# MITHRIL: Mining Sporadic Associations for Cache Prefetching

arXiv: https://arxiv.org/abs/1705.07400 · HTML: https://mlsanity.com/paper/1705.07400/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2017.

## Built on by (most cited first, 3 of 3)

- [DumpKV: Learning based lifetime aware garbage collection for key value separation in LSM-tree](https://mlsanity.com/paper/2406.01250/) (2024)
- [DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks](https://mlsanity.com/paper/2310.07881/) (2023)
- [A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing](https://mlsanity.com/paper/2608.13573/) (2026)

## Abstract (arXiv)

The growing pressure on cloud application scalability has accentuated storage performance as a critical bottle- neck. Although cache replacement algorithms have been extensively studied, cache prefetching - reducing latency by retrieving items before they are actually requested remains an underexplored area. Existing approaches to history-based prefetching, in particular, provide too few benefits for real systems for the resources they cost. We propose MITHRIL, a prefetching layer that efficiently exploits historical patterns in cache request associations. MITHRIL is inspired by sporadic association rule mining and only relies on the timestamps of requests. Through evaluation of 135 block-storage traces, we show that MITHRIL is effective, giving an average of a 55% hit ratio increase over LRU and PROBABILITY GRAPH, a 36% hit ratio gain over AMP at reasonable cost. We further show that MITHRIL can supplement any cache replacement algorithm and be readily integrated into existing systems. Furthermore, we demonstrate the improvement comes from MITHRIL being able to capture mid-frequency blocks.