Modern storage systems often combine fast cache with slower backend devices to accelerate I/O. As performance gaps narrow, concurrently accessing both devices, rather than relying solely on cache hits, can improve throughput. However, in data centers, remote backend storage accessed over networks suffers from unpredictable contention, complicating this split. We present NetCAS, a framework that dynamically splits I/O between cache and backend devices based on real-time network feedback and a precomputed Perf Profile. Unlike traditional hit-rate-based policies, NetCAS adapts split ratios to workload configuration and networking performance. NetCAS employs a low-overhead batched round-robin scheduler to enforce splits, avoiding per-request costs. It achieves up to 174% higher performance than traditional caching in remote storage environments and outperforms converging schemes like Orthus by up to 3.5X under fluctuating network conditions.

翻译：现代存储系统常通过结合快速缓存与较慢的后端设备来加速I/O。随着性能差距缩小，同时访问两种设备（而非仅依赖缓存命中）可提升吞吐量。然而在数据中心中，通过网络访问的远程后端存储会面临不可预测的竞争干扰，这使这种拆分方式复杂化。我们提出NetCAS——一种基于实时网络反馈与预计算性能配置文件（Perf Profile）动态拆分缓存与后端设备I/O的框架。与传统基于命中率的策略不同，NetCAS根据工作负载配置与网络性能自适应调整拆分比例。NetCAS采用低开销的批量轮询调度器来强制执行拆分，避免单次请求开销。在远程存储环境中，NetCAS相比传统缓存可实现高达174%的性能提升，而在波动网络条件下，其性能比Orthus等收敛方案最高提升3.5倍。