# Complexity evaluation of network configurations and abstractions

**Authors:** Jose Moreno  
**arXiv:** [2308.00446](https://arxiv.org/abs/2308.00446)  
**Date:** 2023-08-01  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2308.00446v1/

## Key points

- Traditional configuration methods in computer networking relied on humans using command-line interfaces
- Network abstractions have emerged as alternatives to manual configuration in the past decade
- There is currently no objective way to compare and determine the effectiveness of different network abstractions
- Lack of consensus within the industry regarding modern network abstraction direction leads to a lag in adoption
- The framework utilizes graph structure metrics to evaluate simplicity, efficiency, and effectiveness of network abstraction models
- Existing models like Kubernetes or Cisco Application Centric Infrastructure are efficient for storing network policies
- Public cloud-based abstractions remain infrastructure-centric and suffer from excessive complexity
- Standardized evaluation methods are needed to objectively assess different network abstraction models
- Moreno's research provides a comprehensive comparison framework based on graph structure metrics
- The study highlights the importance of objectively evaluating network abstraction models
- It serves as a foundation for further research in developing more efficient and effective network configurations and abstractions.

## Summary

In the field of computer networking, traditional configuration methods have relied on humans using command-line interfaces. However, in the past decade, various network abstractions have emerged as alternatives to manual configuration. Despite their availability, there is currently no objective way to compare these abstractions and determine their effectiveness. As a result, there is no consensus within the industry regarding the direction modern network abstractions should take, leading to a lag in their adoption. This framework utilizes metrics derived from graph structures to evaluate the simplicity, efficiency, and effectiveness of different network abstraction models. By applying this framework, Moreno finds that while some existing network abstractions such as Kubernetes or Cisco Application Centric Infrastructure models are efficient for storing network policies, others like public cloud-based abstractions remain infrastructure-centric and suffer from excessive complexity. The paper emphasizes the need for standardized evaluation methods to objectively assess different network abstraction models. By providing a comprehensive comparison framework based on graph structure metrics, Moreno's research contributes towards addressing this gap in understanding and facilitating informed decision-making regarding modern network abstractions. Overall, this study highlights the importance of objectively evaluating network abstraction models and provides valuable insights into their strengths and weaknesses. It serves as a foundation for further research in developing more efficient and effective network configurations and abstractions.