Analysis of Resource Pooling and Resource Allocation Schemes in Cloud Computing
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2017 by IJCTT Journal | ||
| Volume-43 Number-2 |
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| Year of Publication : 2017 | ||
| Authors : Dr. Amit Chaturvedi, Aaqib Rashid | ||
| DOI : 10.14445/22312803/IJCTT-V43P112 |
Dr. Amit Chaturvedi, Aaqib Rashid "Analysis of Resource Pooling and Resource Allocation Schemes in Cloud Computing". International Journal of Computer Trends and Technology (IJCTT) V43(2):81-86, January 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Cloud servers always do resource pooling for providing to their customers. In multi-tenant cloud environment, multiple tenants may demand for the same resource or multiple resources may be occupied by the same tenanct for long time and there may be lack of resource due to this reason. So, efficient Resource Allocation Schemes are required to manage the resources. Cloud computing basically is resource pooling and allocation or sharing technology of pooled resources. In this paper, we are analysing various resource pooling and resource allocation schemes proposed by researchers for cloud computing.
References
[1] A.Singh, D. Juneja, M. Malhotra, “A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing”, Journal of King Saud University – Computer and Information Sciences (2015), pp. 1-10, 1319-1578.
[2] S.A. Hussain, M. Fatima, A.Saeed, I. Raza, R.K. Shahzad, “Multilevel classification of security concerns in
[3] cloud computing”, Applied Computing and Informatics (2016), pp.2-9, http://dx.doi.org/10.1016/j.aci.2016.03.001
[4] Saraswathi AT, Kalaashri.Y.RA, Dr.S.Padmavathi, “Dynamic Resource Allocation Scheme in Cloud Computing”, Procedia Computer Science 47 ( 2015 ) 30 – 36, doi: 10.1016/j.procs.2015.03.180
[5] M.Verma, GR Gangadharan, NC Narendra, R Vadlamani, V.Inamdar, L. Ramachandran, “Dynamic resource demand prediction and allocation in multi-tenant service clouds”, Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cpe.3767
[6] Z. Shen, S. Subbiah, X Gu, J. Wilkes, “CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems”, ACM 978-1-4503-0976-9/11/10, October 27–28, 2011,
[7] W. Lin, J.Z. Wang, C. Liang, D. Qi, “A Threshold-based Dynamic Resource Allocation Scheme for Cloud Computing”, Procedia Engineering 23(2011), pp. 695-703.
[8] P. Pradhan, R.K.Behera, BNB Ray, “Modified Round Robin Algorithm for Resource Allocation in Cloud Computing”, International Conference on Computational Modeling and Security (CMS 2016), Procedia Computer Science 85 ( 2016 ), pp. 878 – 890.
[9] Amazon Elastic Compute Cloud.http://aws.amazon.com/ec2/.
[10] Abhishek Chandra, Weibo Gong, PrashantSheno.Dynamic Resource Allocation for Shared DataCentres Using Online Measurements 2003
[11] J. Chase, D. Anderson, P. N. Thakar, and A. M. Vahdat.Managing energy and server resources in hosting centers. InProc. SOSP, 2001.
[12] X. Fan, W.-D.Weber, and L. A. Barroso. Power provisioningfor a warehouse-sized computer. In Proc. ISCA, 2007.
[13] D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Capacitymanagement and demand prediction for next generation datacenters. In Proc. ICWS, 2007.
[14] E. Kalyvianaki, T. Charalambous, and S. Hand. Self-adaptiveand self-configured CPU resource provisioning forvirtualized servers using Kalman filters. In Proc. ICAC,2009.
[15] H. Lim, S. Babu, and J. Chase. Automated control for elasticstorage. In Proc. ICAC, 2010.
[16] Xiaoyun Zhu, Zhikui Wang, SharadSinghal Utility-driven workloadmanagement using nested control design. In Proc. AmericanControl Conference, 2006.
[17] B. Urgaonkar, M. S. G. Pacifici, P. J. Shenoy, and A. N.Tantawi. An analytical model for multi-tier internet servicesand its applications. In Proc. SIGMETRICS, 2005.
[18] Z. Gong, X. Gu, and J. Wilkes. PRESS: PredictiveElasticResource Scaling for Cloud Systems.InProc. CNSM, 2010.
[19] M. Armbrust, A. Fox, D. A. Patterson, N. Lanham,B. Trushkowsky, J. Trutna, and H. Oh. Scads:Scale-independent storage for social computing applications.In Proc. CIDR, 2009.
[20] ZhimingShen, SethuramanSubbiah, XiaohuiGu, John Wilkes, “CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems” 2011
[21] VenkatanathanVaradarajan, Yinqian Zhang, Thomas Ristenpart_, and Michael Swift†, ”Placement Vulnerability Study in Multi-Tenant Public Clouds”
[22] Jing Zhu_, Dan Li_z, Jianping Wu_, Hongnan Liu_, Ying Zhangy, JingchengZhang_ “Towards Bandwidth Guarantee in Multi-tenancy Cloud Computing Networks”
[23] T.Garg1, R.Kumar2, J.Singh, “A way to cloud computing basic to multitenant environment”
[24] Keng-Mao Cho, Pang-Wei Tsai, Chun-Wei Tsai, Chu-Sing Yang,“A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing.”
[25] VasiliosAndrikopoulos, Tobias Binz, Frank Leymann, Steve Strauch, “How to adapt applications for the Cloud environment Challenges and solutions in migrating applications to the Cloud”.
[26] Zhang S, Qian ZZ, Wu J et al. Service-oriented resource allocation in clouds: Pursuing flexibility and efficiency. JOURNALOF COMPUTER SCIENCE AND TECHNOLOGY 30(2): 421–436 Mar. 2015. DOI Service-Oriented Resource Allocation in Clouds: Pursuing Flexibility and Efficiency.
Keywords
Resource scaling, cloud computing, virtual machine, multi-tenant.