SCION: Size-aware Policy Orchestration for Nonstationary Object Caches (Long Paper Version)

TL;DR

SCION is a lightweight framework that dynamically selects cache policies based on workload fingerprints, improving cache performance and robustness in heterogeneous, nonstationary environments.

Contribution

It introduces a workload fingerprint-based policy selection method that outperforms simple heuristics and maintains low overhead in object caching systems.

Findings

AUTO improves cache miss ratio over SIEVE on most workloads.

AUTO remains close to the best single expert on average.

AUTO-fast achieves lower cost under fast-policy constraints.

Abstract

Object caches underpin cloud and edge services, but production workloads are heterogeneous, nonstationary, and throughput-constrained. Recent simple non-ML policies such as SIEVE and S3-FIFO set a strong baseline, so any learned method must be overhead-aware, robust under drift, and competitive with strong experts. We present SCION, a lightweight policy-orchestration framework that selects among a small set of deployable cache policies using a tiny workload fingerprint computed off the critical path. Our prototype, AUTO, uses short-prefix statistics of object size, cacheability, reuse, and cache size, then applies an offline-trained linear selector to choose among GDSF, S3-FIFO, SIEVE, LHD, W-TinyLFU-AV, and DynamicAdaptiveClimb; a simpler SCION-P90 variant uses only a p90 threshold. In a CPU-only, trace-driven evaluation on 30 public object-cache traces and a separate HR-Cache simulator subset, AUTO improves cacheable-only object miss ratio over SIEVE on a majority of workloads, stays close to the best single expert on average, enables explicit OMR/BMR tradeoff selection, and remains competitive on byte miss ratio. Under a fast-policy budget, AUTO-fast achieves lower cost than the best fixed fast policy. SCION reduces regime-mismatch risk while keeping the hot path unchanged.