Dynamic Virtual Machine Allocation Policy for Load Balancing using Principal Component Analysis and Clustering Technique in Cloud Computing
Keywords:
Virtual Machines, Allocation Policy, PCA Technique, Clustering, Cloud Computing, CloudSim,Abstract
The scalability and agility characteristics of cloud computing allow load balancing to reroute workload requests easily and to enhance overall accessibility. One of the most important services for cloud computing is Infrastructure as a Service (IaaS). There is a large number of physical hosts in a cloud data center for IaaS and it is quite difficult to arrange the allocation of the workload requests manually. Therefore, different load balancing methods have been proposed by researchers to avoid overloaded physical hosts in the cloud data center. However, fewer works have used multivariate analysis in cloud computing environment for considering the dynamic changes of the computing resources. Thus, this work suggests a new Virtual Machine (VM) allocation policy for load balancing by using a multivariate technique, Principal Component Analysis (PCA), and clustering technique. Moreover, PCA and clustering techniques were simulated on a cloud computing simulator, CloudSim. In the proposed allocation policy, a group of VMs were dynamically allocated to physical hosts. The allocation was based on the clusters of hosts according to their similar features in computing resources. The clusters were formed using PCA and a clustering technique based on variables related to the physical hosts such as Million Instructions Per Second (MIPS), Random Access Memory (RAM), bandwidth and storage. The results show that the completion time for all tasks has decreased, and the resource utilization has increased. This will optimize the performance of cloud data centers by effectively utilizing the available resources.References
P. Mell, and T. Grance, "The NIST definition of cloud computing," Technical Report No. SP 800-145, The NIST Definition of Cloud Computing, National Institute of Standards & Technology, Gaithersburg, 2011.
A. Shawish, and M. Salama, "Cloud computing: paradigms and technologies," in Inter-cooperative collective intelligence: Techniques and applications: Springer, pp. 39-67, 2014
A. M. Kadhum, and M. K. Hasan, "Assessing the Determinants of Cloud Computing Services for Utilizing Health Information Systems: A Case Study," International Journal o Advanced Science, Engineering and Information Technology, vol. 7, no. 2, pp. 503-510, 2017.
K. Parikh, N. Hawanna, H. PK, and N. C. S. Iyengar, "Virtual machine allocation policy in cloud computing using cloudsim in java," International Journal of Grid and Distributed Computing, vol. 8, no. 1, pp. 145-158, 2015.
P. Samimi, Y. Teimouri, and M. Mukhtar, "A combinatorial double auction resource allocation model in cloud computing," Information Sciences, vol. 357, pp. 201-216, 2016.
N. Fonseca, and R. Boutaba, Cloud Services, Networking, and Management. John Wiley & Sons, 2015.
L. S. Xue, N. A. A. Majid, and E. A. Sundararajan, "Quality of Service Evaluation of IaaS Modeler Allocation Strategies on Cloud Computing Simulator," in Proceedings of the International Conference on High Performance Compilation, Computing and Communications, pp. 6-10: ACM, 2017
S.-C. Wang, K.-Q. Yan, W.-P. Liao, and S.-S. Wang, "Towards a load balancing in a three-level cloud computing network," in 3rd IEEE Conference on Computer Science and Information Technology [ICCSIT], vol. 1, pp. 108-113, 2010
P. Devi, and T. Gaba, "Implementation of cloud computing by using short job scheduling," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 178- 183, 2013.
K. Dasgupta, B. Mandal, P. Dutta, J. K. Mandal, and S. Dam, "A genetic algorithm (ga) based load balancing strategy for cloud computing," Procedia Technology, vol. 10, pp. 340-347, 2013.
R. Kaur, and P. Luthra, "Load Balancing in Cloud System using Maxmin and Min-min Algorithm," International Journal of Computer Applications (0975–8887), 2014.
D. B. LD, and P. V. Krishna, "Honey bee behavior inspired load balancing of tasks in cloud computing environments," Applied Soft Computing, vol. 13, no. 5, pp. 2292-2303, 2013.
D. Nusrat Pasha, A. Agarwal, and R. Rastogi, "Round Robin Approach for VM Load Balancing Algorithm in Cloud Computing Environment," International Journal of Advanced Research in Computer Science and Software Engineeringl, vol. 4, no. 5, pp. 34-79, 2014.
S. Sethi, A. Sahu, and S. K. Jena, "Efficient load balancing in cloud computing using fuzzy logic," IOSR Journal of Engineering, vol. 2, no. 7, pp. 65-71, 2012.
J. Zhao, K. Yang, X. Wei, Y. Ding, L. Hu, and G. Xu, "A Heuristic Clustering-based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment," IEEE Trans. Parallel Distrib. Syst., vol. 27, no 2, pp. 305-316, 2016.
Z. Á. Mann, "Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms," ACM Computing Surveys (CSUR), vol. 48, no. 1, pp. 1-34, 2015.
A. Beloglazov and R. Buyya, "Energy efficient allocation of virtual machines in cloud data centers," in Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on, 2010, pp. 577-578: IEEE.
K. Garala, N. Goswami, and P. D. Maheta, "A performance analysis of load Balancing algorithms in Cloud environment," in Computer Communication and Informatics (ICCCI), 2015 International Conference on, 2015, pp. 1-6: IEEE.
A. Jula, E. Sundararajan, and Z. Othman, "Cloud computing service composition: A systematic literature review," Expert Systems with Applications, vol. 41, no. 8, pp. 3809-3824, 2014.
M. Nitika, M. Shaveta, and M. G. Raj, "Comparative analysis of load balancing algorithms in cloud computing," International Journal of Advanced Research in Computer Engineering & Technology, vol. 1, no. 3, pp. 120-124, 2012.
B. Mondal, K. Dasgupta, and P. Dutta, "Load balancing in cloud computing using stochastic hill climbing-a soft computing approach," Procedia Technology, vol. 4, pp. 783-789, 2012.
G. Lee, "Resource allocation and scheduling in heterogeneous cloud environments," Technical Report No. UCB/EECS-2012-78, 2012.
H. Abdi, and L. J. Williams, "Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433-459, 2010.
F. Boobord, Z. Othman, and A. Abubakar, "PCAWK: A Hybridized Clustering Algorithm Based on PCA and WK-means for Large Size of Dataset," Int. J. Advance Soft Compu. Appl, vol. 7, no. 3, 2015.
B. Gowri, R. Rajmohan, D. Dinagaran, M. Pajany, and A. Divya, "PCA based Lattice Clustering Methodology for Heterogeneous Dataset Modeling and Analysis," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 3, pp. 78- 83, 2016.
R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms," Software: Practice and Experience, vol. 41, no. 1, pp. 23- 50, 2011.
N. A. A. Majid, M. P. Taylor, J. J. J. J. Chen, W. Yu, and B. R. Young, "Diagnosing faults in aluminium processing by using multivariate statistical approaches," Journal of Materials Science, journal article vol. 47, no. 3, pp. 1268-1279, 2012.
O. R. Zaïane, "Introduction to data mining," 1999, Available: http://webdocs.cs.ualberta.ca/~zaiane/courses/cmput690/notes/Chapte r1/index.html.
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