Utility-driven Optimization of TTL Cache Hierarchies under Network Delays

TL;DR

This paper presents a method to optimize TTL cache hierarchies considering network delays, using exact models for small systems and machine learning for large systems, leading to improved cache efficiency.

Contribution

It introduces a novel utility maximization framework for TTL cache hierarchies under network delays and proposes scalable solutions for large systems.

Findings

Significant offloading improvement with optimized TTLs

Effective machine learning approach for large hierarchies

Validation on data center traces and numerical simulations

Abstract

We optimize hierarchies of Time-to-Live (TTL) caches under random network delays. A TTL cache assigns individual eviction timers to cached objects that are usually refreshed upon a hit where upon a miss the object requires a random time to be fetched from a parent cache. Due to their object decoupling property, TTL caches are of particular interest since the optimization of a per-object utility enables service differentiation. However, state-of-the-art exact TTL cache optimization does not extend beyond single TTL caches, especially under network delays. In this paper, we leverage the object decoupling effect to formulate the non-linear utility maximization problem for TTL cache hierarchies in terms of the exact object hit probability under random network delays. We iteratively solve the utility maximization problem to find the optimal per-object TTLs. Further, we show that the exact model suffers from tractability issues for large hierarchies and propose a machine learning approach to estimate the optimal TTL values for large systems. Finally, we provide numerical and data center trace-based evaluations for both methods showing the significant offloading improvement due to TTL optimization considering the network delays.