Carbon-aware resource allocation and task offloading in EH-assisted edge-cloud systems
- 1International College, Hebei University
- 2Olin Business School, Washington University
- 3Department of Computing Technologies, Swinburne University of Technology
Abstract
With the development of the Internet of Things (IoT) and Information Communication Technology (ICT), a large number of computation-intensive terminal applications have emerged. Multi-access Edge Computing (MEC) is a promising technology to meet terminal devices’ computational and battery-capacity demands, yet it also generates significant energy consumption and carbon emissions. This paper investigates the dynamic task offloading problem in Energy Harvesting (EH)-assisted edge-cloud systems. We aim to minimize the overall carbon emissions of the system while ensuring long-term stability and satisfying multi-user, multi-server constraints by optimizing CPU frequencies, offloaded task sizes, and offloading decisions. Specifically, we formulate the problem as a multi-slot stochastic optimization problem, which is NP-hard. Employing a stochastic optimization framework, we transform and decompose the problem into multiple subproblems that can be solved efficiently. Considering the coupling between offloading decisions and resource allocation, we propose the Carbon-Aware Resource Allocation and Offloading (CARAE) algorithm to obtain an efficient carbon-aware task offloading strategy. A series of theoretical analyses and simulations are conducted to verify that the proposed CARAE algorithm effectively reduces overall carbon emissions while maintaining system performance.
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BibTeX
@article{fu2026carbonaware,
title = {{Carbon-aware resource allocation and task offloading in EH-assisted edge-cloud systems}},
author = {Han Fu and Qiaochu Lu and Yaozong Yang},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62950-0},
url = {https://doi.org/10.1038/s41598-026-62950-0},
note = {CC BY},
}
RIS
TY - JOUR
TI - Carbon-aware resource allocation and task offloading in EH-assisted edge-cloud systems
AU - Han Fu
AU - Qiaochu Lu
AU - Yaozong Yang
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62950-0
UR - https://doi.org/10.1038/s41598-026-62950-0
AB - With the development of the Internet of Things (IoT) and Information Communication Technology (ICT), a large number of computation-intensive terminal applications have emerged. Multi-access Edge Computing (MEC) is a promising technology to meet terminal devices’ computational and battery-capacity demands, yet it also generates significant energy consumption and carbon emissions. This paper investigates the dynamic task offloading problem in Energy Harvesting (EH)-assisted edge-cloud systems. We aim to minimize the overall carbon emissions of the system while ensuring long-term stability and satisfying multi-user, multi-server constraints by optimizing CPU frequencies, offloaded task sizes, and offloading decisions. Specifically, we formulate the problem as a multi-slot stochastic optimization problem, which is NP-hard. Employing a stochastic optimization framework, we transform and decompose the problem into multiple subproblems that can be solved efficiently. Considering the coupling between offloading decisions and resource allocation, we propose the Carbon-Aware Resource Allocation and Offloading (CARAE) algorithm to obtain an efficient carbon-aware task offloading strategy. A series of theoretical analyses and simulations are conducted to verify that the proposed CARAE algorithm effectively reduces overall carbon emissions while maintaining system performance.
SN - 2045-2322
ER -