Topology-aware Microservice Architecture in Edge Networks: Deployment Optimization and Implementation
Yuang Chen, Chang Wu, Fangyu Zhang, Chengdi Lu, Yongsheng Huang, Hancheng Lu
TL;DR
The paper tackles latency-sensitive microservice deployment in edge networks by introducing a topology-aware three-tier traffic model that maps microservice deployments to edge-link traffic. It formulates a weighted-sum delay optimization problem and proposes the TAIA-MD scheme, which uses topology sensing and an individual-adaptive genetic algorithm to accelerate convergence and avoid local optima. Extensive simulations and a physical DMSA platform validation demonstrate that TAIA-MD reduces communication delay by 30%–60% and enhances robustness against link failures and network fluctuations. The work advances edge MSAs by integrating topology-aware traffic analysis with adaptive search, enabling practical, robust deployment in heterogeneous, bandwidth-constrained edge environments.
Abstract
As a ubiquitous deployment paradigm, integrating microservice architecture (MSA) into edge networks promises to enhance the flexibility and scalability of services. However, it also presents significant challenges stemming from dispersed node locations and intricate network topologies. In this paper, we have proposed a topology-aware MSA characterized by a three-tier network traffic model encompassing the service, microservices, and edge node layers. This model meticulously characterizes the complex dependencies between edge network topologies and microservices, mapping microservice deployment onto link traffic to accurately estimate communication delay. Building upon this model, we have formulated a weighted sum communication delay optimization problem considering different types of services. Then, a novel topology-aware and individual-adaptive microservices deployment (TAIA-MD) scheme is proposed to solve the problem efficiently, which accurately senses the network topology and incorporates an individual-adaptive mechanism in a genetic algorithm to accelerate the convergence and avoid local optima. Extensive simulations show that, compared to the existing deployment schemes, TAIA-MD improves the communication delay performance by approximately 30% to 60% and effectively enhances the overall network performance. Furthermore, we implement the TAIA-MD scheme on a practical microservice physical platform. The experimental results demonstrate that TAIA-MD achieves superior robustness in withstanding link failures and network fluctuations.