A heterogeneous graph attention Q-network is introduced for AISC deployment that reduces completion time while improving load balance and energy use in dynamic UMEC networks.
Cost -Oriented and Delay -Constrained Anycasting for Service Function Chain Provisioning Leveraging Cloud -Edge Collaboration in Space-Air-Ground Integrated Networks,
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AISC deployment in dynamic UAV-assisted MEC network: a reinforcement learning method based on heterogeneous graph attention neural network
A heterogeneous graph attention Q-network is introduced for AISC deployment that reduces completion time while improving load balance and energy use in dynamic UMEC networks.