Abstract
Cloud computing is being utilized broadly everywhere throughout the world by numerous IT organizations as it gives different advantages to clients like cost sparing and convenience. In any case, with the developing requests of clients for processing administrations, cloud suppliers are urged to convey enormous datacenters which devour an exceptionally high measure of vitality and furthermore add to the expansion in carbon dioxide emanation in the earth. Along these lines, we require to create strategies which will get greater condition neighbourly figuring, for example, Green Cloud Computing. In this paper, we propose another procedure to lessen the carbon discharge and vitality utilization in the circulated cloud datacenters having distinctive vitality sources and carbon impression rates. Our methodology utilizes the carbon impression pace of the datacenters in appropriated cloud engineering and the idea of virtual machine portion and relocation for decreasing the carbon outflow and vitality utilization in the united cloud framework. Reproduction results demonstrate that our proposed methodology diminishes the carbon dioxide outflow and vitality utilization of combined cloud datacenters when contrasted with the traditional booking approach of round-robin VM planning for united cloud datacenters.
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References
Anand A, Krishna A, Tiwari R, Sharma R (2018) Comparative analysis between proprietary software vs. open-source software vs. free software. In: 5th international conference on parallel, distributed and grid computing (PDGC), pp 144–147
Anand A, Arvindhan M (2019) Scheming a proficient auto scaling technique for minimizing response time in load balancing on amazon AWS cloud. SSRN
Saraswathi AT, Kalaashri YRA, Padmavathi S (2015) Dynamic resources allocation scheme in cloud computing. Procedia Comput Sci 47:30–36
Choi Y, Lim Y (2016) Optimization approach for resource allocation on cloud computing for IoT. Int J Distrib Sens Netw
Omara FA, Khattab SM, Sahal R (2014) Optimum resource allocation of database in cloud computing. Egypt Inform J 15(1):1–12
Seddigh M, Taheri H, Sharifian S (2015) Dynamic prediction scheduling for virtual machine placement via ant colony optimization. In: Signal processing and intelligent systems conference (SPIS), Tehran, 2015, pp 104–108
Saha B (2014) Green computing. Int J Comput Trends Technol 14(2):46–50
Murugesan S (2008) Harnessing green IT: principles and practices. IT Prof 10(1)
Vosoogh A, Nouramandi-Pour R (2015) Scheduling problems for cloud computing. Cumhur Sci J 36(3):2628–2652
Kapoor S, Dabas C (2015) Cluster based load balancing in cloud computing. In: Proceedings of 8th international conference on IEEE contemporary computing (IC3), pp 76–80
Buyya R (2013) Introduction to the IEEE transactions on cloud computing. IEEE Trans Cloud Comput 1(1)
Lee YC, Zomaya AY (2010) Energy efficient utilization of resources in cloud Computing systems. J Supercomput
Lemay M, Nguyen K-K, Arnaud BS, Mohamed C (2012) Toward a zero-carbon network converging cloud computing and network virtualization. IEEE Computer Society 12:1089–7801
Boru D, Kliazovich D, Granelli F, Bouvry P, Zomaya AY (2015) Energy efficient data replication in cloud datacenter. Springer Science
Akhter N, Othman M (2016) Energy aware resources allocation datacenter. Clust Comput 19(3):1163–1182
Chheda R, Shookowsky D, Stefanovich S, Toscano J (2008) Profiling energy usage for efficient consumption. Arch J
Mallinger S (1996) Potential carbon dioxide (CO(2)) asphyxiation hazard when filling stationary low-pressure CO(2) supply systems. http://www.osha.gov/dts/hib/hib_data/hib19960605.html
Francis K, Richardson P (2009) Green maturity model for virtualization. Microsoft Arch J Green Comput
Koomey J (2007) Estimating total power consumption by server in the U.S and the world. http://enterprise.amd.com/Downloads/svrpwrusecompletefinal.pdf
Torres J (2010) Green computing: the next wave in computing. In: Ed. UPC Technical University of Catalonia
Kogge P (2011) The tops in flops. IEEE Spectr 49–54
U.S Environmental Protection Agency (2006) Report to congress on server and datacenter energy efficiency public law. http://hightech.lbl.gov/documents/data_centers/epa-datacenters.pdf
Greenpeace (2011) Greenpeace “likes” facebook’s new datacenter, but wants a greener friendship. http://www.greenpeace.org/international/en/press/releases/Greenpeace-likes-Face-books-newdatacentre-but-wants-a-greener-friendship
Kovar J (2011) Data center power consumption grows less than expected: report. www.crn.com/news/data-center/231400014/data-center-power-consumption-growsless-than-expected-report.htm?pgno=2
Miller R (2011) Google’s energy story: high efficiency, huge scale. http://www.datacenterknowledge.com/archives/2011/09/08/googles-energy-story-high-efficiency-huge-scale
Armbrust M et al (2010) A view of cloud computing. Commun ACM 53(4):50–58
Buyya R (2009) Market-oriented cloud computing: vision, hype, and reality of delivering computing as the 5th utility. In: Proceedings of international symposium on cluster computing and the grid
Barroso LA, Hölzle U (2007) The case for energy-proportional computing. IEEE Comput 40(12):33–37
Barroso LA, Holzle U (2009) The datacenter as a computer: an introduction to the design of warehouse- scale machines, 2nd edn. Morgan and Claypool Publishers, San Rafael
Wu CM, Chang RS, Chan HY (2014) A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Futur Gener Comput Syst 37:141–147
de Carvalho OA Junior, Bruschi SM, Santana RHC, Santana MJ (2016) Green cloud meta-scheduling a flexible and automatic approach. J Grid Comput 14:109–126
Alzamil I, Djemame K, Armstrong D, Kavanagh R (2015) Energy-aware profiling for cloud computing environments. Electron Notes Theor Comput Sci 318:91–108
Roy S, Gupta S (2014) The green cloud effective framework: an environment friendly approach reducing CO2 level. In: Proceedings of the 1st international conference on non-conventional energy, Kalyani, India, pp 233–236
Chu FS, Chen KC, Cheng CM (2011) Toward green cloud computing. In: Proceedings of the 5th international conference on ubiquitous information management and communication, Seoul, Korea
Ge Y, Zhang Y, Qiu Q, Lu YH (2012) A game theoretic resource allocation for overall energy minimization in mobile cloud computing system. In: Proceedings of the 2012 ACM/IEEE international symposium on low power electronics and design, Redondo Beach, CA, USA, pp 279–284
Horri A, Dastghaibyfard G (2015) A novel cost based model for energy consumption in cloud computing. Sci World J
Gavaskar S, Anisha A, Renit C, Shiney TS (2016) Mobile apps for green cloud computing performance measure. In: Proceedings of the international conference on energy efficient technologies for sustainability (ICEETS), Nagercoil, India, pp 865–869
Diouani S, Medromi H (2019) An adaptive autonomic framework for optimizing energy consumption in the cloud data centers. Int J Intell Eng Syst 12(4)
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Muthusamy, A., Anand, A., Kannan, T.V., Rao, D.N. (2021). The Modern Way for Virtual Machine Placement and Scalable Technique for Reduction of Carbon in Green Combined Cloud Datacenter. In: Balusamy, B., Chilamkurti, N., Kadry, S. (eds) Green Computing in Smart Cities: Simulation and Techniques. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-48141-4_1
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