Scaler: Efficient and Effective Cross Flow Analysis

TL;DR

Scaler introduces a novel cross flow analysis method and profiler that effectively identify performance issues in complex systems by monitoring component interactions with low overhead.

Contribution

It presents a new analysis technique, Cross Flow Analysis, and the Scaler profiler with innovative methods for accurate, low-overhead performance profiling of component interactions.

Findings

Detects multiple unknown performance issues in popular applications.

Achieves low runtime and memory overhead with high profiling accuracy.

Provides a comprehensive view of component and API interactions.

Abstract

Performance analysis is challenging as different components (e.g.,different libraries, and applications) of a complex system can interact with each other. However, few existing tools focus on understanding such interactions. To bridge this gap, we propose a novel analysis method "Cross Flow Analysis (XFA)" that monitors the interactions/flows across these components. We also built the Scaler profiler that provides a holistic view of the time spent on each component (e.g., library or application) and every API inside each component. This paper proposes multiple new techniques, such as Universal Shadow Table, and Relation-Aware Data Folding. These techniques enable Scaler to achieve low runtime overhead, low memory overhead, and high profiling accuracy. Based on our extensive experimental results, Scaler detects multiple unknown performance issues inside widely-used applications, and therefore will be a useful complement to existing work. The reproduction package including the source code, benchmarks, and evaluation scripts, can be found at https://doi.org/10.5281/zenodo.13336658.