# Access Trends of In-network Cache for Scientific Data

arXiv: https://arxiv.org/abs/2205.05563 · HTML: https://mlsanity.com/paper/2205.05563/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 7 of 7)

- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [StashCache: A Distributed Caching Federation for the Open Science Grid](https://mlsanity.com/paper/1905.06911/) (2019)
- [Creating a content delivery network for general science on the internet backbone using XCaches](https://mlsanity.com/paper/2007.01408/) (2020)
- [Analyzing scientific data sharing patterns for in-network data caching](https://mlsanity.com/paper/2105.00964/) (2021)
- [Moving the California distributed CMS xcache from bare metal into containers using Kubernetes](https://mlsanity.com/paper/2003.02319/) (2020)
- [Deploying in-network caches in support of distributed scientific data sharing](https://mlsanity.com/paper/2203.06843/) (2022)

## Built on by (most cited first, 1 of 1)

- [Effectiveness and predictability of in-network storage cache for scientific workflows](https://mlsanity.com/paper/2307.11069/) (2023)

## Abstract (arXiv)

Scientific collaborations are increasingly relying on large volumes of data for their work and many of them employ tiered systems to replicate the data to their worldwide user communities. Each user in the community often selects a different subset of data for their analysis tasks; however, members of a research group often are working on related research topics that require similar data objects. Thus, there is a significant amount of data sharing possible. In this work, we study the access traces of a federated storage cache known as the Southern California Petabyte Scale Cache. By studying the access patterns and potential for network traffic reduction by this caching system, we aim to explore the predictability of the cache uses and the potential for a more general in-network data caching. Our study shows that this distributed storage cache is able to reduce the network traffic volume by a factor of 2.35 during a part of the study period. We further show that machine learning models could predict cache utilization with an accuracy of 0.88. This demonstrates that such cache usage is predictable, which could be useful for managing complex networking resources such as in-network caching.