Overview of Green Cloud Architecture

Abstract

Currently the Cloud computing technology is on the verge of spurring an information revolution in all regions. It offering utility-oriented IT services to users which better suited option than a traditional methods. Cloud has millions of services based on web services. Cloud is very cost effective infrastructure for this web related services. To run and maintain cloud extremely high energy is needed. This tends to increase cost and carbon emission which reduces its efficiency. This paper discusses and analyzes some of the reason which can help in green cloud architecture. This paper includes review of static architecture energy and dynamic architecture energy issues and tries to find method to solve it.

References

• Google App Engine. 2010. http://code. google. com/appengine/.
• Vecchiola, C. , Chu, X. and Buyya, R. 2009. Aneka: A Software Platform for . NET-based Cloud Computing. In High Performance & Large Scale computing, Advances in Parallel Computing, ed. W. Gentzsch, L. Grandinetti and G. Joubert, IOS Press.
• Microsoft Azure. 2011. www. microsoft. com/windowsazure/
• Charrington, S. 2010, Characteristics of Platform as a Service, Cloud Pulse blog, http://Cloudpulseblog. com /2010/02/the-essential-characteristics-of-paas.
• Ge, R. , Feng, X. , Cameron, K. W. : Performance constrained distributed DVS scheduling for scientific applications on powerCloud Computaware clouds. In: Proc. of Supercomputing Conference, p. 34(2005)
• Kim, K. H. , Buyya, R. , Kim, J. : Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clouds. In: Proc. of CCGRID, pp. 541–548 (2007)
• Vasic, N. , Barisits, M. , Salzgeber, V. , Kostic, D. : Making cloud applications energy-aware. In: ACDC. Proc. of the 1st Workshop on Automated Control for Datacenters and Clouds, pp. 37–42(2009)
• X. Li, Y. Li, T. Liu, J. Qiu, and F. Wang, "The method and tool of cost analysis for cloud computing," in the IEEE International Conference on Cloud Computing (CLOUD 2009), Bangalore,India, 2009, pp. 93-100.
• G. Jung, M. A. Hiltunen, and K. R. Joshi, "Mistral: dynamically managing power, performance, and adaptation cost in cloud infrastructures," in the International Conference on Distributed Computing Systems (ICDCS 2010), Genova, Italy, 2010, pp. 62-73.
• W. Mach and E. Schikuta, "A consumer-provider cloud cost model considering variable cost," in the 9th IEEE International Conference on Dependable, Autonomic and Secure Computing(DASC 2011), Sydney, Australia, 2011, pp. 628-635.
• Y. C. Lee and A. Y. Zomaya, "Energy efficient utilization of resources in cloud computing systems," The Journal of Supercomputing, nline First, pp. 1-13, March 2010.
• Q. Chen, P. Grosso, K. v. d. Veldt, C. d. Laat, R. Hofman, and H. Bal, "Profiling energy consumption of VMs for green cloud computing," in the 9th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2011),Sydney, Australia, 2011, pp. 768-775.
• A. Kansal, F. Zhao, N. Kothari, and A. A. Bhattacharya, "Virtual machine power metering and provisioning," in the 1st ACM Symposium on Cloud Computing (SoCC 2010), Indianapolis
• download. microsoft. com/Why_and_How_Europe_Must_Reach_for_Cloud_Computing. pdf
• http://www. sererra. com/Go-Cloud
• Andersen, D. G. , Franklin, J. , Kaminsky,M. , Phanishayee, A. , Tan,L. , Vasudevan, V. : FAWN: A fast array of wimpy nodes. In: Proc of the 22nd ACM Symposium on Operating Systems Principles (SOSP), Big Sky, MT (2009)
• Caulfield, A. M. , Grupp, L. M. , Swanson, S. : Gordon: using flash memory to build fast, power-efficient clusters for data-intensive applications. In: Proc. of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '09) (2009)
• Feller, E. , Morin, C. , Leprince, D. : State of the art of power saving in clusters and results from the EDF case study. Institut National de Recherche en Informatique et en Automatique (INRIA) (2010)
• Tolentino, M. E. , Turner, J. , Cameron, K. W. : Memory-miser: a performance-constrained runtime system for power-scalable clusters. In: Proc. of International Conference Computing Frontiers,pp. 237–246 (2007)
• Warren, M. S. , Weigle, E. H. , Feng, W. -C. : High-density computing:a 240-processor beowulf in one cubic meter. In: Proc. Of IEEE/ACM SC2002, Baltimore, Maryland, pp. 1–11 (2002)
• Blue Gene/LTeam: An overview of the BlueGene/L supercomputer. In: Supercomputing 2002 Technical Papers (2002)
• Beloglazov, A. , Buyya, R. , Lee, Y. C. , Zomaya, A. : A taxonomy and survey of energy-efficient data centers and cloud computing systems. In: Zelkowitz,M. (ed. ) Advances in Computers. Elsevier,Amsterdam (2011). ISBN 13:978-0-12-012141-0
• Ge, R. , Feng, X. , Cameron, K. W. : Performance constrained distributed DVS scheduling for scientific applications on power aware clusters. In: Proc. of Supercomputing Conference, p. 34 (2005)
• Chen, G. , Malkowski, K. , Kandemir, M. , Raghavan, P. : Reducing power with performance constraints for parallel sparse applications. In: Proc. of the 19th IEEE International Parallel and Distributed Processing Symposium, p. 231a. IEEE Comput. Soc. , LosAlamitos (2005)
• US EPA: Report to congress on server and data center energy efficiency. Technical report (2007)
• Ge, R. , Feng, X. , Cameron, K. W. : Improvement of powerperformance efficiency for high-end computing. In: Proc. of the 1st Workshop on High-Performance, Power-Aware Computing (2005), 8 pp.
• Huang, S. , Feng,W. : Energy-efficient cluster computing via accurate workload characterization. In: Proc. of the 9th IEEE/ACM International
• Symposium Cluster Computing and the Grid, pp. 68–75 (2009)
• Pinheiro, E. , Bianchini, R. , Carrera, E. V. , Heath, T. : Load balancing and unbalancing for power and performance in cluster-based systems. In: Proc. of Workshop on Compilers and Operating Systemsfor Low Power (2001)

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