ABSTRACT
Cloud Computing provides rapid provision of computing resources like processing power, memory, network resources, storage, etc. Running computing resources for longer time, leads energy consumption, increase the emission of Carbon Dioxide (CO2) and increase the expenditure cost for the resources usage. Hence there is a necessity to minimize the execution time to reduce energy consumption in the cloud environment. One of the existing approaches to reducing energy consumption is based on Migration and Placement Policy for Virtual Machine, but still improving placement technique we can further minimize power consumption. In our proposed architecture for cloud resource allocation based on Clustering method, we do map a group of tasks to virtual machines. For clustering, we work on task usage of CPU, memory, and bandwidth. This proposed clustering technique further decreases energy consumption by efficient resource allocation.
- Kaplan, J.M., Forrest, W. and Kindler, N. (2008) Revolutionizing data center energy efficiency. Technical Report. https://www.sallan.org/pdf-docs/McKinsey_Data_Center_Efficiency.pdf (accessed June 5, 2015).Google Scholar
- Buyya, R., Beloglazov, A. and Abawajy, J. (2010) EnergyefficientManagement of Data Center Resources for CloudComputing: A Vision, Architectural Elements, and Open Challenges. Proc. 16th Int. Conf. Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA,July 12--15, pp. 6--17. World Academy of Science, Engineering and Technology, San Diego, USA.Google Scholar
- Greenberg, S., Mills, E., Tschudi, B., Rumsey, P. and Myatt, B.(2006) Best Practices for Data Centers: Lessons Learned fromBenchmarking 22 Data Centers. Proc. ACEEE Summer Study on Energy Efficiency in Buildings, Asilomar, USA, August 13--18,pp. 76--87. ACEEE, Washington, USA.Google Scholar
- Heller, B., Seetharaman, S., Mahadevan, P., Yiakoumis, Y., Sharma, P., Banerjee, S. and McKeown, N. (2010) Elastic-Tree: Saving Energy in Data Center Networks. Proc. 7th USENIX Conf. Networked Systems Design and Implementation (NSDI'10), San Jose, USA, April 28--30. USENIX, Berkeley, USA. Google ScholarDigital Library
- Greenberg, A., Hamilton, J., Maltz, D.A. and Patel, P. (2008) The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev., 39, 68--73. Google ScholarDigital Library
- Zheng, K., Wang, X., Li, L. and Wang, X. (2014) Joint Power Optimization of Data Center Network and Servers with Correlation Analysis. Proc. IEEE INFOCOM 2014, Toronto, Canada, April 27--May 2, pp. 2598--2606. IEEE, Piscataway, USA.Google ScholarCross Ref
- Reiss, C., Wilkes, J. and Hellerstein, J.L. (2011) Google Clusterusage Traces: Format+ Schema. Google, Inc. Mountain View, USA.Google Scholar
- Kansal, A., Zhao, F., Liu, J., Kothari, N. and Bhattacharya, A.A.(2010) VirtualMachine Power Metering and Provisioning. Proc. 1st ACM Symp. Cloud Computing (SoCC'10), Indianapolis, USA, June 10--11, pp. 39--50. ACM, New York, USA. Google ScholarDigital Library
- Nathuji, R. and Schwan, K. (2007) VirtualPower: Coordinated Power Management in Virtualized Enterprise Systems. Proc. 21st ACM SIGOPS Symp. Operating Systems Principles(SOSP'07), Stevenson, WA, USA, October 14--17, pp. 265--278. ACM, New York, USA. Google ScholarDigital Library
- Kim, K.H., Beloglazov, A. and Buyya, R. (2009) Power-aware Provisioning of Cloud Resources for Real-time Services. Proc.7th Int. Workshop on Middleware for Grids, Clouds and e-Science (MGC'09), Champaign, USA, November 30--December 4, pp. 1:1--1:6. ACM, New York, USA. Google ScholarDigital Library
- Greenberg, S., Mills, E., Tschudi, B., Rumsey, P. and Myatt, B.(2006) Best Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers. Proc. ACEEE Summer Study on Energy Efficiency in Buildings, Asilomar, USA, August 13--18,pp. 76--87. ACEEE, Washington, USA.Google Scholar
- A. Mahendiran, N. Saravanan, N. Venkata Subramanian and N. Sairam, "Implementation of K-Means Clustering in Cloud Computing Environment", Research Journal of Applied Sciences, Engineering and Technology 4(10): 1391--1394, 2012, ISSN: 2040--7467Google Scholar
- G. Malathy and Rm. Somasundaram, "Performance Enhancement in Cloud Computing using Reservation Cluster", European Journal of Scientific Research, ISSN 1450--216X, Vol. 86 No 3, September, 2012, pp.394--401Google Scholar
- Y. Chen, A. Ganapathi, R. Griffith, and R. Katz, "Analysis and lessons from a publicly available google cluster trace," University of California, Berkeley, CA, Tech. Rep, 2010.Google Scholar
- S. Di, D. Kondo, W. Cirne, "Host Load Prediction in a Google Compute Cloud with a Bayesian Model", in Proc. Of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Salt Lake City, UT, November 2012. Google ScholarDigital Library
- A. Balliu, D. Olivetti, O. Babaoglu, M. Marzolla and Alina Sirbu, "BiDA1: Big Data Analyzer for Cluster Traces", In proceedings of clusterdata:Balliu2014, 2014.Google Scholar
- Sheng Di, Derrick Kondo and Cappello Franck, " Characterizing Cloud Applications on a Google Data Center," 42nd International Conference on Parallel Processing (ICPP2013), Lyon, France, Oct 2013. Google ScholarDigital Library
- A. K. Mishra, J. L. Hellerstein, W. Cirne, C.R. Das, "Towards Characterizing Cloud Backend Workloads: Insights from Google Compute Clusters," ACM SIGMETRICS Performance Evaluation Review, vol. 37, no. 4, pp. 34--41, March 2010. Google ScholarDigital Library
- Zhang Wei,Yang Hen - I,Jiang Hsin - yi,et al.Automatic data clustering analysis of arbitrary shape with K-means and enhanced ant-based template mechanism{C}.//Proc of 2012 IEEE 36th international conference on computer software and applications, 2012:452 -- 455. Google ScholarDigital Library
- Ebrahimpour R, Rasoolinezhad R, Hajiabolhasani Z, et al. Vanishing point detection in corridors: using Hough transform and K-means clustering{J}. Computer Vision, IET, 2012, 6(1): 40--51.Google Scholar
Recommendations
Minimizing Total Busy Time for Energy-Aware Virtual Machine Allocation Problems
SoICT '15: Proceedings of the 6th International Symposium on Information and Communication TechnologyThis paper investigates the energy-aware virtual machine (VM) allocation problems in clouds along characteristics: multiple resources, fixed interval time and non-preemption of virtual machines. Many previous works have been proposed to use a minimum ...
Energy Saving Virtual Machine Allocation in Cloud Computing
ICDCSW '13: Proceedings of the 2013 IEEE 33rd International Conference on Distributed Computing Systems WorkshopsIn the data center, a server can work in either active state or power-saving state. The power consumption in the power-saving state is almost 0, thus it is always desirable to allocate as many VMs as possible to some active servers and leave the rest to ...
Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms
AbstractIn this paper, we address the problem of reducing Cloud datacenter high energy consumption with minimal Service Level Agreement (SLA) violation. Although there are many energy-aware resource management solutions for Cloud datacenters, existing ...
Highlights- Addressed the problem of reducing Cloud datacenter high energy consumption with minimal Service Level Agreement (SLA) violation.
- We propose two novel adaptive energy-aware algorithms for maximizing energy efficiency and minimizing SLA ...
Comments