AI 中文总结
该研究通过将四个环境模型构建为容器化微服务工作流，对比不同编排方式与单体执行的能耗，发现微服务对小型模型会增耗，而较大工作流采用事件驱动编排或选择性下游重执行可实现节能。
AI 中文摘要
环境模拟模型支持情景分析、校准与决策，但重复执行会产生大量能源成本。微服务具备模块化与可扩展性，但其低碳影响尚不明确，因为服务分解会引入编排、通信、持久化及闲置服务的开销。本文将四个环境模型评估为容器化微服务工作流，对比单体执行、基于轮询的编排与基于事件的编排。结果显示，微服务会增加小型或紧耦合模型的能耗，此类模型中协调开销占主导；对于较大工作流，基于事件的编排虽运行时间更长但可降低能耗，而选择性下游重执行在重复参数探索期间实现了41%的能耗降低。
英文摘要
Environmental simulation models support scenario analysis, calibration, and decision-making, but repeated execution can incur significant energy costs. Microservices offer modularity and scalability, yet their low-carbon impact remains unclear because decomposition introduces orchestration, communication, persistence, and idle-service overheads. This paper evaluates four environmental models as containerised microservice workflows, comparing monolithic execution with polling-based and event-driven orchestration. Results show that microservices increase energy consumption for smaller or tightly coupled models, where coordination overhead dominates. For a larger workflow, event-driven orchestration reduces energy use despite longer runtime, while selective downstream re-execution achieves a 41% reduction during repeated parameter exploration.
Comments5 pages, 7 figures. Accepted at the 2nd International Workshop on Low Carbon Computing (LOCO 2026), Lancaster University, United Kingdom, 10-11 September 2026. Part of the LOCO 2026 proceedings, arXiv:2608.02072
