Energy-aware edge computing resource scheduling method

Guangping Zhu; Qiang Li; Wenjing Li; Dongdong Lv; Yongshan Guo

doi:10.1117/12.3011803

7 December 2023 Energy-aware edge computing resource scheduling method

Guangping Zhu, Qiang Li, Wenjing Li, Dongdong Lv, Yongshan Guo

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294118 (2023) https://doi.org/10.1117/12.3011803
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Currently, various energy sources such as electricity, heat, gas, and water in cities are relatively independently supplied, making it difficult to meet the demand for integrated energy utilization in new smart cities. The urban energy supply structure urgently needs to evolve from single transmission to gradual integration and coupling of multiple flows, which highlights the pressing need to enhance capabilities such as accurate perception of energy status data, trustworthy fusion and sharing, and precise energy efficiency services. These capabilities will greatly contribute to the improvement of overall urban energy efficiency and economic structure optimization. This article primarily focuses on the scheduling of computational resources between edge servers and terminal devices. Edge computing reduces data transmission latency and enhances computational agility during the computation offloading process. However, the computational capacity of edge servers is also limited, and in certain specific computation offloading scenarios (such as ultra-dense networks), interference may arise, leading to unexpected transmission delays. Therefore, it is not advisable to offload all tasks for execution on edge servers; some tasks should be handled by the terminal devices (SMD). Despite consuming more energy through local execution, this approach eliminates the need to consider data transmission time, thus significantly boosting task responsiveness. This article aims to design a task offloading scheme that is aware of terminal energy consumption and involves scheduling computational resources between edge servers and terminal devices.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guangping Zhu, Qiang Li, Wenjing Li, Dongdong Lv, and Yongshan Guo "Energy-aware edge computing resource scheduling method", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294118 (7 December 2023); https://doi.org/10.1117/12.3011803

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KEYWORDS

Data transmission

Batteries

Mathematical optimization

Cloud computing

Fusion energy

Internet of things

Lithium

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