Energy-Proportional Scheduling for Cloud and Edge Infrastructure

Authors

  • David Miller Author

Keywords:

green computing, cloud scheduling, edge computing, carbon awareness, energy proportionality

Abstract

This review examines energy-proportional scheduling across cloud and edge infrastructure. The organizing question is when workloads should move in time or place without degrading reliability and service equity. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to optimizing reported operational energy while exporting cost or emissions elsewhere. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in sustainable operation of geographically distributed computing.

References

  1. Alsadie, D. (2024). Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects. PeerJ Computer Science, 10, e2128. https://doi.org/10.7717/peerj-cs.2128
  2. Anderson, T., Belay, A., Chowdhury, M., Cidon, A., & Zhang, I. (2023). Treehouse: A Case For Carbon-Aware Datacenter Software. ACM SIGEnergy Energy Informatics Review, 3(3), 64-70. https://doi.org/10.1145/3630614.3630626
  3. Baccarelli, E., Naranjo, P. G. V., Scarpiniti, M., Shojafar, M., & Abawajy, J. H. (2017). Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study. IEEE Access, 5, 9882-9910. https://doi.org/10.1109/access.2017.2702013
  4. Chen, Z., Hu, J., Min, G., Luo, C., & El-Ghazawi, T. (2021). Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning. IEEE Transactions on Parallel and Distributed Systems, 33(8), 1911-1923. https://doi.org/10.1109/tpds.2021.3132422
  5. Hussain, M. M., & Beg, M. M. S. (2019). Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures. Big Data and Cognitive Computing, 3(1), 8. https://doi.org/10.3390/bdcc3010008
  6. Jiang, C., Qiu, Y., Gao, H., Fan, T., Li, K., & Wan, J. (2019). An Edge Computing Platform for Intelligent Operational Monitoring in Internet Data Centers. IEEE Access, 7, 133375-133387. https://doi.org/10.1109/access.2019.2939614
  7. Juarez, F., Ejarque, J., & Badia, R. M. (2016). Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Generation Computer Systems, 78, 257-271. https://doi.org/10.1016/j.future.2016.06.029
  8. Katal, A., Dahiya, S., & Choudhury, T. (2022). Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing, 26(3), 1845-1875. https://doi.org/10.1007/s10586-022-03713-0
  9. Khan, Z. A., Aziz, I. A., Osman, N. A. B., & Ullah, I. (2023). A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions. IEEE Access, 11, 143417-143445. https://doi.org/10.1109/access.2023.3343877
  10. Perin, G., Berno, M., Erseghe, T., & Rossi, M. (2021). Towards Sustainable Edge Computing Through Renewable Energy Resources and Online, Distributed and Predictive Scheduling. IEEE Transactions on Network and Service Management, 19(1), 306-321. https://doi.org/10.1109/tnsm.2021.3112796

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Published

2026-08-05

Issue

Vol. 1 (2026): Archive

Section

Review

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Copyright (c) 2026 Computational Systems & Infrastructure

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This work is licensed under a Creative Commons Attribution 4.0 International License.