Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Original Research

Green and Edge-Aware Computing: Rethinking Cloud Infrastructure for Sustainability

  1. Dipankar Barui Department of Artificial Intelligence and Machine Learning, St. Thomas’ College of Engineering and Technology, Kolkata
  2. Raghunath Maji Department of Computer Science and Engineering, Greater Kolkata College of Engineering and Management, Kolkata
  3. Subhankar Roy Department of Computer Science and Engineering, Seacom Engineering College, Kolkata
  4. Satyajit Maiti Department of Computer Science and Engineering, Swami Vivekananda University, Kolkata
  5. Subhadip Goswami Department of Computer Science and Technology, JIS College of Engineering

Abstract

Cloud computing has transformed the way organizations access and manage information technology resources, providing flexible, scalable, and cost-efficient services that support today’s data-driven world. Despite these advantages, the rapid expansion of large-scale cloud infrastructures has resulted in rising energy consumption, significant heat generation, and a growing environmental footprint. This research focuses on advancing green cloud computing by examining methods that reduce power usage while maintaining high performance. Key strategies include improved virtualization techniques, smarter resource scheduling, and the use of thermal energy recovery systems to repurpose excess heat. In addition, the study highlights the increasing relevance of edge computing as a complementary approach to achieving sustainability. By processing data closer to end devices, edge systems help decrease latency, lower network congestion, and reduce reliance on energy-intensive centralized data centers. To further enhance efficiency, the paper explores various load balancing methods, with particular attention to greedy-based algorithms designed to distribute workload intelligently across edge servers. These techniques support optimal resource allocation, minimize processing delays, and ensure smoother network operations, contributing to a more sustainable and responsive computing environment.

Keywords

  • Cloud-Fog computing
  • Internet of things (IoT)
  • Sustainability
  • green computing
  • virtualization

References (14)

  1. Garg SK, Yeo CS, Buyya R. Green cloud framework for improving carbon efficiency of clouds. In: Jeannot E, Namyst R, Roman J, editors. Euro-Par 2011 Parallel Processing. Lecture Notes in Computer Science. Vol. 6852. Berlin (DE): Springer; 2011. p. 491–502. doi:10.1007/978-3-642- 23400-2_45.
  2. Raval A. (2018). What edge computing means for Infrastructure and Operations leaders. [online] Express Computer. Available from: https://www.expresscomputer.in/news/what-edge-computing- means-for-infrastructure-and-operations-leaders/24531/
  3. DataReportal. (2025). Digital 2026: Global Overview Report. [online] DataReportal – Global Digital Insights. Available from: https://datareportal.com/reports/digital-2026-global-overview- report
  4. Tyagi AK, Cherian AK, Tiwari S. Green Cloud Computing. Advances in Mechatronics and Mechanical Engineering. 2024:226-252. doi:10.4018/979-8-3693-5247-2.ch012
  5. Atta-ur-Rahman, Dash S, Ahmad M, Iqbal T. Mobile Cloud Computing: A Green Perspective. Lecture Notes in Networks and Systems. 2021:523-533. doi:10.1007/978-981-33-6081-5_46
  6. Heidari A, Navimipour NJ, Jamali MAJ, Akbarpour S. A green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios. Sustainable Computing: Informatics and Systems. 2023;38:100859. doi:10.1016/j.suscom.2023.100859
  7. Haseeb K, Ud Din I, Almogren A, Ahmed I, Guizani M. Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things. Sustainable Cities and Society. 2021;68:102779. doi:10.1016/j.scs.2021.102779
  8. Cao K, Hu S, Shi Y, Colombo A, Karnouskos S, Li X. A Survey on Edge and Edge-Cloud Computing Assisted Cyber-Physical Systems. IEEE Transactions on Industrial Informatics. 2021;17(11):7806-7819. doi:10.1109/tii.2021.3073066
  9. Ma H, Huang P, Zhou Z, Zhang X, Chen X. GreenEdge: Joint Green Energy Scheduling and Dynamic Task Offloading in Multi-Tier Edge Computing Systems. IEEE Transactions on Vehicular Technology. 2022;71(4):4322-4335. doi:10.1109/tvt.2022.3147027
  10. Vu Khanh Q, Nguyen VH, Minh QN, Dang Van A, Le Anh N, Chehri A. An efficient edge computing management mechanism for sustainable smart cities. Sustainable Computing: Informatics and Systems. 2023;38:100867. doi:10.1016/j.suscom.2023.100867
  11. Mohammadzadeh A, Masdari M, Gharehchopogh FS, Jafarian A. Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing. Evolutionary Intelligence. 2020;14(4):1997-2025. doi:10.1007/s12065-020-00479-5
  12. Rahimikhanghah A, Tajkey M, Rezazadeh B, Rahmani AM. Resource scheduling methods in cloud and fog computing environments: a systematic literature review. Cluster Computing. 2021;25(2):911-945. doi:10.1007/s10586-021-03467-1
  13. Zhang Y, Zhang CH, Shao X. User preference-aware navigation for mobile robot in domestic via defined virtual area. Journal of Network and Computer Applications. 2021;173:102885. doi:10.1016/j.jnca.2020.102885
  14. Rahman M, Iqbal S, Gao J. Load Balancer as a Service in Cloud Computing. 2014 IEEE 8th International Symposium on Service Oriented System Engineering. 2014:204-211. doi:10.1109/sose.2014.31