Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12.3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files are less likely to be reused. Due to this difference in data access patterns, the cache system has implemented a policy to avoid evicting smaller files when processing larger files. We also build a machine learning model to study the predictability of the cache behavior. Tests show that this model is able to accurately predict the cache accesses, cache misses, and network throughput, making the model useful for future studies on resource provisioning and planning.

翻译：大型科学合作项目中，多名科研人员常需访问同一组文件进行不同分析，导致对远程海量共享数据的重复访问。此类数据访问因距离产生长延迟，并占用广域网有限带宽。为减少广域网流量与数据访问延迟，区域数据存储缓存已作为新型网络服务部署。为探究此类缓存系统在科学应用中的有效性，我们针对高能物理实验研究了南加州拍字节级缓存系统。通过对约3TB运行日志的分析，结果表明该缓存系统平均每日减少了广域网67.6%的文件请求，并降低广域网流量12.3TB（降幅35.4%）。由于大文件重用的概率较低，流量降幅（35.4%）低于文件请求降幅（67.6%）。针对数据访问模式的差异，缓存系统已实施策略避免在处理大文件时驱逐小文件。我们构建了机器学习模型以研究缓存行为的可预测性。测试表明，该模型能准确预测缓存命中、缓存缺失及网络吞吐量，为未来的资源调配与规划研究提供了有效工具。