Neural Combinatorial Optimization for Multiobjective Task Offloading in Mobile Edge Computing

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 tra...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 74; no. 7; pp. 10869 - 10880
Main Authors: Xiao, Xiang-Jie, Wang, Yong, Huang, Pei-Qiu, Wang, Kezhi
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Combinatorial analysis
Computation offloading
Computational modeling
Delays
Downlink
Edge computing
encoder-decoder model
Encoders-Decoders
Energy consumption
Finishing
Machine learning
Mobile computing
Mobile edge computing
Multi-access edge computing
multiobjective
Multiple objective analysis
neural combinatorial optimization
Optimization
Preferences
Real time
Real-time systems
Reinforcement learning
Servers
task offloading
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ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary: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-conditioned 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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2025.3546914