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Mobility-Aware Deep Reinforcement Learning With Seq2seq Mobility Prediction for Offloading and Allocation in Edge Computing
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AI
DOI：10.1109/TMC.2023.3328996.png)
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Abstract
En 中文
Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading decision and resource allocation need to be jointly handled to optimize the service provision efficiency within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision in the MEC system. M-DRL is composed of two parts: glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a glimpse of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements.
Keywords:
Predictive models
Computational modeling
Resource management
Prediction algorithms
Servers
Edge computing
Analytical models
Deep reinforcement learning
seq2seq model
edge computing
mobility
offloading decision
virtual machine migration
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