Inferring Causal Relationships to Improve Caching for Clients with Correlated Requests: Applications to VR

TL;DR

This paper introduces a new model for correlated client requests in VR environments, analyzes traditional caching policies under this model, and proposes a novel adaptive policy, LFRU, that significantly improves caching performance.

Contribution

It presents the grouped client request model, analyzes LRU's performance, and introduces LFRU, an adaptive caching policy that leverages causal request relationships for better efficiency.

Findings

LFRU outperforms LRU and LFU in correlated request scenarios.

LFRU achieves up to 2.9x better performance than LRU.

Theoretical analysis shows cache size influences optimal policy choice.

Abstract

Efficient edge caching reduces latency and alleviates backhaul congestion in modern networks. Traditional caching policies, such as Least Recently Used (LRU) and Least Frequently Used (LFU), perform well under specific request patterns. LRU excels in workloads with strong temporal locality, while LFU is effective when content popularity remains static. However, real-world client requests often exhibit correlations due to shared contexts and coordinated activities. This is particularly evident in Virtual Reality (VR) environments, where groups of clients navigate shared virtual spaces, leading to correlated content requests. In this paper, we introduce the \textit{grouped client request model}, a generalization of the Independent Reference Model that explicitly captures different types of request correlations. Our theoretical analysis of LRU under this model reveals that the optimal causal caching policy depends on cache size: LFU is optimal for small to moderate caches, while LRU outperforms it for larger caches. To address the limitations of existing policies, we propose Least Following and Recently Used (LFRU), a novel online caching policy that dynamically infers and adapts to causal relationships in client requests to optimize evictions. LFRU prioritizes objects likely to be requested based on inferred dependencies, achieving near-optimal performance compared to the offline optimal Belady policy in structured correlation settings. We develop VR based datasets to evaluate caching policies under realistic correlated requests. Our results show that LFRU consistently performs at least as well as LRU and LFU, outperforming LRU by up to 2.9x and LFU by up to1.9x in certain settings.