Adaptable and Data-Driven Softwarized Networks

Review, Opportunities, and Challenges[40pt]

Wolfgang Kellerer, Patrick Kalmbach, Andreas Blenk, Arsany Basta, Martin Reisslein, Stefan Schmid

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Communication networks are the key enabling technology for our digital society. In order to sustain their critical services in the future, communication networks need to flexibly accommodate new requirements and changing contexts due to emerging diverse applications. In contrast to traditional networking technologies, software-oriented networking concepts, such as software-defined networking (SDN) and network function virtualization (NFV), provide ample opportunities for highly flexible network operations, enabling fast and simple adaptation of network resources and flows. This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarized networks. We first explain how softwarized networks contribute to network adaptability through the functional primitives observation, composition, and control. We review the wide range of options for fine-granular observations as well as fine-granular composition and control provided by SDN and NFV. The multitude of fine-granular ``tuning knobs'' in adaptable softwarized networks complicates the decision making, which is the main focus of this paper. We propose to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation, composition, and control. The data-driven decision making modules can learn and react to changes in the environment, e.g., new flow demands, so as to support meaningful decision making for adaptation in softwarized networks. Finally, we make the case for employing the concept of empowerment to realize truly ``self-driving'' networks.

Original languageEnglish (US)
JournalProceedings of the IEEE
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Decision making
Chemical analysis
Telecommunication networks
Knobs
Learning systems
Tuning
Network function virtualization
Software defined networking

Keywords

  • Communication networks
  • Data centers
  • Data-driven networking (DDN)
  • Decision making
  • empowerment
  • Europe
  • Machine learning
  • machine learning (ML)
  • Network function virtualization
  • network function virtualization (NFV)
  • Optimization
  • self-driving networks
  • software-defined networking (SDN).

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Adaptable and Data-Driven Softwarized Networks : Review, Opportunities, and Challenges[40pt]. / Kellerer, Wolfgang; Kalmbach, Patrick; Blenk, Andreas; Basta, Arsany; Reisslein, Martin; Schmid, Stefan.

In: Proceedings of the IEEE, 01.01.2019.

Research output: Contribution to journalArticle

Kellerer, Wolfgang ; Kalmbach, Patrick ; Blenk, Andreas ; Basta, Arsany ; Reisslein, Martin ; Schmid, Stefan. / Adaptable and Data-Driven Softwarized Networks : Review, Opportunities, and Challenges[40pt]. In: Proceedings of the IEEE. 2019.
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