Statistical Analysis of Cloud Based Scheduling Heuristics

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).

[1]  Mário M. Freire,et al.  CloudSim Plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[2]  Tran Cong Hung,et al.  Proposed Load Balancing Algorithm to Reduce Response Time and Processing Time on Cloud Computing , 2018, International journal of Computer Networks & Communications.

[3]  Atul Mishra,et al.  A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment , 2014, ArXiv.

[4]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[5]  J. Y. Maipan-uku,et al.  IMMEDIATE/BATCH MODE SCHEDULING ALGORITHMS FOR GRID COMPUTING: A REVIEW , 2017 .

[6]  Zenon Chaczko,et al.  Availability and Load Balancing in Cloud Computing , 2011 .

[7]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[8]  Avnish Thakur,et al.  A taxonomic survey on load balancing in cloud , 2017, J. Netw. Comput. Appl..

[9]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[10]  A. Khiyaita,et al.  Load balancing cloud computing: State of art , 2012, 2012 National Days of Network Security and Systems.

[11]  R. F. Freund,et al.  Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

[12]  Ajay Rana,et al.  Load Balancing in Cloud—A Systematic Review , 2018 .

[13]  Rajwinder Kaur,et al.  Load Balancing in Cloud Computing , 2014 .

[14]  Bibhudatta Sahoo,et al.  Load balancing in cloud computing: A big picture , 2018, J. King Saud Univ. Comput. Inf. Sci..