Carbon-Aware Spatio-Temporal Workload Shifting in Edge–Cloud Environments: A Review and Novel Algorithm

Nasir Asadov (), Vlad C. Coroamă, Matteo Franzil, Stefano Galantino and Matthias Finkbeiner
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Nasir Asadov: Department of Environmental Technology, Technische Universität Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany
Vlad C. Coroamă: Department of Environmental Technology, Technische Universität Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany
Matteo Franzil: Fondazione Bruno Kessler, 38123 Trento, Italy
Stefano Galantino: Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Matthias Finkbeiner: Department of Environmental Technology, Technische Universität Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany

Sustainability, 2025, vol. 17, issue 14, 1-27

Abstract: Due to its rising carbon footprint, new paradigms for carbon-efficient computing are needed. For distributed computing systems, one option is to shift computing loads in space or time to take advantage of low-carbon electricity, a paradigm known as carbon-aware computing. We present a literature review of carbon-aware scheduling techniques, which shows that most of the literature carried out either spatial or temporal shifting but not both. Of the 28 analyzed studies, 11 considered both spatial and temporal shifting, and only 2 developed a combined optimization algorithm. Additionally, existing approaches typically focus on operational electricity alone. With the growing decarbonization of electricity, however, device production (which involves various industrial processes and cannot be easily decarbonized) is bound to become more relevant and needs to be considered. We thus suggest a novel spatio-temporal scheduling algorithm for cloud and edge computing. Our algorithm performs simultaneous spatio-temporal shifting while taking into consideration both device production and operation. As temporal shifting requires forecasts of future workloads, we also put forward a workload predictor. Although not fully implemented yet, we bring various theoretical arguments in support of our proposed algorithm.

Keywords: sustainable computing; workload scheduling; carbon footprint; life cycle assessment (LCA); edge–cloud continuum; artificial intelligence (AI) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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