A Meta Reinforcement Learning-based Scheme for Adaptive Service Placement in Edge Computing

Service placement constitutes a prominent subject in edge computing, given its pivotal role in mitigating service latency. However, conventional heuristic placement strategies encounter difficulties in coping with the intricate dynamics of constantly evolving real-world settings, attributable to the non-stationary nature of service demand and time-varying network conditions. To tackle this challenge, deep reinforcement learning (DRL) techniques have received substantial attention. These approaches harness dynamic edge environments, encompassing cloud, edge, terminal devices, and wireless communication channels, to acquire placement policies through interactive learning. Nevertheless, a significant drawback of these learning-based approaches lie in their frequent necessity for retraining when confronted with novel edge environments, thereby resulting in inefficiencies in dynamic service placement across heterogeneous environments. To overcome this limitation, this paper proposes an innovative recurrent neural network (RNN)-based meta-reinforcement learning (meta-RL) technique. This technique capitalizes on prior historical experiences to expedite the learning of new policies in unfamiliar situations. Extensive simulation results are presented to illustrate the commendable performance of our proposed scheme.