Multi-access Edge Computing (MEC) is an emerging computing paradigm that
extends cloud computing to the network edge (e.g., base stations, MEC servers)
to support resource-intensive applications on mobile devices. As a crucial
problem in MEC, service migration needs to decide where to migrate user
services for maintaining high Quality-of-Service (QoS), when users roam between
MEC servers with limited coverage and capacity. However, finding an optimal
migration policy is intractable due to the highly dynamic MEC environment and
user mobility. Many existing works make centralized migration decisions based
on complete system-level information, which can be time-consuming and suffer
from the scalability issue with the rapidly increasing number of mobile users.
To address these challenges, we propose a new learning-driven method, namely
Deep Recurrent Actor-Critic based service Migration (DRACM), which is
user-centric and can make effective online migration decisions given incomplete
system-level information. Specifically, the service migration problem is
modeled as a Partially Observable Markov Decision Process (POMDP). To solve the
POMDP, we design an encoder network that combines a Long Short-Term Memory
(LSTM) and an embedding matrix for effective extraction of hidden information.
We then propose a tailored off-policy actor-critic algorithm with a clipped
surrogate objective for efficient training. Results from extensive experiments
based on real-world mobility traces demonstrate that our method consistently
outperforms both the heuristic and state-of-the-art learning-driven algorithms,
and achieves near-optimal results on various MEC scenarios
