Heterogeneous Resources Adaptive Co-Optimization in Edge Networks
Yihong Yang, Zhangbing Zhou, Lin Meng · 2025
The heterogeneous resources co-optimization in edge networks is essential to enhance the network throughput. Existing load-sensitive (re-)scheduling approaches mostly formulate the heterogeneous resources balancing as a single-objective optimization issue, omitting the balanced usage of heterogeneous resources on a given edge node. Moreover, these approaches are inadequate for the heterogeneous resources adaptive cooptimization, microservice dependency modeling at a more granular level, and multi-step online re-scheduling. Thus, a Dependency-aware Online Microservice re-Scheduling (DOMS) approach is introduced. In particular, we formulate the microservice re-scheduling as a multiple knapsack optimization issue, and solve it through the Double Dueling Deep Q-Network (D3QN) with prioritized experience replay. Our DOMS incorporates a heterogeneous resources adaptive balancing detection algorithm to enable adaptive co-optimization of heterogeneous resources. A fine-grained dependency graph of microservice performance metrics is built, upon which a multi-step scheduling partition algorithm is devised to facilitate multi-step online re-scheduling. Extensive experiments on a public dataset show that DOMS outperforms comparison approaches in terms of latency, energy consumption, balance degree, and throughput.