Microservices have transformed monolithic applications into lightweight, self-contained, and isolated application components, establishing themselves as a dominant paradigm for application development and deployment in public clouds such as Google and Alibaba. Autoscaling emerges as an efficient strategy for managing resources allocated to microservices' replicas. However, the dynamic and intricate dependencies within microservice chains present challenges to the effective management of scaled microservices. Additionally, the centralized autoscaling approach can encounter scalability issues, especially in the management of large-scale microservice-based clusters. To address these challenges and enhance scalability, we propose an innovative distributed resource provisioning approach for microservices based on the Twin Delayed Deep Deterministic Policy Gradient algorithm. This approach enables effective autoscaling decisions and decentralizes responsibilities from a central node to distributed nodes. Comparative results with state-of-the-art approaches, obtained from a realistic testbed and traces, indicate that our approach reduces the average response time by 15% and the number of failed requests by 24%, validating improved scalability as the number of requests increases.

翻译：微服务已将单体应用转变为轻量级、自包含且隔离的应用组件，成为谷歌和阿里巴巴等公有云中应用开发与部署的主导范式。自动扩缩容作为管理微服务副本分配资源的一种高效策略应运而生。然而，微服务链内部动态且复杂的依赖关系对扩缩后微服务的有效管理提出了挑战。此外，集中式自动扩缩方法可能遇到可扩展性问题，尤其是在管理大规模微服务集群时。为应对这些挑战并提升可扩展性，我们提出了一种基于Twin Delayed Deep Deterministic Policy Gradient算法的创新分布式微服务资源供给方法。该方法能够实现有效的自动扩缩决策，并将职责从中心节点分散至分布式节点。通过在真实测试平台和追踪数据上与最先进方法进行对比，结果表明我们的方法将平均响应时间降低了15%，并将失败请求数量减少了24%，验证了在请求数量增加时系统可扩展性的提升。