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A Tier Labeling Proposal for Energy Efficiency in Green Cloud

A Tier Labeling Proposal for Energy Efficiency in Green Cloud

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DOI	10.20900/jsr20240004
刊名	Journal of Sustainability Research JSR
年，卷(期)	2024, 6(1)
作者	Thiago Nelson Faria dos Reis*,Mario Meireles Teixeira,Carlos de Salles Soares Neto,Alana Oliveira,Apoena Mendes Sousa,
作者单位	1 DCCMAPI/UFMA/UFPI, Federal University of Maranhao, Av dos Portugueeses, Sao Luis, Maranhao 65000-000, Brazil; 2 Computer Engineering, Federal University of Maranhao, Av dos Portugueeses, Sao Luis, Maranhao 65000-000, Brazil; 3 Santa Terezinha University Center (CEST), Av Casemiro Junior, Sao Luis, Maranhao 65045-180, Brazil
Abstract	Background: As cloud computing continues to gain prominence in our daily lives and business environments, it is crucial for us to be conscious of and proactive in managing the environmental impact of this technology. Green cloud computing is an approach that aims to reduce the energy consumption and CO2 emissions associated with cloud computing while still providing the necessary functionality and performance. Methods: This study sought to evaluate the feasibility of reducing energy consumption without compromising processing time through efficient algorithms and intelligent strategies using simulators. Results: A reduction of approximately 55% in energy consumption was observed, leading to a decrease in equivalent CO2 emissions and a 28% reduction in costs associated with virtual machine allocation, ultimately resulting in a reduced environmental impact. Conclusions: To facilitate the implementation of green cloud computing, a scoring framework was proposed for evaluating the algorithm results, along with an energy efficiency classification framework utilizing artificial intelligence.
KeyWord	green cloud computing; energy efficiency; environmental impact; energy optimization strategies; energy consumption reduction technologies
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1. Khan R, Khan SU. Achieving energy saving through proxying applications on behalf of idle devices. Procedia Comput Sci. 2016;83:187-94. 2. Masdari M, Zangakani M. Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues. J Grid Comput. 2020;18(4):727-59. 3. Gade A, Bhat N, Thakare N. Survey on energy-efficient cloud: A novel approach towards green computing. Available from: http://helix.dnares.in/wp-content/uploads/2018/09/3976-3979.pdf. Accessed 2024 Mar 12. 4. Saha B. Green computing: current research trends. Int J Comput Sci Eng. 2018;6(3):467-9. 5. Meyer V, Krindges R, Ferreto TC, De Rose CA, Hessel F. Simulators usage analysis to estimate power consumption in cloud computing environments. Available from: https://ieeexplore.ieee.org/abstract/document/8748875. Accessed 2024 Mar 12. 6. Makaratzis AT, Giannoutakis KM, Tzovaras D. Energy modeling in cloud simulation framework. Future Gener Comp Sy. 2018;79:715-25. 7. Jena SR, Shanmugam R, Saini K, Kumar S. Cloud computing tools: inside views and analysis. Procedia Comput Sci. 2020;173:382-91. 8. Yang J, Xiao W, Jiang C, Hossain MS, Muhammad G, Amin SU. Aipowered green cloud and data center. IEEE Access. 2018;7:4195-203. 9. Wadhwa M, Goel A, Choudhury T, Mishra VP. Green cloud computing—A greener approach to IT. Available from: https://www.researchgate.net/profile/Ved-P-Mishra/publication/339406642_Green_Cloud_Computing_-_A_Greener_Approach_To_IT/links/5e4fd636a6fdccd965b60dfc/Green-Cloud-Computing-A-Greener-Approach-To-IT.pdf. Accessed 2024 Mar 12. 10. Zong Z. An improvement of task scheduling algorithms for green cloud computing. Available from: https://ieeexplore.ieee.org/abstract/document/9201785. Accessed 2024 Mar 12. 11. Silva DT, Rodrigues JA, Manacero A, Lobato RS, Spolon R, Cavenaghi MA. Modeling and simulation of cloud computing with Ispd. Available from: https://sol.sbc.org.br/index.php/wscad/article/view/21946/21769. Accessed 2024 Mar 12. 12. Stergiou CL, Psannis KE, Ishibashi Y. Green Cloud Communication System for Big Data Management. Available from: https://ruomo.lib.uom.gr/bitstream/7000/791/1/Green%20Cloud%20Communication%20System%20for%20Big%20Data%20Management%28Revised%29.pdf. Accessed 2024 Mar 12. 13. Saboor A, Mahmood AK, Omar AH, Hassan MF, Shah SNM, Ahmadian A. Enabling rank-based distribution of microservices among containers for green cloud computing environment. Peer Peer Netw Appl. 2022;15(1):77-91. 14. França CG, Faceroli ST, Nascimento PSC, Kligerman AS. Análise comparativa de modelos de previsão de geração de energia eólica baseados em machine learning
[Comparative analysis of wind power generation prediction models based on machine learning]. Comput Syst J. 2019;9(2):348. Portuguese. 15. Araujo RS, Sousa FLN, Vanderley PS, Silva Bentes SO, Gomes LM, Ferreira FCL. Fontes de energias renováveis: pesquisas, tendencias e perspectivas sobre as práticas sustentáveis
[Renewable energy sources: research, trends and perspectives on sustainable practices]. Res Soc Dev. 2022;11(11):e468111133893. Portuguese. 16. Radu LD. Green Cloud Computing: A Literature Survey. Symmetry. 2017;9(12):295. 17. Jasmin M, Philomina S. Runtime adaptive Dynamic Voltage Frequency Scaling technique for reducing the power consumption in Multi Processor System On Chip. Available from: http://jmcms.s3.amazonaws.com/wp-content/uploads/2019/08/24094357/53Runtime-adaptive-Dynamic-Voltage-Frequency-Scaling-technique-for-reducing-the-power-consumption-in-Multi-Processor-System-On-Chip.pdf. Accessed 2024 Mar 12. 18. Agrawal MN, Saini MJK, Wankhede P. Review on green cloud computing: A step towards saving global environment. Available from: https://www.researchgate.net/profile/Pallavi-Shelke/publication/353868856_Review_on_Green_Cloud_Computing_A_Step_Towards_Saving_Global_Environment/links/6115f9441ca20f6f861b73c4/Review-on-Green-Cloud-Computing-A-Step-Towards-Saving-Global-Environment.pdf. Accessed 2024 Mar 12. 19. Shami TM, El-Saleh AA, Alswaitti M, Al-Tashi O, Summakieh MA, Mirjalili S. Particle swarm optimization: A comprehensive survey. IEEE Access. 2020;10:10031-61. 20. Barbierato E, Gribaudo M, Iacono M, Jakobik A. Exploiting Cloudsim in a Multiformalism modeling Approach for Cloud-Based Systems. Simul Model Pract Theory. 2019;93:133-47. 21. Silva Filho MC, Oliveira RL, Monteiro CC, Inacio PR, Freire MM. Cloudsim plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility, and correctness. Available from: https://dl.ifip.org/db/conf/im/im2017mini/047.pdf. Accessed 2024 Mar 12. 22. CLOUDSIM. Full-featured and Fully Documented Cloud Simulation Framework. Available from: http://cloudsimplus.org/. Accessed 2021 Jun 1. 23. Balharith T, Alhaidari F. Round robin scheduling algorithm in CPU and cloud computing: a review. Available from: https://ieeexplore.ieee.org/abstract/document/8769534. Accessed 2024 Mar 12. 24. Dorigo M, Stützle T. Ant Colony Optimization: Overview and Recent Advances. In: Gendreau M, Potvin JY, editors. Handbook of Metaheuristics. Cham (Switzerland): Springer International Publishing; 2019. p. 311-51. 25. Jain R. Computer systems performance analysis: Techniques for experimental design, measurement, simulation, and modeling. Available from: https://www.cse.wustl.edu/~jain/iucee/index.html. Accessed 2024 Mar 12. 26. AWS. Family pricing of Amazon WorkSpaces. Available from: https://aws.amazon.com/workspaces/pricing/. Accessed 2022 Feb 1. 27. EPA. Greenhouse Gas Equivalencies Calculator. Available from: https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator. Accessed 2022 Aug 1. 28. Nesmachnow S, Dorronsoro B, Pecero JE, Bouvry P. Energy-aware scheduling on multicore heterogeneous grid computing systems. J Grid Comput. 2021;11:653-80. 29. Alvarez PA, Ishizaka A, Martínez L. Multiple-criteria decision-making sorting methods: A survey. Expert Syst Appl. 2021;183:115368. 30. Zheng Y, Wang D. Multi-criteria ranking: Next generation of multi-criteria recommendation framework. IEEE Access. 2022;10:90715-25. 31. Kumar R, Singh S, Bilga PS, Singh J, Singh S, Scutaru ML, et al. Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review. J Mater Res Technol. 2021;10:1471-92. 32. Espadoto M, Hirata NST, Telea AC. Self-supervised dimensionality reduction with neural networks and pseudo-labeling. Available from: https://repositorio.usp.br/directbitstream/c3d85d02-dcca-4129-9bf5-bd602ddbd123/3031757.pdf. Accessed 2024 Mar 12. 33. Zhang M, Li P, Xia Y, Wang K, Jin L. Labeling trick: A theory of using graph neural networks for multi-node representation learning. Adv Neural Inf Process Syst. 2021;34:9061-73. 34. Lin JCW, Shao Y, Djenouri Y, Yun U. ASRNN: A recurrent neural network with an attention model for sequence labeling. Knowl-Based Syst. 2021;212:106548.

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Thiago Nelson Faria dos Reis*,Mario Meireles Teixeira,Carlos de Salles Soares Neto,Alana Oliveira,Apoena Mendes Sousa. A Tier Labeling Proposal for Energy Efficiency in Green Cloud, Journal of Sustainability Research. 2024; 6; (1). https://doi.org/10.20900/jsr20240004.

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