压缩分层预取技术降低云微服务延迟，提升能效。
SLOFetch: Compressed-Hierarchical Instruction Prefetching for Cloud Microservices
- 用36位压缩编码捕捉8个目标地址，减少预取状态开销。
- 分层元数据存储，仅保留热点数据在芯片上，其余虚拟化管理。
- 轻量在线机器学习控制器动态评估预取收益，适合高并发云场景。
大规模网络化服务依赖复杂的软件栈和微服务编排，导致指令足迹增大，前端停顿加剧尾延迟和能耗。本文重新审视云工作负载下的指令预取，提出一种符合SLO驱动与自优化系统的方案。基于纠缠指令预取器（EIP），引入压缩条目，利用空间聚类特性，仅用36位即可捕获基地址附近的最多八个目标地址；同时设计分层元数据存储机制，将仅L1驻留且高频查询的条目保留在芯片上，其余批量元数据虚拟化至低层级。进一步加入轻量级在线机器学习控制器，通过上下文特征评分预取收益，并采用强化学习调整阈值。在数据中心应用中，该方法保持了类似EIP的加速效果，但芯片状态更小，显著提升了现代机器学习时代网络化服务的效率。
原文摘要 · Abstract (English)
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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