CECTO: Cloud-Edge Collaborated Task Offloading Based on Dual Time-Scale A2C
Optimizing task offloading in the context of Power IoT (PIoT) through cloud-edge collaboration is crucial for smart grid development. This paper combines the most advanced Space-Air-Ground Internet (SAGIN) with PIoT and proposes a cloud-edge collaborated computing scheme namely CECTO based on the Double Time-Scale Advantage Actor-Critic (DTS-A2C) algorithm. It decouples the collaborated computing model into subproblems using the Lyapunov method. For the task offloading, it applies the DTS-A2C algorithm to optimize the data transmission power on a small time-scale and task offloading decisions on a large time-scale. Experimental results demonstrate that the CECTO method can significantly reduce task delays and device energy consumption.
Authors
- Yu Chen (ORCID: https://orcid.org/0009-0008-1833-0645)
- Shiping Chen (ORCID: https://orcid.org/0000-0002-4603-0024)
- Lina Ni (ORCID: https://orcid.org/0000-0003-2917-8198)
- Jiehan Zhou (ORCID: https://orcid.org/0000-0002-4026-1649)
Institutions
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Data61 (AU)
- Huazhong University of Science and Technology (CN)
- Shandong University of Science and Technology (CN)
Publication Details
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1109/cbd65573.2024.00078
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00