CFP last date
20 May 2024
Reseach Article

A Survey on Power Efficiency in Cloud Computing to Optimize the Cost

Published on June 2016 by Banashankari, Chandan Raj B. R.
National Conference on Advances in Computing, Communication and Networking
Foundation of Computer Science USA
ACCNET2016 - Number 1
June 2016
Authors: Banashankari, Chandan Raj B. R.
858ff2c7-d698-456c-af94-f4ac2f071437

Banashankari, Chandan Raj B. R. . A Survey on Power Efficiency in Cloud Computing to Optimize the Cost. National Conference on Advances in Computing, Communication and Networking. ACCNET2016, 1 (June 2016), 1-6.

@article{
author = { Banashankari, Chandan Raj B. R. },
title = { A Survey on Power Efficiency in Cloud Computing to Optimize the Cost },
journal = { National Conference on Advances in Computing, Communication and Networking },
issue_date = { June 2016 },
volume = { ACCNET2016 },
number = { 1 },
month = { June },
year = { 2016 },
issn = 0975-8887,
pages = { 1-6 },
numpages = 6,
url = { /proceedings/accnet2016/number1/24967-2252/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing, Communication and Networking
%A Banashankari
%A Chandan Raj B. R.
%T A Survey on Power Efficiency in Cloud Computing to Optimize the Cost
%J National Conference on Advances in Computing, Communication and Networking
%@ 0975-8887
%V ACCNET2016
%N 1
%P 1-6
%D 2016
%I International Journal of Computer Applications
Abstract

In this paper, we describe about power minimization in cloud computing. Cloud computing is the well-known technology for scaling of extensive data and complex computation. Increasing data volume is giving the bigger task of Data Centers (DCs) to provide a better quality of cloud computing. These DCs, servers, cooling and also security systems consume enormous power, resulting with an increase in CO2 emission and ramp-up in the operational cost. The Power Consumption (PC) rate will increase simultaneously, with DCs size expansion or increase in the number of DCs to fulfill the needs of data storage, processing and hosting demands. However, present and future environment for cloud computing is changing rapidly which ease necessity of Power Efficiency (PE) is balancing. The PC can be minimized by efficient resource management of DC in virtualized manner. This paper discusses a survey on Power Efficiency in Cloud Competing (PECC) to optimize the cost and describes the discussion over green IT technologies. Finally, future work flow towards CC cost optimization is presented.

References
  1. Sultan, Nabil. "Cloud computing for education: A new dawn. " International Journal of Information Management 30. 2, pp. 109-116, 2010.
  2. Tsai, Wei-Tek, Xin Sun, and Janaka Balasooriya. "Service-oriented cloud computing architecture. " Information Technology: New Generations (ITNG), 2010 Seventh International Conference on. IEEE, 2010.
  3. Foster, Ian, et al. "Cloud computing and grid computing 360-degree compared. " Grid Computing Environments Workshop, 2008. GCE'08. Ieee, 2008.
  4. Mark I Williams, " A Quick Start Guide to Cloud Computing: Moving Your Business into the Cloud",Kogan Page Publishers, 152 pages,03-Oct-2010.
  5. Buyya, Rajkumar, Chee Shin Yeo, and Srikumar Venugopal. "Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. " High Performance Computing and Communications, 2008. HPCC'08. 10th IEEE International Conference on. Ieee, 2008.
  6. Rattle, Robert. Computing Our Way to Paradise?: The Role of Internet and Communication Technologies in Sustainable Consumption and Globalization. Rowman & Littlefield, 2010.
  7. Buyya, Rajkumar, Christian Vecchiola, and S. Thamarai Selvi. Mastering cloud computing: foundations and applications programming. Newnes, 2013.
  8. Beloglazov, Anton, Jemal Abawajy, and Rajkumar Buyya. "Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. " Future generation computer systems 28. 5 (2012): 755-768.
  9. Younge, Andrew J. , et al. "Efficient resource management for cloud computing environments. " Green Computing Conference, 2010 International. IEEE, 2010.
  10. Aswal, Mahendra S. "A STUDY OF GREEN CLOUD COMPUTING PARADIGMS. "
  11. Williams, Bill. The economics of cloud computing. Cisco Press, 2011.
  12. Chen, Hui, et al. "A cyber-physical integrated system for application performance and energy management in data centers. " Green Computing Conference (IGCC), 2012 International. IEEE, 2012.
  13. Abbasi, Zahra, Georgios Varsamopoulos, and Sandeep KS Gupta. "Tacoma: Server and workload management in internet data centers considering cooling-computing power trade-off and energy proportionality. " ACM Transactions on Architecture and Code Optimization (TACO) 9. 2 , 11 edition 2012.
  14. Chang, William Y. , Hosame Abu-Amara, and Jessica Feng Sanford. Transforming enterprise cloud services. Springer Science & Business Media, 2010.
  15. Wilson III, Ellis H. A protean attack on the compute-storage gap in high-performance computing. Diss. The Pennsylvania State University, 2014.
  16. Marks, Eric A. , and Bob Lozano. Executive's guide to cloud computing. John Wiley and Sons, 2010.
  17. Belapurkar, Abhijit, et al. Distributed systems security: issues, processes, and solutions. John Wiley & Sons, 2009.
  18. Chorafas, Dimitris N. Cloud computing strategies. CRC press, 2010.
  19. Younge, Andrew J. , et al. "Efficient resource management for cloud computing environments. " Green Computing Conference, 2010 International. IEEE, 2010.
  20. Lambin, Eric F. , et al. "The causes of land-use and land-cover change: moving beyond the myths. " Global environmental change 11. 4, pp. 261-269, 2001.
  21. Harding, Ronnie, ed. Environmental decision-making: The roles of scientists, engineers, and the public. Federation Press, 1998.
  22. Miettinen AP, Nurminen JK. Energy efficiency of mobile clients in cloud computing. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing 2010 Jun 22 (pp. 4-4). USENIX Association.
  23. Beloglazov, Anton, and Rajkumar Buyya. "Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. " In Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science, vol. 4. ACM, 2010.
  24. Qian, Haiyang, and Deep Medhi. "Server operational cost optimization for cloud computing service providers over a time horizon. " In Proceedings of the 11th USENIX conference on Hot topics in management of internet, cloud, and enterprise networks and services, pp. 4-4. USENIX Association, 2011.
  25. Verma, Amandeep, and Sakshi Kaushal. "Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud. " In of the IJCA on International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT'12), pp. 1-4. 2012.
  26. Breitgand, David, A. Marashini, and Johan Tordsson. "Policy-driven service placement optimization in federated clouds. " IBM Research Division, Tech. Rep 9 (2011): 11-15.
  27. Bittencourt, Luiz Fernando, and Edmundo Roberto Mauro Madeira. "HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds. " Journal of Internet Services and Applications 2, no. 3 (2011): 207-227.
  28. Poobalan, A. , and V. Selvi. "Optimization of Cost in Cloud Computing Using OCRP Algorithm. " International Journal of Engineering Trends and Technology (IJETT)–Volume 4.
  29. Dabbagh, M. ; Hamdaoui, B. ; Guizani, M. ; Rayes, A. , "Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers," in Network and Service Management, IEEE Transactions on , vol. 12, no. 3, pp. 377-391, Sept. 2015
  30. Farahnakian F. ; Ashraf, A. ; Pahikkala, T. ; Liljeberg, P. ; Plosila, J. ; Porres, I. ; Tenhunen, H. , "Using Ant Colony System to Consolidate VMs for Green Cloud Computing," in Services Computing, IEEE Transactions on , vol. 8, no. 2, pp. 187-198, March-April 1 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Cost Optimization Data Centers Virtualization.