TraceMesh: Scalable and Streaming Sampling for Distributed Traces

• URL: http://arxiv.org/abs/2406.06975v1
• Date: Tue, 11 Jun 2024 06:13:58 GMT
• Title: TraceMesh: Scalable and Streaming Sampling for Distributed Traces
• Authors: Zhuangbin Chen, Zhihan Jiang, Yuxin Su, Michael R. Lyu, Zibin Zheng,
• Abstract summary: TraceMesh is a scalable and streaming sampler for distributed traces. It accommodates previously unseen trace features in a unified and streamlined way. TraceMesh outperforms state-of-the-art methods by a significant margin in both sampling accuracy and efficiency.
• Score: 51.08892669409318
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Distributed tracing serves as a fundamental element in the monitoring of cloud-based and datacenter systems. It provides visibility into the full lifecycle of a request or operation across multiple services, which is essential for understanding system dependencies and performance bottlenecks. To mitigate computational and storage overheads, most tracing frameworks adopt a uniform sampling strategy, which inevitably captures overlapping and redundant information. More advanced methods employ learning-based approaches to bias the sampling toward more informative traces. However, existing methods fall short of considering the high-dimensional and dynamic nature of trace data, which is essential for the production deployment of trace sampling. To address these practical challenges, in this paper we present TraceMesh, a scalable and streaming sampler for distributed traces. TraceMesh employs Locality-Sensitivity Hashing (LSH) to improve sampling efficiency by projecting traces into a low-dimensional space while preserving their similarity. In this process, TraceMesh accommodates previously unseen trace features in a unified and streamlined way. Subsequently, TraceMesh samples traces through evolving clustering, which dynamically adjusts the sampling decision to avoid over-sampling of recurring traces. The proposed method is evaluated with trace data collected from both open-source microservice benchmarks and production service systems. Experimental results demonstrate that TraceMesh outperforms state-of-the-art methods by a significant margin in both sampling accuracy and efficiency.

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