Dependency-aware online microservice re-scheduling for adaptive resources co-optimization in edge networks

The usage of heterogeneous resources provisioned by edge nodes can be co-optimized through re-scheduling microservices. Current (re-)scheduling approaches typically treat the task of co-optimization as a single-objective optimization problem, which cannot address the issue of imbalanced usage of heterogeneous resources (e.g., CPU, memory, bandwidth) on a single edge node. More importantly, these approaches are inadequate in handling: (i) the adaptive co-optimization of heterogeneous resources, (ii) the fine-grained construction of microservice dependencies, and (iii) multi-step online microservice re-scheduling. To address these challenges, this article proposes a Dependency-aware Online Microservice re-Scheduling (DOMS) approach. DOMS formulates microservice re-scheduling as a multi-knapsack optimization problem and solves it using a Double Dueling Deep Q-Network (D3QN) with prioritized experience replay. Specifically, an adaptive heterogeneous resources balancing detection algorithm is developed, incorporating a dynamic detection threshold mechanism. A fine-grained microservice performance metrics dependency graph is constructed by capturing causal relationships to represent sequential execution dependency. Based on this graph, a microservice multi-step scheduling partition algorithm is devised. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that DOMS outperforms the state-of-the-art techniques with improvements of at least 1.85%, 6.45%, 0.56%, and 3.18% in terms of latency, energy consumption, balance degree, and throughput. These results highlight the effectiveness and superiority of DOMS in maintaining a balanced usage of heterogeneous resources and improving network throughput, while satisfying latency and energy consumption constraints.