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

TL;DR

The paper tackles latency in mobile augmented reality by introducing SPAARC, an edge-cache prefetching framework that leverages object associations via Association Rule Mining and spatial proximity to proactively fetch virtual objects. It presents an AR-centric workflow, including an association component with metrics S, C, and L, a proximity filter, and an adaptive tuning mechanism to adjust minimum support. Through simulations and real-world traces, SPAARC achieves cache hit-rate improvements of 3%–40% over traditional baselines and reduces on-demand cloud fetches, while maintaining manageable prefetch overhead and demonstrating robustness across varying user and object counts. These results suggest that AR-aware prefetching at the edge can substantially improve MAR QoE and guide practical deployment, with future work addressing overhead and further automated parameter optimization.

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.

Figures (13)

• Figure 1: A Typical Mobile Augmented Reality (MAR) Pipeline
• Figure 2: Grocery and tourist scenarios
• Figure 3: MAR Pipeline with SPAARC on the edge cache
• Figure 4: SPAARC workflow
• Figure 5: Hit rates across datasets. The top hit rates achieved by SPAARC compared to baselines.