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Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network.

Xiaomin Gong1, Shuangyin Ren1, Chunjiang Wang1

  • 1Academy of Systems Engineering, AMS, Beijing 100141, China.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a Software Defined Computing First Network (SD-CFN) architecture with novel algorithms for resource measurement (DCRMM) and routing (RSCR). These methods enhance network performance and efficiency in computing first networks.

Keywords:
computing first networkcomputing resource measurementcomputing routingreinforcement learningsoftware defined network

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Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Computing First Networks (CFN) are foundational to modern network infrastructure.
  • Efficient computing resource measurement and routing are critical for CFN performance.
  • Existing algorithms face challenges in dynamic network environments.

Purpose of the Study:

  • To introduce a Software Defined Computing First Network (SD-CFN) architecture.
  • To propose a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM).
  • To develop a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm.

Main Methods:

  • DCRMM utilizes entropy weight method and K-Means clustering for resource measurement.
  • RSCR employs a knowledge plane for routing calculations, considering latency, bandwidth, and packet loss.
  • Simulations were performed on the GÉANT topology.

Main Results:

  • DCRMM demonstrated superior node stability, utilization, and matching accuracy compared to MSA and MDA.
  • RSCR outperformed OSPF in link latency, packet loss rate, and throughput.
  • The proposed SD-CFN architecture effectively integrates DCRMM and RSCR.

Conclusions:

  • DCRMM and RSCR provide advanced solutions for computing resource measurement and routing in CFNs.
  • The SD-CFN architecture enhances network efficiency and performance.
  • This research contributes innovative methods for optimizing computing first networks.