Load Balancing Based on Statistical Model in Expert Cloud
AbstractExpert Cloud is a new class of Cloud computing which enables the users to achieve their requirements from a collection containing experts and skills created by the human resources (HRs). The acquisition of these skills and experts from this collection is possible by using the Internet and Cloud computing concepts without consideration of the HRs location. The load balancing in cloud computing means equal load distribution among resources, virtual machines (VMs) and servers. The effective load distribution in a heterogeneous environment such as cloud is an important challenge. The increase in the number of users, the differences of request types and also different resources capabilities and capacities cause that some resources become overload and some others become idle. This paper presents a dynamic load balanced task scheduling algorithm in expert cloud. In this method, we utilize the genetic algorithm (GA) as a ranking for making distinction among the HRs capabilities. In our proposed method, we use interval estimation and specification matrix to allocate the HRs and also to determine the service rate. We model the load balancing and mapping process based on Simple Exponential Smoothing and Probability Theory. This statistical load balancing model allows us to allocate the HRs based on service rate and Poisson model. So, each task is delivered to the HR which is capable to execute it. The simulation results show that the expert cloud can reduce the execution and tardiness time and improve HR utilization. The cost of using resources as an effective factor is also observed.
. P. Mell, and T. Grance, "The NIST Definition of Cloud Computing", National Institute of Standards and Technology, pp. 1-7, 2011.
. F. H. Qusay,. "Demystifying Cloud Computing", The Journal of Defense Software Engineering ,pp. 16–21, 2011.
. D. Babu. L.D, and P.V. Krishna, "Honey bee behavior inspired load balancing of tasks in cloud computing environments", Applied Soft Computing, 13(5), pp. 2292-2303,2013.
. S. L. Chen, and Y.Y. Chen, "CLB: A novel load balancing architecture and algorithm for cloud services." Computers & Electrical Engineering(58), 154-160, 2017.
. K.Q. Yan, S.C.Wang, C.P. Chang, and J.S. Lin , "A hybrid load balancing policy underlying grid computing environment". Computer Standards & Interfaces, 29(2): 161-173, 2007.
. J. Balasangameshwara, and N. Raju, " A hybrid policy for fault tolerant load balancing in grid computing environments". Journal of Network and Computer Applications, 35(1): 412-422, 2012.
. p. Kuila, and P. K. Jana,. "Approximation Schemes for Load Balanced Clustering in Wireless Sensor Networks". The Journal of Supercomputing, Springer (68), pp. 87-105, 2014.
. F. Ramezani, j. Lu, and F.K. Hussain, "Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization." International Journal of Parallel Programming, 42(5), pp. 739-754, 2013.
. A. Nakai, E. Madeira, and L.E. Buzato,"On the Use of Resource Reservation for Web Services Load Balancing." Journal of Network and Systems Managemen,t 23(3), pp. 502-538, 2014.
. I. De Falco, E. Laskowski, R. Olejnik, U. Scafuri, E. Tarantino, and M. Tudruj, "Extremal Optimization applied to load balancing in execution of distributed programs." Applied Soft Computing,30, pp. 501-513, 2015.
. L.M Khanli, S. Razzaghzadeh, and S.V. Zargari, "A New Step Toward Load Balancing Based on Competency Rank and Transitional Phases in Grid Networks", Future Generation Computer Systems, Elsevier, Vol. 28,pp. 682-688, 2012.
. S. Razzaghzadeh, A.H. Navin, A.M. Rahmani, and M. hosseinzadeh , " Probabilistic modeling to achieve load balancing in Expert Clouds" , Ad Hoc Networks, Elsevier, Vol. 28,pp.12-23,2017.
. Y. Liu, C. Zhang, B. Li, and J. Niu, "DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters." Journal of Network and Computer Applications, Vol. 83, pp. 213-220, 2015.
. Maheswaran, M., et al. (1999). "Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems", Journal of Parallel and Distributed Computing, Vol. 59: 107-131.
. S.C. Wang, K.Q. Yan, W.P. Liao, and S.S. Wang, "Towards a load balancing in a three-level cloud computing network", 3rd International Conference on Computer Science and Information Technology (ICCSIT), IEEE, 2010.
. A. Sang, X. Wang, M. Madihian, and R.D. Gitlin, "Coordinated load balancing, handoff/cell-site selection, and scheduling in multi-cell packet data systems", Wireless Networks, 14 (1) , pp. 103–120 , 2008.
. J. Ni , Y. Huang, Z. Luan, J. Zhang, and D. Qian, "Virtual machine mapping policy based on load balancing in private cloud environment", International Conference on Cloud and Service Computing (CSC), IEEE, pp. 292–295,2011.
 K.R. Babu, and P. Samuel, "Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud". Innovations in Bio-Inspired Computing and Applications: Proceedings of the 6th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2015) held in Kochi, India during December 16-18, 2015. V. Snášel, A. Abraham, P. Krömer, M. Pant and K. A. Muda. Cham, Springer International Publishing: pp. 67-78, 2016.
. T. Wang, Z. Lin, B. Yang, J. Gao, A. Huang, D. Yang, Q. Zhang, S. Tang, and J. Niu, "MBA: A market-based approach to data allocation and dynamic migration for cloud database," Science China Information Sciences 55(9), pp. 1935-1948, 2012
. J. Niu, K. Cai, E.H. Gerding, and S. Parsons, "Characterizing effective auction mechanisms: insights from the 2007 TAC market design competition". Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2. Estoril, Portugal, International Foundation for Autonomous Agents and Multiagent Systems: pp. 1079-1086, 2018.
. H. Hu, Y. Wen, T.S. Chua, J. Huang, W. Zhu, and X. Li, "Joint Content Replication and Request Routing for Social Video Distribution Over Cloud CDN: a community clustering method", IEEE Trans. Cir-cuits Syst. Video Technol. 26 (7) , pp. 1320–1333 , 2016.
. P. Kaur, and S.Mehta, "Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm", Journal of Parallel and Distributed Computing, 101, pp. 41-50, 2017.
. M. Mitchell, "An Introduction to Genetic Algorithms". Cambridge, MA: MIT Press. ISBN 9780585030944.