In industrial environments it is critical to find out the capacity of a system and plan for a deployment layout that meets the production traffic demands. The system capacity is influenced by both the performance of the system’s constituting components and the physical environment setup. In a large system, the configuration parameters of individual components give the flexibility to developers and load test engineers to tune system performance without changing the source code. However, due to the large search space, estimating the capacity of the system given different configuration values is a challenging and costly process. In this paper, we propose an approach, called MLASP, that uses machine learning models to predict the system key performance indicators (i.e., KPIs), such as throughput, given a set of features made off configuration parameter values, including server cluster setup, to help engineers in capacity planning for production environments. Under the same load, we evaluate MLASP on two large-scale mission-critical enterprise systems developed by Ericsson and on one open-source system. We find that: 1) MLASP can predict the system throughput with a very high accuracy. The difference between the predicted and the actual throughput is less than 1%; and 2) By using only a small subset of the training data (e.g., 3% of the entire data for the open-source system), MLASP can still predict the throughput accurately. We also document our experience of successfully integrating the approach into an industrial setting. In summary, this paper highlights the benefits and potential of using machine learning models to assist load test engineers in capacity planning.

在工业环境中，找出系统容量并规划满足生产流量需求的部署布局至关重要。系统容量受系统构成组件的性能和物理环境设置的影响。在大型系统中，各个组件的配置参数为开发人员和负载测试工程师提供了灵活性，可以在不更改源代码的情况下调整系统性能。然而，由于搜索空间很大，在给定不同配置值的情况下估计系统的容量是一个具有挑战性且成本高昂的过程。在本文中，我们提出了一种方法，称为MLASP，它使用机器学习模型来预测系统关键性能指标（即 KPI），例如吞吐量，给定一组功能配置参数值，包括服务器集群设置，以帮助工程师为生产环境进行容量规划。在相同的负载下，我们在两个由爱立信开发的大型任务关键型企业系统和一个开源系统上评估MLASP。我们发现： 1) MLASP可以非常准确地预测系统吞吐量。预测和实际吞吐量的差异小于1%；和 2) 通过仅使用训练数据的一小部分（例如，开源系统整个数据的 3%），MLASP仍然可以准确预测吞吐量。我们还记录了我们将该方法成功集成到工业环境中的经验。总之，本文重点介绍了使用机器学习模型来帮助负载测试工程师进行容量规划的好处和潜力。