具有QoS保证的实用高效微服务自动扩展
Practical Efficient Microservice Autoscaling with QoS Assurance
摘要：
云应用程序越来越多地从单一服务转向基于敏捷微服务的部署。然而，由于松散耦合和交互组件的数量庞大，微服务的有效资源管理构成了一个重大障碍。各种微服务之间的相互依赖性使得现有的云资源自动扩展技术无效。同时，基于机器学习（ML）的方法试图捕捉微服务中的复杂关系，需要大量的训练数据，并导致故意违反SLO。此外，这些ML繁重的方法在适应动态变化的微服务操作环境方面很慢。在本文中，我们提出了PEMA（实用高效微服务自动缩放），一个轻量级的微服务资源管理器，通过机会主义的资源减少找到有效的资源分配。PEMA的轻量级设计实现了新颖的工作负载感知和自适应资源管理。使用三个原型微服务实现，我们表明PEMA可以找到高效的资源分配，并与基于商业规则的资源分配相比节省高达33%的资源。
英文摘要：
Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscaling techniques ineffective. Meanwhile, machine learning (ML) based approaches that try to capture the complex relationships in microservices require extensive training data and cause intentional SLO violations. Moreover, these ML-heavy approaches are slow in adapting to dynamically changing microservice operating environments. In this paper, we propose PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA's lightweight design enables novel workload-aware and adaptive resource management. Using three prototype microservice implementations, we show that PEMA can find efficient resource allocation and save up to 33% resource compared to the commercial rule-based resource allocations.
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。
