A Novel Energy Efficient Resource Management System in Cloud Computing Environment.

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Primary target of cloud provider is to provide the maximum resource utilization and increase the revenue by reducing energy consumption and operative cost. In the service providers point of view, resource allocation, resource sharing, migration of resources on demand, memory management, storage management, load balancing, energy efficient resource usage, computational complexity handling in virtualization are some of the major tasks that has to be dealt with. The major issue focused in this paper is to reduce the energy consumption problem and management of computation capacity utilization.  For the same, an energy efficient resource management method is proposed to grip the resource scheduling and to minimize the energy utilized by the cloud datacenters for the computational work. Here a novel resource allocation mechanism is proposed, based on the optimization techniques. Also a novel dynamic virtual machine (VM) allocation method is suggested to help dynamic virtual machine allocation and job rescheduling to improve the consolidation of resources to execute the jobs. Experimental results indicated that proposed strategy outperforms as compared to the existing systems.

     

     


  • Keywords


    Cloud Provider: VM Allocation; Resource Allocation; Resource utilization; Energy consumption.

  • References


      [1] M. Armbrustet al., “Above the clouds: A Berkeley view of cloud computing,” University of California, Berkeley, Tech. Rep., Feb 2009.

      [2] Abhinandan S. Prasad, and Shrisha Rao,”A Mechanism Design Approach to Resource Procurement in Cloud Computing”, IEEE Transactions on Computers, Vol. 63, No. 1, Pp.17-30, January 2014.

      [3] Jens-Matthias Bohli, Nils Gruschka, Meiko Jensen, Member, IEEE ,Luigi Lo Iacono, and Ninja Marnau,” Security and Privacy-Enhancing Multicloud Architectures” IEEE Transactions On Dependable & Secure Computing, Vol. 10, No. 4, July/August 2013.

      [4] En-Hao Chang, Chen-Chieh Wang, Chien-Te Liu, Kuan-Chung Chen, Student Member, Ieee, And Chung-Ho Chen, Member, IEEE,” Virtualization Technology For Tcp/Ip Offload Engine”, IEEE Transactions On Cloud Computing, Vol. 2, No. 2, April-June 2014.

      [5] Zhen Xiao, Senior Member, Ieee, Qi Chen, And Haipeng Luo,” Automatic Scaling Of Internet Applications For Cloud Computing Services”, IEEE Transactions On Computers, Vol. 63, No. 5, May 2014.

      [6] Hui Zhang, Guofei Jiang, Kenji Yoshihira, And Haifeng Chen,”Proactive Workload Management In Hybrid Cloud Computing IEEE Transactions On Network And Service Management”, Vol. 11, No. 1, March 2014

      [7] S.K.Sonkar, .M.U.Kharat,” A Review on Resource Allocation and VM Scheduling Techniques and a Model for Efficient Resource Management in Cloud Computing Environment”, IEEE International conference on ICTBIG), ISBN: 978-1-5090-5519-9. Nov. 2016, DOI: 10.1109/ICTBIG.2016.7892646.

      [8] A. Singh, M. Korupolu, and D. Mohapatra, “Server-storage virtualization: integration and load balancing in data centers,” in Proc. of the ACM/IEEE conference on Supercomputing, 2008.

      [9] Zhen Xiao, Senior Member, IEEE, Weijia Song, and Qi Chen, “Dynamic resource allocation using virtual machine in cloud computing environment,” IEEE Transaction on Parallel and Distributed Systems, vol.24, no.6, pp.1107-1117, June 2013.

      [10] W. E. Walsh, G. Tesauro, J. O. Kephart, and R. Das, “Utility Functions in Autonomic Systems,” in ICAC ’04: Proceedings of the First International Conference on Autonomic Computing. IEEE Computer Society, pp. 70–77, 2004.

      [11] B. Kruekaew and W. Kimpan, “Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony” Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I, IMECS 2014, March 12 - 14, 2014, Hong Kong.

      [12] J. Hu, J. GU, G. Sun, and T. Zhao, “A scheduling strategy on load balancing of virtual machine resources in cloud computing environment,” in 3rd IEEE Int. Symp. On Parallel Architectures, Algorithms and Programming (PAAP), 2010, 18-20 Dec. 2010, pp.89-96.

      [13] G. Raj, “Effective Cost Mechanism for Cloudlet Retransmission and Prioritized VM Scheduling Mechanism Over Broker Virtual Machine Communication Framework”, International Journal on Cloud Computing: Services and Architecture, pp.41-50, 2012

      [14] Bo Li; Jianxin Li, Jinpeng Huai, Tianyu Wo; Qin Li; Liang Zhong ,“EnaCloud: An Energy-Saving Application Live Placement Approach for Cloud Computing Environments”.IEEE International Conference on Cloud Computing, Pages: 17 – 24, Year: 2009, DOI: 10.1109/CLOUD.2009.72

      [15] Xin Li; Zhuzhong Qian; Ruiqing Chi; Bolei Zhang; Sanglu Lu,” Balancing Resource Utilization for Continuous Virtual machine Requests in Clouds”, Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS),IEEE Conference Publication 2012 ,Pages: 266 - 273, DOI: 10.1109/IMIS.2012.72

      [16] AtonBelglazov,RajkumarBuyya,”Energy Efficient Allocation of Virtual Machines in Cloud Data Centers”,10th IEEE international Conference on Cluster, Cloud and Grid Computing (CCGrid), 2010 , Pages: 577 - 578,Year:2010, DOI: 10.1109/CCGRID.2010. 45

      [17] Mehiar Dabbagh, Bechir Hamdaoui, Mohsen Guizaniy and Ammar Rayesz “An Energy-Efficient VM Prediction and Migration Framework for Overcommitted Clouds”, IEEE Transaction on Cloud Computing DOI 10.1109/TCC.2016.2564403

      [18] Fei Taoa, Ying Fengb, Lin Zhanga, and T.W. Liaoc “CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling “Science direct-Applied Soft Computing, vol. 19, june2014, PP.264-279.

      [19] Tarandeep Kaur, Inderveer Chana “Energy aware scheduling of deadline constrained tasks in cloud Computing”, 4 April 2016 © Springer cluster computing 2016.

      [20] Jieun Choi, Theodora Adufu, Yoonhee Kim, Seoyoung Kim, Soonwook Hwang “A Job Dispatch Optimization Method on Cluster and Cloud for Large-scale High-Throughput Computing Service”, IEEE international conference on cloud and Automatic Computing Catalogue number 978-1-4673-3/15, DOI : 10.1109/ICCAC.2015.42

      [21] A.V.Karthick, .E.Ramara, R.Ganapathy Subramanian, “An Efficient Multi Queue Job Scheduling for Cloud Computing”, IEEE – World Congress on Computing and communication Technologies, catalogue number 978-1-4799-2877-4/14,DOI:10.1109/WCCCT.2014.8

      [22] Vahid Arabnejad, Kris Bubendorfer, Bryan Ng and Kyle Chard “A Deadline Constrained Critical Path Heuristic for Cost-effectively Scheduling Workflows”, 2015 IEEE/ACM 8th International conference on utility and cloud computing, catalogue number:978-0-7695-5697-0/15, DOI: 10.1109/UCC.2015.4

      [23] Zhiming Wang, Kai Shuang, Long Yang, Fangchun Yang “Energy-aware and revenue enhancing Combinatorial Scheduling in Virtualized of Cloud Datacenter”, Journal of Convergence Information Technology(JCIT)Volume7, Number1, January 2012 ,,DOI:10.4156/jcit.

      [24] Daochao Huang, Peng Du, Chunge Zhu, Hong Zhang, Xinran Liu“Multi-resource Packing for Job Scheduling in Virtual Machine Based Cloud Environment”, IEEE symposium on Service Oriented System Engineering, Catalogue number: 978-1-4799-8365, DOI: 10.1109/SOSE.2015.30.

      [25] Ryan Jansen, Paul R. Brenner, “Energy Efficient Virtual Machine Allocation in the Cloud an Analysis of Cloud Allocation Policies”, 2nd IEEE conference on Green computing, Catalogue number: 978-1-4577-1221-0/11.

      [26] Shahin Vakilinia, Behdad Heidarpour and Mohamed Cheriet, Energy “Efficient Resource Allocation in Cloud Computing Environments”, IEEE special section on Future network: Arch. Protocols and Applications vol.4, 2016, PP.8544-8557.


 

View

Download

Article ID: 28281
 
DOI: 10.14419/ijet.v7i4.19.28281




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.