Layer-Aware Containerized Microservice Scheduling via Multiobjective 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.