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CoGenT: A Content-oriented Generative-hit Framework for Content Delivery Networks

Peng Wang, Yu Liu, Ziqi Liu, Ming-Yang Wang, Ke Liu, Ke Zhou, Zhihai Huang

TL;DR

This paper tackles cache misses in CDNs by exploiting content locality through a novel edge-based generative-hit framework, CoGenT. It enables on-edge data generation from cached content using idle edge compute, addressing pseudo-missing requests while keeping generated data non-persistent to manage storage. The architecture comprises a pseudo-missing judgment module, a decision-tree-based two-pronged controller, and an extensible generative-hit processor, validated through a Tencent CDN deployment and simulator experiments. Real-world results show substantial latency and bandwidth reductions, with additional gains observed when applying CoGenT to existing caching strategies in simulation. The work lays groundwork for integrating data-generation techniques, including large language models, to further enhance CDN QoE and efficiency.

Abstract

The service provided by content delivery networks (CDNs) may overlook content locality, leaving room for potential performance improvement. In this study, we explore the feasibility of leveraging generated data as a replacement for fetching data in missing scenarios based on content locality. Due to sufficient local computing resources and reliable generation efficiency, we propose a content-oriented generative-hit framework (CoGenT) for CDNs. CoGenT utilizes idle computing resources on edge nodes to generate requested data based on similar or related cached data to achieve hits. Our implementation in a real-world system demonstrates that CoGenT reduces the average access latency by half. Additionally, experiments conducted on a simulator also confirm that CoGenT can enhance existing caching algorithms, resulting in reduced latency and bandwidth usage.

CoGenT: A Content-oriented Generative-hit Framework for Content Delivery Networks

TL;DR

This paper tackles cache misses in CDNs by exploiting content locality through a novel edge-based generative-hit framework, CoGenT. It enables on-edge data generation from cached content using idle edge compute, addressing pseudo-missing requests while keeping generated data non-persistent to manage storage. The architecture comprises a pseudo-missing judgment module, a decision-tree-based two-pronged controller, and an extensible generative-hit processor, validated through a Tencent CDN deployment and simulator experiments. Real-world results show substantial latency and bandwidth reductions, with additional gains observed when applying CoGenT to existing caching strategies in simulation. The work lays groundwork for integrating data-generation techniques, including large language models, to further enhance CDN QoE and efficiency.

Abstract

The service provided by content delivery networks (CDNs) may overlook content locality, leaving room for potential performance improvement. In this study, we explore the feasibility of leveraging generated data as a replacement for fetching data in missing scenarios based on content locality. Due to sufficient local computing resources and reliable generation efficiency, we propose a content-oriented generative-hit framework (CoGenT) for CDNs. CoGenT utilizes idle computing resources on edge nodes to generate requested data based on similar or related cached data to achieve hits. Our implementation in a real-world system demonstrates that CoGenT reduces the average access latency by half. Additionally, experiments conducted on a simulator also confirm that CoGenT can enhance existing caching algorithms, resulting in reduced latency and bandwidth usage.
Paper Structure (16 sections, 10 figures, 2 tables)

This paper contains 16 sections, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Observations and Motivation
  3. Generative Hit
  4. Scenarios
  5. Rationale
  6. CoGenT Design
  7. Overall Architecture
  8. Pseudo-missing Judgment Module
  9. Two-pronged Controller
  10. Extensible Generative-hit Processor
  11. Evaluation
  12. Deployment of prototype
  13. Improvement and overhead on the real production system
  14. Superiority, enhancement effect, and content redundancy tested on the simulator
  15. Related Work
  16. ...and 1 more sections

Figures (10)

  • Figure 1: The CPU utilization variation during a week on different edge nodes. City#1-9 are the names of the cities where edge nodes are located.
  • Figure 2: Content redundancy rates of two CDN instances. $x=512$GB in Case A and $x=2048$GB in Case B.
  • Figure 3: Several pseudo-missing scenarios and their generative-hit schemes.
  • Figure 4: The workflow of hit, missing, and pseudo-missing in the architecture of CoGenT.
  • Figure 5: The latency of generative hits in different pseudo-missing scenarios.
  • ...and 5 more figures