Microservices are becoming the defining paradigm of cloud applications, which raises urgent challenges for efficient datacenter management. Guaranteeing end-to-end Service Level Agreement (SLA) while optimizing resource allocation is critical to both cloud service providers and users. However, one application may contain hundreds of microservices, which constitute an enormous search space that is unfeasible to explore exhaustively. Thus, we propose RAMBO, an SLA-aware framework for microservices that leverages multi-objective Bayesian Optimization (BO) to allocate resources and meet performance/cost goals. Experiments conducted on a real microservice workload demonstrate that RAMBO can correctly characterize each microservice and efficiently discover Pareto-optimal solutions. We envision that the proposed methodology and results will benefit future resource planning, cluster orchestration, and job scheduling.

中文翻译：

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RAMBO：使用贝叶斯优化的微服务资源分配

微服务正在成为云应用程序的定义范例，这为有效的数据中心管理提出了紧迫的挑战。在优化资源分配的同时，确保端到端服务水平协议（SLA）对于云服务提供商和用户都是至关重要的。但是，一个应用程序可能包含数百个微服务，这些微服务构成了巨大的搜索空间，无法穷举地进行探索。因此，我们提出了RAMBO，这是一种微服务的SLA感知框架，它利用多目标贝叶斯优化（BO）来分配资源并满足性能/成本目标。在实际的微服务工作负载上进行的实验表明，RAMBO可以正确表征每个微服务并有效地发现Pareto最优解决方案。