Neural Combinatorial Optimization for Multiobjective Task Offloading in Mobile Edge Computing
Abstract
Task offloading is crucial in supporting resource-intensive applications in mobile edge computing. This paper explores multiobjective task offloading, aiming to minimize energy consumption and latency simultaneously. Although learning-based algorithms have been used to address this problem, they train a model based on one a priori preference to make the offloading decision. When the preference changes, the trained model may not perform well and needs to be retrained. To address this issue, we propose a neural combinatorial optimization method that combines an encoder-decoder model with reinforcement learning. The encoder captures task relationships, while the decoder, equipped with a preference-based attention mechanism, determines offloading decisions for various preferences. Additionally, reinforcement learning is employed to train the encoder-decoder model. Since the proposed method can infer the offloading decision for each preference, it eliminates the need to retrain the model when the preference changes, thus improving real-time performance. Experimental studies demonstrate the effectiveness of the proposed method by comparison with three algorithms on instances of different scales.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U23A20347); Royal Society International Exchange (Grant Number: IEC-NSFC-211264)Similar works
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Last time updated on 07/04/2025
This paper was published in Brunel University Research Archive.
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