SLOFetch: Compressed-Hierarchical Instruction Prefetching for Cloud Microservices

📝 Original Info

• Title: SLOFetch: Compressed-Hierarchical Instruction Prefetching for Cloud Microservices
• ArXiv ID: 2511.04774
• Date: 2025-11-06
• Authors: ** 정보가 제공되지 않음 (Authors not listed). **

📝 Abstract

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.

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