Layer-Aware Containerized Microservice Scheduling via Multi-Objective Proximal Policy Optimization in Edge Computing Enabled IoT

Containerized microservice (MS) architecture has emerged as the preferred solution for increasingly complex IoT applications in edge computing (EC). However, containerized MS’s runtime requires frequent pulling the layer-based container image and data transfer between dependent nodes, which may incur huge traffic and latency overhead, especially in resource-limited EC-enabled IoT. Additionally, focusing only on reducing such overhead potentially harms load balance, thus degrading service performance. To address these issues, we propose an innovative layer-aware concurrent containerized MS scheduling framework to jointly trade off both consumers’ QoS (latency) and service provider’s profit (load balance). Specifically, we first model the scheduling problem as a multi-objective Markov Decision Process, fully accounting for the latency of each phase in MS’s lifecycle and various perceptible affinities related to the IoT application’s Directed Acyclic Graph. Secondly, a multi-objective DRL algorithm (MO-PPO) is proposed by reconfiguring the Actor-Critic network and devising an empirical trajectory sampling balance mechanism, enabling only a single trained model to approximate the Pareto front. Finally, extensive experiments based on real-world data traces show MO-PPO reduces the service latency by 31.8% and load imbalance by 38.7% at the optimal trade-off point compared to the baselines.