Energy-Proportional Scheduling for Cloud and Edge Infrastructure

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.

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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 DOI
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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 DOI