As Internet applications continue to scale up, microservice architecture has become increasingly popular due to its flexibility and logical structure. Anomaly detection in traces that record inter-microservice invocations is essential for diagnosing system failures. Deep learning-based approaches allow for accurate modeling of structural features (i.e., call paths) and latency features (i.e., call response time), which can determine the anomaly of a particular trace sample. However, the point-wise manner employed by these methods results in substantial system detection overhead and impracticality, given the massive volume of traces (billion-level). Furthermore, the point-wise approach lacks high-level information, as identical sub-structures across multiple traces may be encoded differently. In this paper, we introduce the first Group-wise Trace anomaly detection algorithm, named GTrace. This method categorizes the traces into distinct groups based on their shared sub-structure, such as the entire tree or sub-tree structure. A group-wise Variational AutoEncoder (VAE) is then employed to obtain structural representations. Moreover, the innovative "predicting latency with structure" learning paradigm facilitates the association between the grouped structure and the latency distribution within each group. With the group-wise design, representation caching, and batched inference strategies can be implemented, which significantly reduces the burden of detection on the system. Our comprehensive evaluation reveals that GTrace outperforms state-of-the-art methods in both performances (2.64% to 195.45% improvement in AUC metrics and 2.31% to 40.92% improvement in best F-Score) and efficiency (21.9x to 28.2x speedup). We have deployed and assessed the proposed algorithm on eBay's microservices cluster, and our code is available at https://github.com/NetManAIOps/GTrace.git.

The microservice architecture is widely employed in large Internet systems. For each user request, a few of the microservices are called, and a trace is formed to record the tree-like call dependencies among microservices and the time consumption at each call node. Traces are useful in diagnosing system failures, but their complex structures make it difficult to model their patterns and detect their anomalies. In this paper, we propose a novel dual-variable graph variational autoencoder (VAE) for unsupervised anomaly detection on microservice traces. To reconstruct the time consumption of nodes, we propose a novel dispatching layer. We find that the inversion of negative log-likelihood (NLL) appears for some anomalous samples, which makes the anomaly score infeasible for anomaly detection. To address this, we point out that the NLL can be decomposed into KL-divergence and data entropy, whereas lower-dimensional anomalies can introduce an entropy gap with normal inputs. We propose three techniques to mitigate this entropy gap for trace anomaly detection: Bernoulli & Categorical Scaling, Node Count Normalization, and Gaussian Std-Limit. On five trace datasets from a top Internet company, our proposed TraceVAE achieves excellent F-scores.

Microservice systems are highly dynamic and complex. For such systems, operation engineers and developers highly rely on trace analysis to understand architectures and diagnose various problems such as service failures and quality degradation. However, the huge number of traces produced at runtime makes it challenging to capture the required information in real-time. To address the faced challenges, in this paper, we propose a graph-based microservice trace analysis approach GMTA for understanding architecture and diagnosing various problems. Built on a graph-based representation, GMTA includes efficient processing of traces produced on the fly. It abstracts traces into different paths and further groups them into business flows. To support various analytical applications, GMTA includes an efficient storage and access mechanism by combining a graph database and a real-time analytics database and using a carefully designed storage structure. Based on GMTA, we construct analytical applications for architecture understanding and problem diagnosis, these applications support various needs such as visualizing service dependencies, making architectural decisions, analyzing the changes of services behaviors, detecting performance issues, and locating root causes. GMTA has been implemented and deployed in eBay. An experimental study based on trace data produced by eBay demonstrates GMTA's effectiveness and efficiency for architecture understanding and problem diagnosis. Case studies conducted in eBay's monitoring team and Site Reliability Engineering (SRE) team further confirm GMTA's substantial benefits in industrial-scale microservice systems.