Green Computing – A Survey of the Current Technologies
Keywords:Cloud Computing, Green Cloud Computing, ICT, E-waste, Carbon Footprint
Cloud computing is a dynamic technology with various application spheres because of its scalability, cost-effectiveness, and reliability. However, since the energy demand for information and Communication Technologies (ICT) is on the rise, cloud computing is facing new challenges related to environmental protection, power consumption, energy efficiency, and carbon dioxide emissions. The latest technologies that strive for sustainable energy efficiency and a reduced e-waste and carbon footprint are constantly being researched and deployed. These technologies have the potential to transform cloud computing into green cloud computing. In this survey, the authors investigated recent research methodologies such as algorithm-based, architecture-based, framework-based, model-based, methods-based, and general issue-based approaches. Many of these research projects are still in their infancy and are yet to be commercially implemented. The last thing that was talked about was some future research trends and some of the open challenges in green cloud computing.
Alzamil, I., Djemame, K., Armstrong, D., & Kavanagh, R. (2015). Energy-aware profiling for cloud computing environments. Electronic Notes in Theoretical Computer Science, 318, 91-108.
Anan, M., & Nasser, N. (2015, December). SLA-based optimization of energy efficiency for green cloud computing. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
Arthi, T., & Hamead, H. S. (2013, April). Energy aware cloud service provisioning approach for green computing environment. In 2013 International Conference on Energy Efficient Technologies for Sustainability (pp. 139-144). IEEE.
Azaiez, M., Chainbi, W., & Chihi, H. (2014, April). A Green Model of Cloud Resources Provisioning. In CLOSER (pp. 135-142).
Baldé, C. P., Forti, V., Gray, V., Kuehr, R., & Stegmann, P. (2017). The global e-waste monitor 2017: Quantities, flows and resources. United Nations University, International Telecommunication Union, and International Solid Waste Association.
BDAN (26th July 2019). Green Cloud Computing – The Sustainable Way to Use the Cloud. https://bigdataanalyticsnews.com/green-cloud-computing-sustainable-use
Cappiello, C., Ho, N. T. T., Pernici, B., Plebani, P., & Vitali, M. (2015). CO 2-aware adaptation strategies for cloud applications. IEEE Transactions on Cloud Computing, 4(2), 152-165.
Chang, Y. C., Peng, S. L., Liao, Y. H., & Chang, R. S. (2015). Green computing: An SLA-based energy-aware methodology for data centers. In Intelligent Systems and Applications (pp. 1345-1354). IOS Press.
Chaudhry, M. T., Ling, T. C., & Manzoor, A. (2012, November). Considering thermal-aware proactive and reactive scheduling and cooling for green data-centers. In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) (pp. 87-91). IEEE.
de Carvalho Junior, O. A., Bruschi, S. M., Santana, R. H. C., & Santana, M. J. (2016). Green cloud meta-scheduling. Journal of Grid Computing, 14(1), 109-126.
Dougherty, B., White, J., & Schmidt, D. C. (2012). Model-driven auto-scaling of green cloud computing infrastructure. Future Generation Computer Systems, 28(2), 371-378.
Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., & Tenhunen, H. (2015, June). Utilization prediction aware VM consolidation approach for green cloud computing. In 2015 IEEE 8th International Conference on Cloud Computing (pp. 381-388). IEEE.
Ferreira, J., Dantas, J., Araujo, J., Mendonca, D., Maciel, P., & Callou, G. (2015, October). An algorithm to optimize electrical flows of private cloud infrastructures. In 2015 IEEE International Conference on Systems, Man, and Cybernetics (pp. 771-776). IEEE.
Fioccola, G. B., Donadio, P., Canonico, R., & Ventre, G. (2016, December). Dynamic routing and virtual machine consolidation in green clouds. In 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 590-595). IEEE.
Fiorani, M., Aleksic, S., Monti, P., Chen, J., Casoni, M., & Wosinska, L. (2014). Energy efficiency of an integrated intra-data-center and core network with edge caching. Journal of Optical Communications and Networking, 6(4), 421-432.
Garg, S. K., Yeo, C. S., Anandasivam, A., & Buyya, R. (2011). Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. Journal of Parallel and Distributed Computing, 71(6), 732-749.
Guazzone, M., Anglano, C., & Canonico, M. (2012, May). Exploiting VM migration for the automated power and performance management of green cloud computing systems. In International Workshop on Energy Efficient Data Centers (pp. 81-92). Springer, Berlin, Heidelberg.
Ho, T. T. N., & Pernici, B. (2015, July). A data-value-driven adaptation framework for energy efficiency for data intensive applications in clouds. In 2015 IEEE conference on technologies for sustainability (SusTech) (pp. 47-52). IEEE.
Huang, J., Wu, K., & Moh, M. (2014, July). Dynamic Virtual Machine migration algorithms using enhanced energy consumption model for green cloud data centers. In 2014 International Conference on High Performance Computing & Simulation (HPCS) (pp. 902-910). IEEE.
Hulkury, M. N., & Doomun, M. R. (2012, November). Integrated green cloud computing architecture. In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) (pp. 269-274). IEEE.
Hussein, S. R., Alkabani, Y., & Mohamed, H. K. (2014, December). Green cloud computing: Datacenters power management policies and algorithms. In 2014 9th International Conference on Computer Engineering & Systems (ICCES) (pp. 421-426). IEEE.
Itani, W., Ghali, C., Kayssi, A., Chehab, A., & Elhajj, I. (2015). G-route: an energy-aware service routing protocol for green cloud computing. Cluster Computing, 18(2), 889-908.
Kaur, G., & Midha, S. (2016, January). A preemptive priority based job scheduling algorithm in green cloud computing. In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence) (pp. 152-156). IEEE.
Kliazovich, D., Bouvry, P., & Khan, S. U. (2012). GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. The Journal of Supercomputing, 62(3), 1263-1283.
Kołodziej, J., Khan, S. U., Wang, L., Kisiel-Dorohinicki, M., Madani, S. A., Niewiadomska-Szynkiewicz, E., ... & Xu, C. Z. (2014). Security, energy, and performance-aware resource allocation mechanisms for computational grids. Future Generation Computer Systems, 31, 77-92.
Koutsandria, G., Skevakis, E., Sayegh, A. A., & Koutsakis, P. (2016). Can everybody be happy in the cloud? Delay, profit and energy-efficient scheduling for cloud services. Journal of Parallel and Distributed Computing, 96, 202-217.
Lee, H. M., Jeong, Y. S., & Jang, H. J. (2014). Performance analysis based resource allocation for green cloud computing. The Journal of Supercomputing, 69(3), 1013-1026.
Li, J., Li, B., Wo, T., Hu, C., Huai, J., Liu, L., & Lam, K. P. (2012). CyberGuarder: A virtualization security assurance architecture for green cloud computing. Future generation computer systems, 28(2), 379-390.
Lin, X., Liu, Z., & Guo, W. (2015, June). Energy-efficient vm placement algorithms for cloud data center. In Second International Conference on Cloud Computing and Big Data in Asia (pp. 42-54). Springer, Cham.
Liu, L., Wang, H., Liu, X., Jin, X., He, W. B., Wang, Q. B., & Chen, Y. (2009, June). GreenCloud: a new architecture for green data center. In Proceedings of the 6th International Conference Industry Session on Autonomic Computing and Communications Industry Session (pp. 29-38).
Liu, Y., Shu, W., & Zhang, C. (2016). A Parallel Task Scheduling Optimization Algorithm Based on Clonal Operator in Green Cloud Computing. J. Commun., 11(2), 185-191.
Long, Z., & Ji, W. (2016). Power-efficient immune clonal optimization and dynamic load balancing for low energy consumption and high efficiency in green cloud computing. J. Commun., 11(6), 558-563.
Masanet, E. (2013). The energy efficiency potential of cloud-based software: a US case study.
Nonde, L., Elgorashi, T. E., & Elmirgahni, J. M. (2016, December). Virtual network embedding employing renewable energy sources. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
Pahlevan, A., Rossi, M., Garcia del Valle, P., Brunelli, D., & Atienza Alonso, D. (2017). Joint computing and electric systems optimization for green datacenters (No. BOOK_CHAP, pp. 1-21). Springer.
Radu, L. D. (2017). Green cloud computing: A literature survey. Symmetry, 9(12), 295.
Saponara, S., Coppola, M., & Fanucci, L. (2012). How green is your cloud?-A 64-b ARM-based heterogeneous computing platform with NoC interconnect for server-on-chip energy-efficient cloud computing. In CLOSER 2012, International Conference in Clouc Computing and Services Science (pp. 135-140). SciTePress–Science and Technology Publications.
Shu, W., Wang, W., & Wang, Y. (2014). A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. EURASIP Journal on Wireless Communications and Networking, 2014(1), 1-9.
Thakur, S., & Chaurasia, A. (2016, January). Towards Green Cloud Computing: Impact of carbon footprint on environment. In 2016 6th international conference-cloud system and big data engineering (Confluence) (pp. 209-213). IEEE.
Tyurin, S., & Kamenskih, A. (2017). Green logic: models, methods, algorithms. In Green IT Engineering: Concepts, Models, Complex Systems Architectures (pp. 69-86). Springer, Cham.
Xu, L., Wang, K., Ouyang, Z., & Qi, X. (2014, August). An improved binary PSO-based task scheduling algorithm in green cloud computing. In 9th International Conference on Communications and Networking in China (pp. 126-131). IEEE.
Xu, X., Cao, L., & Wang, X. (2016). Resource pre-allocation algorithms for low-energy task scheduling of cloud computing. Journal of Systems Engineering and Electronics, 27(2), 457-469.
Yang, C. T., Wang, K. C., Cheng, H. Y., Kuo, C. T., & Hsu, C. H. (2011, September). Implementation of a green power management algorithm for virtual machines on cloud computing. In International Conference on Ubiquitous Intelligence and Computing (pp. 280-294). Springer, Berlin, Heidelberg.
Zhang, D., Chen, Z., Cai, L. X., Zhou, H., Ren, J., & Shen, X. (2016, December). Resource allocation for green cloud radio access networks powered by renewable energy. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
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