Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis

What is it about?

Modern software systems generate huge amounts of data to help engineers monitor and troubleshoot issues. This data, known as distributed traces, records how different parts of a system interact. However, because the volume of these traces can be overwhelming, it's challenging to keep all the useful information without overloading storage and network resources. Traditionally, systems either keep an entire trace or discard it completely—a strategy we found to be inefficient. To solve this, we developed a new approach called the "commonality + variability" paradigm. Instead of treating traces as all-or-nothing, we break them down into common patterns and unique details. By keeping the patterns and filtering out unnecessary details, we can capture all the important information more efficiently. Our solution, called Mint, applies this method in real-time as data is collected. It significantly reduces storage and network costs while ensuring that critical information isn’t lost. Plus, Mint is lightweight enough to be used in real-world systems without causing performance issues.

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Photo by engin akyurt on Unsplash

Photo by engin akyurt on Unsplash

Why is it important?

As modern software systems grow more complex, effectively monitoring them becomes critical for ensuring reliability and quickly diagnosing issues. However, traditional tracing methods struggle to balance the need for detailed information with the practical limits of storage and network capacity. This challenge is especially urgent in large-scale, real-time environments where losing important trace data can lead to undetected system failures and prolonged downtime. Our work is unique because it breaks away from the outdated "keep or discard" (1 or 0) sampling approach. Instead, **Mint** introduces a novel *"commonality + variability"* paradigm that allows systems to capture **all traces** while intelligently reducing redundant data. This ensures that no critical information is lost, improving the accuracy of problem detection and analysis. Mint’s ability to significantly reduce resource usage without sacrificing trace quality makes it highly relevant for today’s cloud-native and distributed environments. Its lightweight design also means it can be seamlessly deployed in production systems, providing immediate, real-world benefits for system reliability and performance monitoring.