探索在云中部署微服务架构的可持续替代方案
Exploring sustainable alternatives for the deployment of microservices architectures in the cloud
AI总结：
本文提出一种基于 NSGA-II 遗传算法的新方法，自动生成微服务云部署方案，以优化应用性能、部署成本与功耗间的最佳权衡，并通过 Train Ticket 案例验证了其有效性。
摘要：
随着组织日益将其应用迁移至云，微服务架构的优化对于实现可持续性目标变得势在必行。然而，可持续部署可能会增加成本并降低性能，因此在这些相互冲突的需求之间识别最佳权衡是一个不易实现的关键目标。本文提出了一种新方法，通过针对应用性能、部署成本和功耗的最佳组合来支持微服务架构的云部署。通过利用遗传算法，特别是 NSGA-II，我们自动化生成替代的架构部署方案。结果通过对 Train Ticket 案例研究的全面评估，展示了本方法的潜力。
英文摘要：
As organizations increasingly migrate their applications to the cloud, the optimization of microservices architectures becomes imperative for achieving sustainability goals. Nonetheless, sustainable deployments may increase costs and deteriorate performance, thus the identification of optimal tradeoffs among these conflicting requirements is a key objective not easy to achieve. This paper introduces a novel approach to support cloud deployment of microservices architectures by targeting optimal combinations of application performance, deployment costs, and power consumption. By leveraging genetic algorithms, specifically NSGA-II, we automate the generation of alternative architectural deployments. The results demonstrate the potential of our approach through a comprehensive assessment of the Train Ticket case study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
