TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling

• URL: http://arxiv.org/abs/2307.07445v1
• Date: Fri, 16 Jun 2023 04:25:59 GMT
• Title: TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling
• Authors: Ke Deng, Zhiyuan He, Hao Zhang, Haohan Lin, Desheng Wang
• Abstract summary: In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of multimodal processing data with strong interdependencies. Traditional algorithms are inadequate for real-time scheduling due to their requirement for multiple iterations to derive the optimal scheme. We propose a novel TSNet-SAC based on Transformer, that utilizes algorithms solely to guide the training of TSNet.
• Score: 6.873630624967785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies. However, traditional heuristic algorithms are inadequate for real-time scheduling due to their requirement for multiple iterations to derive the optimal scheme. We propose a novel TSNet-SAC based on Transformer, that utilizes heuristic algorithms solely to guide the training of TSNet. Additionally, a Sliding Augment Component (SAC) is introduced to enhance the robustness and resolve algorithm defects. Furthermore, the Extender component is designed to handle multi-scale training data and provide network scalability, enabling TSNet to adapt to different access scenarios. Simulation demonstrates that TSNet-SAC outperforms existing networks in accuracy and robustness, achieving superior scheduling-making latency compared to heuristic algorithms.

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