are becoming more flexible and secure, and are allocated on demand. A key technology playing an important role in cloud data centers is the resource scheduling program. In this paper, a near-optimal strategy is proposed to solve the problems in this field, by using an evolutionary particle swarm algorithm to reduce the range of multiple targets to a proper level. The placement method based on the particle swarm optimization algorithm can act as real-time placement, due to the increase in computational capability of processors over the past five years. This placement is a searching method in which competencies are dynamically altered based on the variance of fitness values ​​in each generation. This migration and placement approach also minimizes the completion time for virtual machines. In order to assess the proposed method, the results were analyzed and compared through various qualitative criteria, from different aspects and based on changes in different functioning parameters. The performance of the proposed method was compared with other approaches in this field and reflects the high quality of the proposed method.

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Main Subjects


Bellavista, P., Montanari, R., & Das, S. K. (2013). Mobile social networking middleware: A survey. Pervasive and Mobile Computing, 9(4), 437-453. DOI:10.1007/s11276-013-0677-7
Chang, R. S., Chang, J. S., & Lin, P. S. (2009). An ant algorithm for balanced job scheduling in grids. Future Generation Computer Systems, 25(1), 20-27. DOI:10.1109/APSCC.2007.41
Deshpande, U., You, Y., Chan, D., Bila, N., & Gopalan, K. (2014, June). Fast server deprovisioning through scatter-gather live migration of virtual machines. In 2014 IEEE 7th International Conference on Cloud Computing (pp. 376-383). IEEE. DOI:10.1109/CLOUD.2014.58
Esmaili, Mohammad, Mirzaei, Abbas, 2015, Scheduling Tasks in Cloud Computing Using Enhanced Particle Swarm Optimization Algorithm, International Conference on New Research Findings in Electrical Engineering and Computer Science, in persian. DOI:10.1016/j.compag.2019.04.041
Galloway, M., Loewen, G., & Vrbsky, S. (2015, June). Performance metrics of virtual machine live migration. In 2015 IEEE 8th International Conference on Cloud Computing (pp. 637-644). IEEE. DOI:10.1109/CLOUD.2015.90
Garg, S. K., Yeo, C. S., Anandasivam, A., & Buyya, R. (2009). Energy-efficient scheduling of HPC applications in cloud computing environments. arXiv preprint arXiv:0909.1146. Doi: 10.48550/arXiv.0909.1146
Gharooni-fard, G., Moein-darbari, F., Deldari, H., & Morvaridi, A. (2010). Scheduling of scientific workflows using a chaos-genetic algorithm. Procedia Computer Science, 1(1), 1445-1454. DOI:10.1016/j.procs.2010.04.160
Huang, D., Yang, D., Zhang, H., & Wu, L. (2012, December). Energy-aware virtual machine placement in data centers. In 2012 IEEE Global Communications Conference (GLOBECOM) (pp. 3243-3249). IEEE. DOI:10.1109/GLOCOM.2012.6503614
Kamel-Tabakh Farizani, Seyyed Reza, Hashemi, Seyyed Tayebeh, 2016, Algorithmic Load Balance Algorithm in Cloud Computing Inspired by Bee Behavior - International Conference on Modern Research in Engineering Sciences, in persian. DOI:10.25046/aj060299
Kumar, P., & Verma, A. (2012, August). Scheduling using improved genetic algorithm in cloud computing for independent tasks. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (pp. 137-142). ACM.  DOI:10.1145/2345396.2345420
Latiff, M. S. A., Abdul-Salaam, G., & Madni, S. H. H. (2016). Secure scientific applications scheduling technique for cloud computing environment using global league championship algorithm. PloS one, 11(7), e0158102. DOI:10.1371/journal.pone.0158102
Li, J., Peng, J., Lei, Z., & Zhang, W. (2011). An energy-efficient scheduling approach based on private clouds. Journal of Information &computational Science, 8(4), 716-724. DOI:10.4156/jcit.vol6.issue7.1
Lin, C. C., Jian, Z. D., & Hsu, C. H. (2014, December). A strategy of service quality optimization for live virtual machine migration. In 2014 IEEE 17th International Conference on Computational Science and Engineering (pp. 1308-1313). IEEE. DOI:10.1007/978-3-319-10509-3_7
Liu, Y., Shao, H., Jing, W., & Qiu, Z. (2015). Multi-DAGs scheduling integrating with security and availability in cloud environment. Chinese Journal of Electronics, 24(4), 709-716. DOI:10.1080/00207543.2018.1449978
Oprescu, A. M., & Kielmann, T. (2010, November). Bag-of-tasks scheduling under budget constraints. In 2010 IEEE Second International Conference on Cloud Computing Technology and Science (pp. 351-359). IEEE. DOI:10.1109/CloudCom.2010.32
Ramachandran, M., & Chang, V. (2016). Towards performance evaluation of cloud service providers for cloud data security. International Journal of Information Management, 36(4), 618-625. DOI:10.1016/j.ijinfomgt.2016.03.005
Sundar, S., & Liang, B. (2016, April). Communication augmented latest possible scheduling for cloud computing with delay constraint and task dependency. In 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1009-1014). IEEE. DOI:10.1109/INFOCOM.2018.8486305
Tawfeek, M. A., El-Sisi, A., Keshk, A. E., & Torkey, F. A. (2013, November). Cloud task scheduling based on ant colony optimization. In 2013 8th international conference on computer engineering & systems (ICCES) (pp. 64-69). IEEE. DOI:10.1109/ICCES.2013.6707172
Ungurean, I. (2010). Job scheduling algorithm based on dynamic management of resources provided by grid computing systems. Elektronika ir Elektrotechnika, 103(7), 57-60. DOI:10.1016/j.future.2012.12.012
Van den Bossche, R., Vanmechelen, K., & Broeckhove, J. (2013). Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds. Future Generation Computer Systems, 29(4), 973-985. DOI:10.1016/j.future.2012.12.012
Zhang, F., Chen, J., Chen, H., & Zang, B. (2011, October). CloudVisor: retrofitting protection of virtual machines in multi-tenant cloud with nested virtualization. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles (pp. 203-216). ACM.  Doi:  10.1145/2043556.2043576
Zhang, J., Ren, F., Shu, R., Huang, T., & Liu, Y. (2015). Guaranteeing delay of live virtual machine migration by determining and provisioning appropriate bandwidth. IEEE Transactions on Computers, 65(9), 2910-2917. DOI:10.1504/IJKEDM.2018.094743
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