Abstract
Scheduling of cloudlets (tasks) on virtual machines in cloud has always been of prime concern. Various heuristics have already been proposed in this area of research and are well documented. In this work, authors have proposed a unique method of statistically evaluating the results of simulation of these heuristics for cloud-based model. The results are evaluated for a standard set of performance metrics. The statistical method applied proves the reliability of simulation results obtained and can be applied to evaluation of all heuristics. In addition to this a recent and more advanced CloudSim Plus simulation tool is used as there is paucity of work that demonstrates using this tool for this research problem. The simulations use a standard model of task and machine heterogeneity that is pertinent to cloud computing. To make the simulation environment more realistic, Poisson distribution is used for the arrival of cloudlets, and exponential distribution for length (size) of cloudlets (tasks).
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References
Foster, I., Kesselman, C. (eds.): The Grid 2: Blueprint for a New Computing Infrastructure. Elsevier, San Francisco (2003)
Mell, P., Grance, T.: The NIST definition of cloud computing (2011)
Chaczko, Z., et al.: Availability and load balancing in cloud computing. In: International Conference on Computer and Software Modeling, Singapore, vol. 14 (2011)
Thakur, A., Goraya, M.S.: A taxonomic survey on load balancing in cloud. J. Netw. Comput. Appl. 98, 43–57 (2017)
Kaur, R., Luthra, P.: Load balancing in cloud computing. In: Proceedings of International Conference on Recent Trends in Information, Telecommunication and Computing, ITC 2012 (2012)
Kunwar, V., Agarwal, N., Rana, A., Pandey, J.P.: Load balancing in cloud—a systematic review. In: Aggarwal, V.B., Bhatnagar, V., Mishra, D.K. (eds.) Big Data Analytics. AISC, vol. 654, pp. 583–593. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6620-7_56
Khiyaita, A., et al.: Load balancing cloud computing: state of art. In: 2012 National Days of Network Security and Systems (JNS2). IEEE (2012)
Mayanka, K., Mishra, A.: A comparative study of load balancing algorithms in cloud computing environment. arXiv preprint: arXiv:1403.6918 (2014)
Mishra, S.K., Sahoo, B., Parida, P.P.: Load balancing in cloud computing: a big picture. J. King Saud Univ. Comput. Inf. Sci. (2018)
Phi, N., et al.: Proposed load balancing algorithm to reduce response time and processing time on cloud computing. Int. J. Comput. Netw. Commun. (IJCNC) 10(3), 87–98 (2018)
Maipan-uku, J.Y., Rabiu, I., Mishra, A.: Immediate/batch mode scheduling algorithms for grid computing: a review
Maheswaran, M., et al.: Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems. In: Proceedings of the Eighth Heterogeneous Computing Workshop (HCW 1999). IEEE (1999)
Calheiros, R.N., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
Silva Filho, M.C., et al.: CloudSim plus: a cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE (2017)
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Narang, S., Goswami, P., Jain, A. (2019). Statistical Analysis of Cloud Based Scheduling Heuristics. In: Gani, A., Das, P., Kharb, L., Chahal, D. (eds) Information, Communication and Computing Technology. ICICCT 2019. Communications in Computer and Information Science, vol 1025. Springer, Singapore. https://doi.org/10.1007/978-981-15-1384-8_9
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DOI: https://doi.org/10.1007/978-981-15-1384-8_9
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