Research Article
Computation Offloading and Resource Allocation for Edge Intelligence: A Deep Reinforcement Learning Solution
@INPROCEEDINGS{10.1007/978-3-031-60347-1_16, author={Xiao Chen and Hongbing Qiu and Yanlong Li}, title={Computation Offloading and Resource Allocation for Edge Intelligence: A Deep Reinforcement Learning Solution}, proceedings={Mobile Multimedia Communications. 16th EAI International Conference, MobiMedia 2023, Guilin, China, July 22-24, 2023, Proceedings}, proceedings_a={MOBIMEDIA}, year={2024}, month={10}, keywords={Artificial Intelligence Computation Offloading Resource Allocation Markov Process}, doi={10.1007/978-3-031-60347-1_16} }- Xiao Chen
Hongbing Qiu
Yanlong Li
Year: 2024
Computation Offloading and Resource Allocation for Edge Intelligence: A Deep Reinforcement Learning Solution
MOBIMEDIA
Springer
DOI: 10.1007/978-3-031-60347-1_16
Abstract
The artificial intelligence training of the terminal clients is mostly limited by the insufficient computing power and energy shortage of the client itself, as well as the client’s offline and malfunction, which lead to the terminal intelligent training consuming a large amount of system time. A trusted server is introduced in this scenario, which allows the training tasks of terminal clients to be offloaded to the edge server, to solve this problem. Firstly, a joint optimization model of computation offloading and resource allocation for federated training in mobile edge networks is established. Then the optimization problem is transformed into a Markov process, and the DQN algorithm is applied to obtain the optimal decision. Large amounts of simulation results show that the proposed algorithm reduces the training delay of terminal clients effectively.
