Abstract
Cloud computing plays an important role in the improvement of the Information Technology (IT) industry. However, the cloud Quality of service (QoS) level is considered the biggest challenge facing cloud providers and a major concern for enterprises today. That is why, resource allocation is the optimum solution towards this end, which could be done by the best use of task scheduling techniques. In this paper, we provide a literature analysis of the resource allocation in Cloud computing. As well, we propose a classification of tasks scheduling approaches used in the cloud. Furthermore, our work helps researchers develop existing approaches or suggest a new method in this area.
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References
Abdullahi, M., Ngadi, M.A., et al.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener. Comput. Syst. 56, 640–650 (2016)
Achar, R., Thilagam, P.S., Shwetha, D., Pooja, H., et al.: Optimal scheduling of computational task in cloud using virtual machine tree. In: 2012 Third International Conference on Emerging Applications of Information Technology (EAIT), 30 November–01 December 2012, Kolkata, India, pp. 143–146. IEEE (2012)
Alkhanak, E.N., Lee, S.P., Rezaei, R., Parizi, R.M.: Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J. Syst. Softw. 113, 1–26 (2016)
Bessai, K., Youcef, S., Oulamara, A., Godart, C., Nurcan, S.: Bi-criteria workflow tasks allocation and scheduling in cloud computing environments. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), November 2012, Chicago, IL, USA, pp. 638–645. IEEE (2012)
Bey, K.B., Benhammadi, F., Boudaren, M.E.Y., Khamadja, S.: Load balancing heuristic for tasks scheduling in cloud environment. In: Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, 26–29 April 2017, Porto, Portugal, pp. 489–495. INSTICC, SciTePress (2017)
Calheiros, R.N., Buyya, R.: Meeting deadlines of scientific workflows in public clouds with tasks replication. IEEE Trans. Parallel Distrib. Syst. 25(7), 1787–1796 (2014)
Chaisiri, S., Lee, B.S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: 2009 IEEE Asia-Pacific Services Computing Conference, APSCC 2009, December 2009, Biopolis, Singapore, pp. 103–110. IEEE (2009)
Challita, S., Paraiso, F., Merle, P.: A study of virtual machine placement optimization in data centers. In: 7th International Conference on Cloud Computing and Services Science, CLOSER 2017, April 2017, Porto, Portugal (2017). https://hal.inria.fr/hal-01481631
Delavar, A.G., Aryan, Y.: HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Cluster Comput. 17(1), 129–137 (2014)
Ghribi, C., Hadji, M., Zeghlache, D.: Energy efficient VM scheduling for cloud data centers: exact allocation and migration algorithms. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 671–678. IEEE (2013)
Gupta, A., Garg, R.: Load balancing based task scheduling with ACO in cloud computing. In: 2017 International Conference on Computer and Applications (ICCA), 6–7 September 2017, Doha, United Arab Emirates, pp. 174–179. IEEE (2017)
Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16(3), 275–295 (2015)
Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)
Kong, Y., Zhang, M., Ye, D.: A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl. Based Syst. 115, 123–132 (2017)
Kumar, D., Raza, Z.: A PSO based VM resource scheduling model for cloud computing. In: 2015 IEEE International Conference on Computational Intelligence and Communication Technology (CICT), October 2015, Liverpool, UK, pp. 213–219. IEEE (2015)
Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: 2011 Sixth Annual Chinagrid Conference (ChinaGrid), August 2011, Dalian, Liaoning, China, pp. 3–9. IEEE (2011)
Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y., et al.: Resource scheduling for infrastructure as a service (IaaS) in cloud computing: challenges and opportunities. J. Netw. Comput. Appl. 68, 173–200 (2016)
Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016)
Mhedheb, Y., Jrad, F., Tao, J., Zhao, J., Kołodziej, J., Streit, A.: Load and thermal-aware VM scheduling on the cloud. In: International Conference on Algorithms and Architectures for Parallel Processing, October 2013, Liverpool, UK, pp. 101–114. Springer (2013)
Mirzayi, S., Rafe, V.: A hybrid heuristic workflow scheduling algorithm for cloud computing environments. J. Exp. Theor. Artif. Intell. 27(6), 721–735 (2015)
Nan, X., He, Y., Guan, L.: Optimization of workload scheduling for multimedia cloud computing. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2872–2875. IEEE (2013)
Panda, S.K., Gupta, I., Jana, P.K.: Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Inf. Syst. Front., 1–19 (2017)
Portaluri, G., Giordano, S.: Multi objective virtual machine allocation in cloud data centers. 2016 5th IEEE International Conference on Cloud Networking (Cloudnet), October 2016, Pisa, Italy, pp. 107–112. IEEE (2016)
Salot, P.: A survey of various scheduling algorithm in cloud computing environment. Int. J. Res. Eng. Technol. 2(2), 131–135 (2013)
Sandhu, R., Sood, S.K.: Scheduling of big data applications on distributed cloud based on QoS parameters. Cluster Comput. 18(2), 817–828 (2015)
Tsai, C.W., Huang, W.C., Chiang, M.H., Chiang, M.C., Yang, C.S.: A hyper-heuristic scheduling algorithm for cloud. IEEE Trans. Cloud Comput. 2(2), 236–250 (2014)
Wang, W., Zeng, G., Tang, D., Yao, J.: Cloud-DLS: dynamic trusted scheduling for cloud computing. Expert Syst. Appl. 39(3), 2321–2329 (2012)
Zhan, Z.H., Liu, X.F., Gong, Y.J., Zhang, J., Chung, H.S.H., Li, Y.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. (CSUR) 47(4), 63 (2015)
Zhang, F., Cao, J., Tan, W., Khan, S.U., Li, K., Zomaya, A.Y.: Evolutionary scheduling of dynamic multitasking workloads for big-data analytics in elastic cloud. IEEE Trans. Emerg. Top. Comput. 2(3), 338–351 (2014)
Zhong-wen, G., Kai, Z.: The research on cloud computing resource scheduling method based on time-cost-trust model. In: 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT), December 2012, Changchun, China, pp. 939–942. IEEE (2012)
Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016)
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Belgacem, A., Beghdad-Bey, K., Nacer, H. (2019). Task Scheduling in Cloud Computing Environment: A Comprehensive Analysis. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_3
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DOI: https://doi.org/10.1007/978-3-319-98352-3_3
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