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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Controller-driven vector autoregression model for predicting content popularity in programmable named data networking

Firdous Qaiser1, Mudassar Hussain2, Abdul Ahad2,3,4

  • 1Department of Computer Science, University of Sialkot, Sialkot, Pakistan.

Peerj. Computer Science
|March 4, 2024
PubMed
Summary
This summary is machine-generated.

Software Defined Networking (SDN) optimizes content caching in Named Data Networking (NDN) edge infrastructures. The Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn) framework improves cache hit rates by 20% and reduces retrieval delays by 28%.

Keywords:
Caching placementContent cachingInformation centric networking (ICN)NDNSDNVector autoregression (VAR)

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Area of Science:

  • Computer Science
  • Network Engineering
  • Information Technology

Background:

  • Named Data Networking (NDN) offers content delivery advantages in edge infrastructures through name-based routing and in-network caching.
  • Decentralized decision-making in NDN caching devices often leads to suboptimal performance.
  • Existing NDN caching strategies require enhancement for efficient edge content delivery.

Purpose of the Study:

  • To introduce a Software Defined Networking (SDN) controller for optimizing popular content placement in NDN nodes.
  • To develop a novel content caching framework, Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn), for edge infrastructures.
  • To enhance cache hit rates and reduce content retrieval delays in NDN environments.

Main Methods:

  • Implementation of an SDN controller to manage content caching strategies, considering network congestion, security, topology, and flow rules.
  • Development of the PaCPn framework utilizing a multi-variant vector autoregression (VAR) model for content popularity prediction.
  • Employment of a controller-driven heuristic algorithm to evaluate caching point proximity to consumers based on distance, delivery time, and content status.

Main Results:

  • The PaCPn framework achieved a 20% improvement in cache hit rates across various metrics like cache size and traffic.
  • Content retrieval delays were reduced by 28% by PaCPn, considering factors such as cache capacity and network throughput.
  • PaCPn demonstrated significant performance enhancements compared to existing NDN content caching schemes in edge infrastructures.

Conclusions:

  • The proposed PaCPn framework effectively optimizes content caching in NDN edge infrastructures using SDN.
  • SDN-driven content placement significantly improves cache performance and reduces retrieval latency.
  • This research provides a viable solution for advancing NDN content caching and optimizing edge infrastructures.