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ASTRA: Association, Spatial proximity and Temporal Relevance based Adaptive prefetching for Edge AR

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 augmented reality (AR) experiences. 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, user's field of view and physical location in a coherent manner. This paper investigates how to further optimize edge caching by employing AR-Aware prefetching techniques. We present ASTRA, a prefetching framework tailored for mobile augmented reality edge caches. It integrates object associations derived from user interaction patterns with spatial awareness based on the user's physical location and field of view. This approach employs an association factor per object that considers the recency of object co-Access; and a lazy fetching strategy that prioritizes prefetching only when the user is in close proximity to the virtual objects. Furthermore, ASTRA incorporates an adaptive tuning algorithm for minimum support in association rule generation to minimize the computation overhead, making it a distinct and effective solution for enhancing user experience in AR applications by ensuring timely virtual object availability.Through extensive evaluation using both synthetic and real-world workloads, we demonstrate that ASTRA significantly improves cache hit rates compared to current prefetching algorithms, achieving gains in hit rate of upto 35% and end-To-end latency by upto 14%. Further, we demonstrate that the adaptive tuning algorithm that automatically tunes minimum support further improves the hit rate of ASTRA by 10%. Our findings demonstrate the potential of ASTRA to substantially enhance the user experience in MAR applications by ensuring the timely availability of virtual objects.

Original languageEnglish (US)
Title of host publicationProceedings - 2025 IEEE International Conference on Cloud Engineering, IC2E 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-179
Number of pages10
ISBN (Electronic)9798331534653
DOIs
StatePublished - 2025
Event13th IEEE International Conference on Cloud Engineering, IC2E 2025 - Rennes, France
Duration: Sep 23 2025Sep 26 2025

Publication series

NameProceedings - 2025 IEEE International Conference on Cloud Engineering, IC2E 2025

Conference

Conference13th IEEE International Conference on Cloud Engineering, IC2E 2025
Country/TerritoryFrance
CityRennes
Period9/23/259/26/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

  • Augmented reality
  • association
  • caching
  • confidence
  • edge computing
  • prefetching
  • proximity
  • support

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