windowed lru cache implementation

This work addresses the limitations of traditional Linux page cache eviction policies, which rely on fixed heuristics and struggle to adapt to diverse workloads, thereby constraining cache efficiency. The authors propose the first integration of a lightweight single-layer perceptron directly into the kernel’s page cache subsystem, leveraging eBPF to enable low-overhead, real-time intelligent eviction decisions. The model is trained on kernel-level data collected from real-world workloads to predict page reuse times and dynamically select eviction candidates. Experimental results across a range of representative workloads demonstrate that, compared to a FIFO baseline, the approach improves cache hit rates by up to 10% and achieves a median AUC of 80%, confirming the feasibility and superiority of machine learning–driven cache management within the kernel.

Traditional Linux page caching employs fixed replacement policies, which struggle to adapt to diverse application workloads; kernel-level policy customization remains impractical due to high development complexity and security risks. This paper introduces CacheBPF—the first eBPF-based programmable page cache framework—that safely injects user-defined eviction policies into the kernel via tracepoints, requiring no kernel source modification and supporting cross-process policy sharing with strict isolation. It pioneers the integration of eBPF into core OS memory management, leveraging a policy sandbox and application-aware metadata tagging to ensure both safety and flexibility. Evaluated under realistic workloads, CacheBPF achieves up to 70% higher throughput and 58% lower tail latency compared to baseline policies, demonstrating that fine-grained, workload-specific eviction strategies significantly improve performance for heterogeneous applications.

This work addresses the challenge in hierarchical caching networks where conventional request-time-based eviction policies fail to assess the impact of removing aligned storage blocks on the connectivity of downstream critical services, often causing service disruptions. The authors model aligned eviction as a weighted vertex separation problem on a graph, precisely computing the downstream demand cut of candidate blocks to reject evictions that compromise protected paths and selecting the feasible eviction with minimal impact. They introduce a novel verifiable service-cut certificate mechanism that, for the first time, unifies capacity reclamation, path continuity, and distributed failure within a certifiable interface, and prove that strategies relying solely on historical information can incur unbounded single-step damage. Experiments across 144 scenarios processing 582.9 trillion packets (404.86 PiB) validate theoretical predictions, reveal a zero-impact extremal phase transition point, and enable full impact vectors and audit samples within supplementary material budgets.