Energy-latency-aware microservice orchestration in edge computing via node ranking matrix and proportional routing

The deployment of the microservice architecture in edge networks presents new opportunities for supporting latency-sensitive network services. However, most such services are both computation-intensive and energy-consuming, posing significant challenges for edge nodes with constrained computing resources and energy supply. Therefore, designing efficient microservice orchestration strategies to reduce service latency and network energy consumption is essential but highly challenging. Due to frequent communication among microservices, service deployment and request routing are tightly coupled, which lead to a complex joint optimization problem. This complexity further increases when considering large-scale microservices under multi-instance modeling and fine-grained analysis. Nevertheless, previous work has failed to address these challenges and largely overlooked the balance between latency and energy consumption. To overcome these issues, this paper proposes an energy-latency balanced microservice orchestration method to jointly minimize service latency and energy usage. First, we adopt multi-instance modeling to enable precise end-to-end latency analysis, and integrate an energy model to quantify overall network consumption. Then, we design the Node Ranking Matrix-based Microservice Orchestration Algorithm (NRMA), which dynamically selects high-ranking nodes based on centrality and energy metrics, thereby balancing latency and energy in the deployment stage. Moreover, we use the proportional routing strategy that distributes user request traffic according to the number of deployed instances, preventing node overload and reducing cross-node communication. Experimental results show that the proposed method is significantly better than the baseline algorithms in terms of latency and energy consumption, and achieves significant results.