Autoscaling is critical for ensuring optimal performance and resource utilization in cloud applications with dynamic workloads. However, traditional autoscaling technologies are typically no longer applicable in microservice-based applications due to the diverse workload patterns and complex interactions between microservices. Specifically, the propagation of performance anomalies through interactions leads to a high number of abnormal microservices, making it difficult to identify the root performance bottlenecks (PBs) and formulate appropriate scaling strategies. In addition, to balance resource consumption and performance, the existing mainstream approaches based on online optimization algorithms require multiple iterations, leading to oscillation and elevating the likelihood of performance degradation. To tackle these issues, we propose PBScaler, a bottleneck-aware autoscaling framework designed to prevent performance degradation in a microservice-based application. The key insight of PBScaler is to locate the PBs. Thus, we propose TopoRank, a novel random walk algorithm based on the topological potential to reduce unnecessary scaling. By integrating TopoRank with an offline performance-aware optimization algorithm, PBScaler optimizes replica management without disrupting the online application. Comprehensive experiments demonstrate that PBScaler outperforms existing state-of-the-art approaches in mitigating performance issues while conserving resources efficiently.

中文翻译：

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PBScaler：基于微服务的应用程序的瓶颈感知自动缩放框架

自动缩放对于确保具有动态工作负载的云应用程序的最佳性能和资源利用率至关重要。然而，由于不同的工作负载模式和微服务之间复杂的交互，传统的自动缩放技术通常不再适用于基于微服务的应用程序。具体来说，通过交互传播性能异常会导致大量异常微服务，从而难以识别根本性能瓶颈（PB）并制定适当的扩展策略。此外，为了平衡资源消耗和性能，现有基于在线优化算法的主流方法需要多次迭代，导致振荡并增加性能下降的可能性。为了解决这些问题，我们提出了 PBScaler，这是一种瓶颈感知自动缩放框架，旨在防止基于微服务的应用程序中的性能下降。 PBScaler 的关键洞察是定位 PB。因此，我们提出了 TopoRank，一种基于拓扑潜力的新型随机游走算法，以减少不必要的缩放。通过将 TopoRank 与离线性能感知优化算法集成，PBScaler 可以在不中断在线应用程序的情况下优化副本管理。综合实验表明，PBScaler 在缓解性能问题、同时有效节省资源方面优于现有的最先进方法。