Nowadays, microservice architecture has become mainstream way of cloud applications delivery. Distributed tracing is crucial to preserve the observability of microservice systems. However, existing trace representation approaches only concentrate on operations, relationships and metrics related to service invocations. They ignore service events that denotes meaningful, singular point in time during the service's duration. In this paper, we propose iTCRL, a novel trace contrastive representation learning approach based on causal intervention. This approach first constructs a unified graph representation for each trace to describe the runtime status of service events in traces and the complex relationships between them. Then, Causal-intervention-based Trace Contrastive Learning is proposed, which learns trace representations from causal perspective based on the unified graph representations of traces. It uses causal intervention to generate contrastive views, heterogeneous graph neural network-based trace encoder to learn trace representations, and direct causal effect to guide the training of trace encoder. Experimental results on three datasets show that iTCRL outperforms all baselines in terms of trace classification, trace anomaly detection, trace sampling and noise robustness, and also validate the contribution of Causal-intervention-based Trace Contrastive Learning.

中文翻译：

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iTCRL：基于因果干预的微服务系统跟踪对比表示学习


如今，微服务架构已成为云应用程序交付的主流方式。分布式跟踪对于保持微服务系统的可观测性至关重要。但是，现有的跟踪表示方法仅关注与服务调用相关的操作、关系和指标。它们会忽略表示服务持续时间内有意义的单一时间点的服务事件。在本文中，我们提出了 iTCRL，这是一种基于因果干预的新型跟踪对比表示学习方法。这种方法首先为每个跟踪构建一个统一的图形表示，以描述跟踪中服务事件的运行时状态以及它们之间的复杂关系。然后，提出了基于因果干预的跟踪对比学习，基于跟踪的统一图表示，从因果角度学习跟踪表示。它使用因果干预来生成对比视图，使用基于异构图神经网络的跟踪编码器来学习跟踪表示，并使用直接因果效应来指导跟踪编码器的训练。三个数据集上的实验结果表明，iTCRL 在跟踪分类、跟踪异常检测、跟踪采样和噪声鲁棒性方面优于所有基线，并且还验证了基于因果干预的跟踪对比学习的贡献。