# SLOFetch: Compressed-Hierarchical Instruction Prefetching for Cloud Microservices

arXiv: https://arxiv.org/abs/2511.04774 · HTML: https://mlsanity.com/paper/2511.04774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 6 Nov 2025.

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

- [RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction](https://mlsanity.com/paper/2512.07848/) (2025)

## Abstract (arXiv)

Large-scale networked services rely on deep soft-ware stacks and microservice orchestration, which increase instruction footprints and create frontend stalls that inflate tail latency and energy. We revisit instruction prefetching for these cloud workloads and present a design that aligns with SLO driven and self optimizing systems. Building on the Entangling Instruction Prefetcher (EIP), we introduce a Compressed Entry that captures up to eight destinations around a base using 36 bits by exploiting spatial clustering, and a Hierarchical Metadata Storage scheme that keeps only L1 resident and frequently queried entries on chip while virtualizing bulk metadata into lower levels. We further add a lightweight Online ML Controller that scores prefetch profitability using context features and a bandit adjusted threshold. On data center applications, our approach preserves EIP like speedups with smaller on chip state and improves efficiency for networked services in the ML era.