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From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches

Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova, Pere Barlet-Ros, Albert Cabellos-Aparicio

Abstract

Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a given network. Its applications range from network planning and troubleshooting to feeding information to network controllers for configuration optimization. Traditional network performance modeling has relied heavily on Discrete Event Simulation (DES) and analytical methods grounded in mathematical theories such as Queuing Theory and Network Calculus. However, as of late, we have observed a paradigm shift, with attempts to obtain efficient Parallel DES, the surge of Machine Learning models, and their integration with other methodologies in hybrid approaches. This has resulted in a great variety of modeling approaches, each with its strengths and often tailored to specific scenarios or requirements. In this paper, we comprehensively survey the relevant network performance modeling approaches for wired networks over the last decades. With this understanding, we also define a taxonomy of approaches, summarizing our understanding of the state-of-the-art and how both technology and the concerns of the research community evolve over time. Finally, we also consider how these models are evaluated, how their different nature results in different evaluation requirements and goals, and how this may complicate their comparison.

From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches

Abstract

Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a given network. Its applications range from network planning and troubleshooting to feeding information to network controllers for configuration optimization. Traditional network performance modeling has relied heavily on Discrete Event Simulation (DES) and analytical methods grounded in mathematical theories such as Queuing Theory and Network Calculus. However, as of late, we have observed a paradigm shift, with attempts to obtain efficient Parallel DES, the surge of Machine Learning models, and their integration with other methodologies in hybrid approaches. This has resulted in a great variety of modeling approaches, each with its strengths and often tailored to specific scenarios or requirements. In this paper, we comprehensively survey the relevant network performance modeling approaches for wired networks over the last decades. With this understanding, we also define a taxonomy of approaches, summarizing our understanding of the state-of-the-art and how both technology and the concerns of the research community evolve over time. Finally, we also consider how these models are evaluated, how their different nature results in different evaluation requirements and goals, and how this may complicate their comparison.

Paper Structure

This paper contains 46 sections, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Methodology and scope
  3. Describing Network Models
  4. Survey Outcomes
  5. Structure of Survey
  6. Taxonomy of Network Performance Models
  7. Network Simulation
  8. Discrete Event Simulation
  9. Parallel Discrete Event Simulation
  10. Summary
  11. Analytical Models
  12. Queuing Theory
  13. Fluid Models
  14. Network Calculus
  15. Algebraic-based Discrete Network Calculus (ADNC)
  16. ...and 31 more sections

Figures (3)

  • Figure 1: Identified models per year of publication and type. Does not consider other types of publications, such as surveys. If the same model is covered in multiple papers, it is counted once in the year of its earliest publication.
  • Figure 2: Taxonomy of network performance models. Solid line arrows indicate direct evolution, while dashed line arrows indicate inspiration or a "response to". Year indicates the year of the earliest publication within that category.
  • Figure 3: Evaluation categories across different model types. If a model is evaluated with multiple categories, they are categorized in the following priority: real data, testbed data, simulated data, and analytical.