Performance and Cost-Aware Cache Provisioning
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.