Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches

TL;DR

This study analyzes data access patterns in the XRootD distributed storage cache used in high-energy physics, revealing insights that inform optimal cache sizing to improve performance.

Contribution

It provides a detailed analysis of cache access patterns and introduces a cache simulator to evaluate the impact of cache size on hit rates.

Findings

Increasing cache size from 40TB to 56TB significantly improves hit rate.

File read operation size grows over time, while read frequency remains constant.

Files tend to have consistent open durations, aiding cache modeling.

Abstract

The XRootD system is used to transfer, store, and cache large datasets from high-energy physics (HEP). In this study we focus on its capability as distributed on-demand storage cache. Through exploring a large set of daily log files between 2020 and 2021, we seek to understand the data access patterns that might inform future cache design. Our study begins with a set of summary statistics regarding file read operations, file lifetimes, and file transfers. We observe that the number of read operations on each file remains nearly constant, while the average size of a read operation grows over time. Furthermore, files tend to have a consistent length of time during which they remain open and are in use. Based on this comprehensive study of the cache access statistics, we developed a cache simulator to explore the behavior of caches of different sizes. Within a certain size range, we find that increasing the XRootD cache size improves the cache hit rate, yielding faster overall file access. In particular, we find that increase the cache size from 40TB to 56TB could increase the hit rate from 0.62 to 0.89, which is a significant increase in cache effectiveness for modest cost.