SPAARC: Spatial Proximity and Association based prefetching for Augmented Reality in edge Cache

TL;DR

SPAARC is a novel AR-aware prefetching policy for edge caches in mobile AR, which improves cache hit rates and reduces cloud fetches by considering spatial proximity, object associations, and user location.

Contribution

The paper introduces SPAARC, a new prefetching strategy tailored for AR edge caching that leverages spatial and association data to optimize cache performance.

Findings

SPAARC increases cache hit rates by up to 40%.

It reduces cloud data retrieval needs significantly.

The adaptive tuning algorithm optimizes SPAARC parameters for best results.

Abstract

Mobile Augmented Reality (MAR) applications face performance challenges due to their high computational demands and need for low-latency responses. Traditional approaches like on-device storage or reactive data fetching from the cloud often result in limited AR experiences or unacceptable lag. Edge caching, which caches AR objects closer to the user, provides a promising solution. However, existing edge caching approaches do not consider AR-specific features such as AR object sizes, user interactions, and physical location. This paper investigates how to further optimize edge caching by employing AR-aware prefetching techniques. We present SPAARC, a Spatial Proximity and Association-based Prefetching policy specifically designed for MAR Caches. SPAARC intelligently prioritizes the caching of virtual objects based on their association with other similar objects and the user's proximity to them. It also considers the recency of associations and uses a lazy fetching strategy to efficiently manage edge resources and maximize Quality of Experience (QoE). Through extensive evaluation using both synthetic and real-world workloads, we demonstrate that SPAARC significantly improves cache hit rates compared to standard caching algorithms, achieving gains ranging from 3% to 40% while reducing the need for on-demand data retrieval from the cloud. Further, we present an adaptive tuning algorithm that automatically tunes SPAARC parameters to achieve optimal performance. Our findings demonstrate the potential of SPAARC to substantially enhance the user experience in MAR applications by ensuring the timely availability of virtual objects.