MINERVA: Performance Prediction of Microservice Applications with Black-box Container Orchestration

MINERVA：使用黑盒容器编排进行微服务应用程序的性能预测

• 批准号: 510552229
• 负责人: Dr. André Bauer
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/510552229?language=en
• 关键词: MINERVA; Performance; Prediction; Microservice; Applications

Modern distributed systems are nowadays typically based on microservice architectures, which are considered a best practice among software developers. Modern microservice applications are commonly deployed in containers managed by a container orchestration framework (e.g., Kubernetes), which provides many self-adaptation features such as autoscaling, intelligent load balancing, circuit breakers, and failure recovery. While such features significantly influence the application performance, the internal mechanisms they use are normally considered a black box from the perspective of application developers. This makes it challenging to understand, quantify, compare, and optimize the performance properties of microservice applications and orchestration frameworks. Questions such as the following arise: How should the adaptation mechanisms be configured to optimize the tradeoff between performance and operating costs? How long would the system take to adapt to a sudden load spike of three times the current load? What would be the expected time-to-recovery and performance degradation if a certain service instance or a node fails? Existing performance prediction approaches can only be used to evaluate the steady-state performance and they normally require the explicit modeling of possible adaptations and adaptation rules. This is infeasible due to the large number of potential adaptations and the fact that many adaptation mechanisms rely on complex machine learning models. With some approaches, the adaptation logic itself may evolve during operation. To the best of our knowledge, there is no model-based performance prediction approach that considers both steady-state and transient phases, while not requiring the explicit modeling of the adaptation logic. The goal of the project is to develop such an approach for microservice applications with black-box container orchestration. It should enable application developers and system operators to answer performance-related questions like the above. The project will target three main goals: (1) modeling formalisms to capture the system aspects relevant for predicting both transient and steady-state performance of modern microservice applications in environments with black-box container orchestration, (2) efficient and scalable algorithms for simulating the performance of a system modeled using the proposed modeling formalisms, and (3) novel analysis algorithms and workflows to analyze and interpret the results obtained through the developed modeling and simulation approach. Further, we will design and implement a novel benchmark as well as metrics for comparing adaptation mechanisms in a standardized manner based on the proposed simulation framework. A series of case studies will be conducted to benchmark different adaptation mechanisms and to individually validate the methods, models, and tools developed under the project as well as to validate the overall proposed approach and its end-to-end performance.

现代分布式系统目前通常基于微服务体系结构，这被认为是软件开发人员的最佳实践。现代微服务应用通常部署在由容器编排框架(如Kubernetes)管理的容器中，该框架提供许多自适应功能，如自动伸缩、智能负载均衡、断路器和故障恢复。虽然这些功能会显著影响应用程序的性能，但从应用程序开发人员的角度来看，它们使用的内部机制通常被认为是一个黑匣子。这使得理解、量化、比较和优化微服务应用程序和协调框架的性能属性变得具有挑战性。出现了如下问题：应如何配置适应机制，以优化绩效和运营成本之间的权衡？系统需要多长时间才能适应三倍于当前负载的突然负载峰值？如果某个服务实例或节点出现故障，预计的恢复时间和性能降级会有多长？现有的性能预测方法只能用于评估稳态性能，通常需要对可能的适应和适应规则进行显式建模。这是不可行的，因为大量的潜在适应和事实是许多适应机制依赖于复杂的机器学习模型。对于某些方法，自适应逻辑本身可能在操作期间演变。就我们所知，没有一种基于模型的性能预测方法可以同时考虑稳态和暂态阶段，而不需要对适应逻辑进行显式建模。该项目的目标是为具有黑盒容器编排的微服务应用程序开发这样一种方法。它应该使应用程序开发人员和系统操作员能够回答上述与性能相关的问题。该项目将以三个主要目标为目标：(1)建模形式，以捕获与预测黑盒容器编排环境中现代微服务应用的瞬时和稳态性能相关的系统方面；(2)高效且可扩展的算法，用于模拟使用所提出的建模形式建模的系统的性能；(3)新的分析算法和工作流，以分析和解释通过所开发的建模和仿真方法所获得的结果。此外，我们将基于所提出的仿真框架设计并实现一种新的基准以及用于以标准化方式比较适应机制的度量。将进行一系列案例研究，以确定不同适应机制的基准，分别验证在该项目下开发的方法、模型和工具，并验证整个拟议办法及其端到端性能。