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Online Digital Twin-Empowered Content Resale Mechanism in Age of Information-Aware Edge Caching Networks

Yuhan Yi, Guanglin Zhang, Hai Jiang

TL;DR

This paper proposes an online digital twin (DT) empowered content resale mechanism in AoI-aware edge caching networks and designs an optimal two-timescale caching strategy to maximize the utility of an edge network service provider (ENSP).

Abstract

For users requesting popular contents from content providers, edge caching can alleviate backhaul pressure and enhance the quality of experience of users. Recently there is also a growing concern about content freshness that is quantified by age of information (AoI). Therefore, AoI-aware online caching algorithms are required, which is challenging because the content popularity is usually unknown in advance and may vary over time. In this paper, we propose an online digital twin (DT) empowered content resale mechanism in AoI-aware edge caching networks. We aim to design an optimal two-timescale caching strategy to maximize the utility of an edge network service provider (ENSP). The formulated optimization problem is non-convex and NP-hard. To tackle this intractable problem, we propose a DT-assisted Online Caching Algorithm (DT-OCA). In specific, we first decompose our formulated problem into a series of subproblems, each handling a cache period. For each cache period, we use a DT-based prediction method to effectively capture future content popularity, and develop online caching strategy. Competitive ratio analysis and extensive experimental results demonstrate that our algorithm has promising performance, and outperforms other benchmark algorithms. Insightful observations are also found and discussed.

Online Digital Twin-Empowered Content Resale Mechanism in Age of Information-Aware Edge Caching Networks

TL;DR

This paper proposes an online digital twin (DT) empowered content resale mechanism in AoI-aware edge caching networks and designs an optimal two-timescale caching strategy to maximize the utility of an edge network service provider (ENSP).

Abstract

For users requesting popular contents from content providers, edge caching can alleviate backhaul pressure and enhance the quality of experience of users. Recently there is also a growing concern about content freshness that is quantified by age of information (AoI). Therefore, AoI-aware online caching algorithms are required, which is challenging because the content popularity is usually unknown in advance and may vary over time. In this paper, we propose an online digital twin (DT) empowered content resale mechanism in AoI-aware edge caching networks. We aim to design an optimal two-timescale caching strategy to maximize the utility of an edge network service provider (ENSP). The formulated optimization problem is non-convex and NP-hard. To tackle this intractable problem, we propose a DT-assisted Online Caching Algorithm (DT-OCA). In specific, we first decompose our formulated problem into a series of subproblems, each handling a cache period. For each cache period, we use a DT-based prediction method to effectively capture future content popularity, and develop online caching strategy. Competitive ratio analysis and extensive experimental results demonstrate that our algorithm has promising performance, and outperforms other benchmark algorithms. Insightful observations are also found and discussed.
Paper Structure (15 sections, 1 theorem, 23 equations, 8 figures, 1 algorithm)

This paper contains 15 sections, 1 theorem, 23 equations, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Two-Timescale Caching Strategy
  4. Utility Model
  5. Problem Formulation
  6. Digital Twin-Assisted Online Caching Algorithm
  7. Problem Decomposition and Offline Algorithm
  8. Digital Twin-Based Prediction Method
  9. Digital Twin-Assisted Online Caching Algorithm
  10. Competitive Ratio Analysis
  11. Experimental Results
  12. Parameter Setting
  13. Performance Evaluation and Comparison
  14. Performance with Real-World Dataset
  15. Conclusion

Key Result

Theorem 1

Denote $u^o(\omega)$ and $u^*(\omega)$ as the utility obtained by executing DT-OCA-PP and the offline optimal algorithm, respectively, over instance $\omega$. The competitive ratio of DT-OCA-PP, denoted by $\max_{\omega \in \Omega}~\frac{u^*(\omega)}{u^o(\omega)}$, is bounded as in which $\alpha\triangleq\min\limits_{n\in\mathcal{N}_c, \textcolor{black}{t\in\mathcal{T}}}\frac{p_n^s(l)+s_n C_d}{p_

Figures (8)

  • Figure 1: Online DT-empowered content resale mechanism in AoI-aware edge caching networks.
  • Figure 2: DT-based prediction method.
  • Figure 3: The Transformer model of DT-based prediction method.
  • Figure 4: ENSP's cache status by using DT-OCA in the first $5$ cache periods.
  • Figure 5: (a) The utility, (b) the hit rate, and (c) the average AoI.
  • ...and 3 more figures

Theorems & Definitions (5)

  • Remark 1
  • Definition 1: Competitive ratio
  • Theorem 1
  • proof
  • Remark 2