用于控制评估微服务弹性模式的声明式方法和基准工具

Carlos M. Aderaldo, Thiago M. Costa, Davi M. Vasconcelos, Nabor C. Mendonça, Javier Cámara, David Garlan
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引用次数: 0

摘要

微服务开发人员越来越多地使用重试(Retry)和断路器(Circuit Breaker)等弹性模式来应对可能出现故障的远程服务。然而,对于这些弹性模式通常会带来的调用延迟在不同工作负载和故障场景下会如何影响应用程序性能,目前还鲜有研究。本文介绍了一种新方法和基准工具,用于在受控环境中实验性地评估现有弹性模式对性能的影响。这种方法的主要新颖之处在于能够声明式地指定并自动生成涉及不同弹性模式的多个测试场景,人们可以使用任何编程语言和弹性库来实现这些测试场景。文章通过报告在两种主流编程语言(C# 和 Java)中使用两种流行的弹性库(Polly 和 Resilience4j)对重试和断路器弹性模式在多种服务工作负载和故障率下的性能影响进行的实验研究,说明了所提出的方法和工具的优势。我们的研究结果表明,在中低故障率下,两种弹性模式都能有效降低应用程序目标服务的负载,对应用程序的性能几乎没有影响。然而,随着故障率的增加,这两种模式都会明显降低应用程序的性能,其影响因服务的工作量、模式的编程语言和弹性库而异。
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A declarative approach and benchmark tool for controlled evaluation of microservice resiliency patterns
Microservice developers increasingly use resiliency patterns such as Retry and Circuit Breaker to cope with remote services that are likely to fail. However, there is still little research on how the invocation delays typically introduced by those resiliency patterns may impact application performance under varying workloads and failure scenarios. This article presents a novel approach and benchmark tool for experimentally evaluating the performance impact of existing resiliency patterns in a controlled setting. The main novelty of this approach resides in the ability to declaratively specify and automatically generate multiple testing scenarios involving different resiliency patterns, which one can implement using any programming language and resilience library. The article illustrates the benefits of the proposed approach and tool by reporting on an experimental study of the performance impact of the Retry and Circuit Breaker resiliency patterns in two mainstream programming languages (C# and Java) using two popular resilience libraries (Polly and Resilience4j), under multiple service workloads and failure rates. Our results show that, under low to moderate failure rates, both resiliency patterns effectively reduce the load over the application's target service with barely any impact on the application's performance. However, as the failure rate increases, both patterns significantly degrade the application's performance, with their effect varying depending on the service's workload and the patterns' programming language and resilience library.
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