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

Year : 2026 | Volume : 17 | Issue : 01 | Page : 17 24
    By

    Dipankar Barui,

  • Raghunath Maji,

  • Subhankar Roy,

  • Satyajit Maiti,

  • Subhadip Goswami,

  1. Assistant Professor, Department of Artificial Intelligence and Machine Learning, St. Thomas’ College of Engineering and Technology, Kolkata, West Bengal, India
  2. Assistant Professor, Department of Computer Science and Engineering, Greater Kolkata College of Engineering and Management, Kolkata, West Bengal, India
  3. Assistant Professor, Department of Computer Science and Engineering, Seacom Engineering College, Kolkata, West Bengal, India
  4. Assistant Professor, Department of Computer Science and Engineering, Swami Vivekananda University, Kolkata, West Bengal, India
  5. Assistant Professor, Department of Computer Science and Technology, JIS College of Engineering, West Bengal, India

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, green computing, Internet of Things (IoT), sustainability, virtualization

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Dipankar Barui, Raghunath Maji, Subhankar Roy, Satyajit Maiti, Subhadip Goswami. Green and Edge-Aware Computing: Rethinking Cloud Infrastructure for Sustainability. Journal of Computer Technology & Applications. 2026; 17(01):17-24.
How to cite this URL:
Dipankar Barui, Raghunath Maji, Subhankar Roy, Satyajit Maiti, Subhadip Goswami. Green and Edge-Aware Computing: Rethinking Cloud Infrastructure for Sustainability. Journal of Computer Technology & Applications. 2026; 17(01):17-24. Available from: https://journals.stmjournals.com/jocta/article=2026/view=237218


References

  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: Opportunities and challenges. In: D L, Nagpal N, Kassarwani N, Varthanan GV, Siano P, editors. A sustainable future with E-Mobility: Concepts, Challenges, and Implementations. Hershey (PA): IGI Global; 2024. p. 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. In: Udgata SK, Sethi S, Srirama SN, editors. Intelligent Systems. Lecture Notes in Networks and Systems. Vol. 185. Singapore: Springer; 2021. p. 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. Sustain Comput Inform Syst. 2023;38:100859. doi:10.1016/j.suscom.2023.100859.
  7. Haseeb K, Ud Din IU, Almogren A, Ahmed I, Guizani M. Intelligent and secure edge-enabled computing model for sustainable cities using green Internet of Things. Sustain Cities Soc. 2021;68:102779. doi:10.1016/j.scs.2021.102779.
  8. Cao K, Hu S, Shi Y, Colombo AW, Karnouskos S, Li X. A survey on edge and edge-cloud computing assisted cyber-physical systems. IEEE Trans Ind Inform. 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 Trans Veh Technol. 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. Sustain Comput Inform Syst. 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. Evol Intell. 2021;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 Comput. 2022;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. J Netw Comput Appl. 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, Oxford, UK. 2014. p. 204–211. doi:10.1109/SOSE.2014.31.
Regular Issue Subscription Original Research
Volume 17
Issue 01
Received 14/10/2025
Accepted 12/12/2025
Published 20/02/2026
Publication Time 129 Days
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