The performance of large-scale systems must be thoroughly tested under various levels of workload, as load-related issues can have a disastrous impact on the system. However, load testing often requires a large amount of time, running from hours to even days. In our prior work, we reduced the execution time of a load test by detecting repetitiveness in individual performance metric values, such as CPU utilization, that are observed during the test. However, as we explain in this paper, disregarding combinations of performance metrics may miss important information about the load-related behavior of a system. In this paper we revisit our prior approach, by proposing an approach that reduces the execution time of a load test by detecting whether a test no longer exercises new combinations of the observed performance metrics. We study three open source systems, in which we use our new and prior approaches to reduce the execution time of a 24-hour load test. We show that our new approach is capable of reducing the execution time of the test to less than 8.5 hours, while preserving a coverage of at least 95% of the combinations that are observed between the performance metrics during the 24-hour tests.

中文翻译：

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减少负载测试所需的时间

大型系统的性能必须在各种级别的工作负载下进行彻底测试，因为与负载相关的问题可能会对系统造成灾难性的影响。然而，负载测试通常需要大量时间，从几小时到几天不等。在我们之前的工作中，我们通过检测测试期间观察到的各个性能指标值（例如 CPU 利用率）的重复性来减少负载测试的执行时间。然而，正如我们在本文中所解释的那样，忽略性能指标的组合可能会错过有关系统负载相关行为的重要信息。在本文中，我们通过提出一种方法来重新审视我们之前的方法，该方法通过检测测试是否不再使用观察到的性能指标的新组合来减少负载测试的执行时间。我们研究了三个开源系统，在这些系统中我们使用我们新的和以前的方法来减少 24 小时负载测试的执行时间。我们表明，我们的新方法能够将测试的执行时间减少到不到 8.5 小时，同时保留至少 95% 的组合覆盖率，这些组合在 24 小时测试期间的性能指标之间观察到。