An Incremental Learning Based Edge Caching System : From Modeling to Evaluation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Guangping Xu
  • Bo Tang
  • Liming Yuan
  • Yanbing Xue
  • Zan Gao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8954696
Pages (from-to)12499-12509
Journal / PublicationIEEE Access
Volume8
Online published9 Jan 2020
Publication statusPublished - 2020

Link(s)

Abstract

Caches are widely applied to improve data delivery performance in distributed systems like edge networks and content delivery networks (CDNs). We consider caching mechanism in those networks that deliver contents to end users. The challenge comes from the dynamic content distribution problem. The distribution of data popularity is highly skewed and changing over time. Besides, the access pattern of the user requests also varies over time. Some learning algorithms for edge caching problems need to rebuild a new model periodically to adapt to system dynamics, where the knowledge learned from the past is discarded. Besides, each model updating needs a large amount of data, leading to outdated models for consecutive user requests. Inspired by the success of incremental learning approaches in processing massive data in real time, we propose an incremental learning based framework at an edge caching server. The incremental learning algorithm is used to preserve valuable knowledge and to adapt to dynamic workloads faster. We implement our incremental learning based cache system prototype and evaluate its performance under various real-world workloads. The experimental results show that our algorithm can boost cache hit ratio for dynamic workloads compared with the state-of-the-art caching algorithms.

Research Area(s)

  • Edge caching, incremental learning, system dynamics

Citation Format(s)

An Incremental Learning Based Edge Caching System: From Modeling to Evaluation. / Xu, Guangping; Tang, Bo; Yuan, Liming et al.
In: IEEE Access, Vol. 8, 8954696, 2020, p. 12499-12509.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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