An Empirical Study on How Architectural Topology Affects Microservice Performance and Energy Usage

• URL: http://arxiv.org/abs/2604.00080v1
• Date: Tue, 31 Mar 2026 17:14:41 GMT
• Title: An Empirical Study on How Architectural Topology Affects Microservice Performance and Energy Usage
• Abstract summary: This study quantifies the impact of topologies on energy efficiency and performance across six canonical ones.<n>Probabilistic and Parallel Fan-Out emerging as the most energy-efficient under dense-bound loads.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microservice architectures form the backbone of modern software systems for their scalability, resilience, and maintainability, but their rise in cloud-native environments raises energy efficiency concerns. While prior research addresses microservice decomposition and placement, the impact of topology, the structural arrangement and interaction pattern among services, on energy efficiency remains largely underexplored. This study quantifies the impact of topologies on energy efficiency and performance across six canonical ones (Sequential Fan-Out, Parallel Fan-Out, Chain, Hierarchical, Probabilistic, Mesh), each instantiated at 5-, 10-, and 20-service scales using the $μ\text{Bench}$ framework. We measure throughput, response time, energy usage, CPU utilization, and failure rates under an identical workload. The results indicate that topology influences the energy efficiency of microservices under the studied conditions. As system size increases, energy consumption grows, with the steepest rise observed in dense Mesh and Chain topologies. Mesh topologies perform worst overall, with low throughput, long response times, and high failure rates. Hierarchical, Chain, and Fan-Out designs balance performance and energy use better. As systems scale, metrics converge, with Probabilistic and Parallel Fan-Out emerging as the most energy-efficient under CPU-bound loads. These results guide greener microservice architecture design and serve as a baseline for future research on workload and deployment impacts.

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