论文标题
用于性能调试的总体驱动痕量可视化
Aggregate-Driven Trace Visualizations for Performance Debugging
论文作者
论文摘要
云系统中的性能问题很难调试。分布式跟踪是一种广泛采用的方法，它使工程师对云系统的可见性。现有的跟踪分析方法着重于调试单个请求问题，而不是调试单个请求性能问题。在给定请求中诊断性能问题需要将有问题请求的性能与典型请求的总体绩效进行比较。有效有效的此类问题的调试面临三个挑战：（i）确定正确的综合数据以进行诊断； （ii）可视化汇总数据； （iii）有效收集，存储和处理跟踪数据。 我们提出Travista，该工具旨在在解决这些挑战的单个跟踪中调试性能问题。 Travista通过三种类型的汇总数据（度量，时间和结构数据）扩展了流行的单个跟踪图表可视化，以将所有痕迹的有问题跟踪的性能上下文化。
Performance issues in cloud systems are hard to debug. Distributed tracing is a widely adopted approach that gives engineers visibility into cloud systems. Existing trace analysis approaches focus on debugging single request correctness issues but not debugging single request performance issues. Diagnosing a performance issue in a given request requires comparing the performance of the offending request with the aggregate performance of typical requests. Effective and efficient debugging of such issues faces three challenges: (i) identifying the correct aggregate data for diagnosis; (ii) visualizing the aggregated data; and (iii) efficiently collecting, storing, and processing trace data. We present TraVista, a tool designed for debugging performance issues in a single trace that addresses these challenges. TraVista extends the popular single trace Gantt chart visualization with three types of aggregate data - metric, temporal, and structure data, to contextualize the performance of the offending trace across all traces.
