AI 中文总结
本文针对边缘-云部署场景，提出新型混合分段缓存策略，可在满足服务水平目标（SLO）命中率的前提下，降低缓存容量需求并控制处理成本。
AI 中文摘要
传统缓存策略评估通常将容量固定为数据集的0.1%左右，以此衡量命中率，但实际边缘-云部署需要平衡存储和计算开销这些计费资源。因此，系统操作员常关注不同目标：确定满足特定服务水平目标（SLO）命中率所需的最小缓存大小。本文探索这种以SLO为中心的范式，分析各策略达到定义目标所需的最小容量和执行时间；此外，我们表明基于历史工作负载模式动态调整分段策略中的分段比例可提升效率。通过在真实和合成轨迹上评估，我们提出一种新型混合分段策略，在降低容量需求的同时保持处理成本较低。
英文摘要
While traditional cache policy evaluations fix capacity - often at 0.1% of the dataset - and measure the resulting hit rate, practical edge-cloud deployments require balancing both storage and computational overhead as billed resources. Consequently, system operators frequently focus on a different objective: determining the minimum cache size needed to satisfy a specific Service-Level Objective (SLO) hit-rate. This paper explores this SLO-centric paradigm by analyzing the minimum capacity and execution time each policy requires to hit a defined target. Additionally, we show that dynamically adjusting the segment ratio in segmented policies based on historical workload patterns enhances efficiency. Through evaluations across real-world and synthetic traces, we present a novel hybrid segmented policy that reduces capacity requirements while keeping processing costs low.
