Research on Task Offloading and Resource Allocation Algorithms in Cloud-edge-end Collaborative Computing for the Internet of Things

SHI Jianfeng; CHEN Xinyang; LI Baolong

doi:10.11999/JEIT240659

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SHI Jianfeng, CHEN Xinyang, LI Baolong. Research on Task Offloading and Resource Allocation Algorithms in Cloud-edge-end Collaborative Computing for the Internet of Things[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240659

Citation:

SHI Jianfeng, CHEN Xinyang, LI Baolong. Research on Task Offloading and Resource Allocation Algorithms in Cloud-edge-end Collaborative Computing for the Internet of Things[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240659

Citation:

SHI Jianfeng, CHEN Xinyang, LI Baolong. Research on Task Offloading and Resource Allocation Algorithms in Cloud-edge-end Collaborative Computing for the Internet of Things[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240659

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Research on Task Offloading and Resource Allocation Algorithms in Cloud-edge-end Collaborative Computing for the Internet of Things

doi: 10.11999/JEIT240659

School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 214442, China

Funds: The National Natural Science Foundation of China (62201274, 62201275), The Natural Science Foundation of Jiangsu Province (BK20210641)

Received Date: 2024-07-26
Rev Recd Date: 2024-12-12

Available Online: 2024-12-17

Abstract

Abstract

To meet the latency and energy consumption requirements of Internet of Things(IoT) devices in remote and disaster areas, a new dynamic satellite-assisted IoT network model consisting of IoT devices, Low Earth orbit(LEO) satellites, and a cloud computing center is constructed. Under the practical constraints such as latency, energy consumption etc, the weighted sum of system latency and energy consumption is regarded as cost, and the joint task offloading and power and computational resource allocation problem for minimizing system cost is constructed. For dynamic task arrival scenarios, a Model-assisted Adaptive Deep Reinforcement Learning (MADRL) algorithm is proposed for the joint allocation of task-offloading decisions, communication and computational resources. The algorithm solves the problem in two parts, the first part optimizes the communication, and computational resources through model-assisted, binary search algorithms and gradient descent method, and the second part trains the Q-network to adapt to random task arrivals and make optimal offloading decisions through adaptive deep reinforcement learning algorithms. Simulation results show that the introduction of satellite mobility reduces the system cost by 41%. The introduction of the inter-satellite cooperation technique reduces the system cost by 22.1%. Moreover, the algorithm proposed in this paper has good convergence performance, which reduces the system cost by 3% compared to the benchmark algorithm, and has optimal performance in different environments.
- Cloud-edge-end collaborative computing,
- Satellite-assited Internet of Things(SIoT),
- Deep Reinforcement Learning(DRL),
- Task offloading,
- Resource allocation.

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References(35)

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