# Slicing the IO execution with ReLayTracer

**Authors:** Ganguk Lee, Yeaseul Park, Jeongseob Ahn, Youngjin Kwon

arXiv: 1906.07124 · 2019-06-18

## TL;DR

ReLayTracer is a detailed IO profiling tool that helps identify root causes of IO performance anomalies by providing per-request, per-layer latency analysis and hardware behavior insights.

## Contribution

It introduces ReLayTracer, a novel per-request, per-layer IO profiler that offers fine-grained latency profiling and kernel activity analysis for diagnosing IO performance issues.

## Key findings

- Provides detailed per-layer latency distributions
- Identifies root causes of IO anomalies effectively
- Offers insights into hardware and kernel activities during IO requests

## Abstract

Analyzing IO performance anomalies is a crucial task in various computing environments, ranging from large-scale cloud applications to desktop applications. However, the IO stack of modern operating systems is complicated, making it hard to understand the performance anomalies with existing tools. Kernel IO executions are frequently interrupted by internal kernel activities, requiring a sophisticated IO profile tool to deal with the noises. Furthermore, complicated interactions of concurrent IO requests cause different sources of tail latencies in kernel IO stack. As a consequence, developers want to know fine-grained latency profile across IO layers, which may differ in each IO requests. To meet the requirements, this paper suggests ReLayTracer, a per-request, per-layer IO profiler. ReLayTracer enables a detailed analysis to identify root causes of IO performance anomalies by providing per-layer latency distributions of each IO request, hardware performance behavior, and time spent by kernel activities such as an interrupt.

## Full text

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## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/1906.07124/full.md

## References

19 references — full list in the complete paper: https://tomesphere.com/paper/1906.07124/full.md

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Source: https://tomesphere.com/paper/1906.07124