TY - JOUR
T1 - Adaptable and Data-Driven Softwarized Networks
T2 - Review, Opportunities, and Challenges
AU - Kellerer, Wolfgang
AU - Kalmbach, Patrick
AU - Blenk, Andreas
AU - Basta, Arsany
AU - Reisslein, Martin
AU - Schmid, Stefan
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - Data-driven networking (DDN)
KW - empowerment
KW - machine learning (ML)
KW - network function virtualization (NFV)
KW - self-driving networks
KW - software-defined networking (SDN)
UR - http://www.scopus.com/inward/record.url?scp=85062429639&partnerID=8YFLogxK
U2 - 10.1109/JPROC.2019.2895553
DO - 10.1109/JPROC.2019.2895553
M3 - Review article
AN - SCOPUS:85062429639
SN - 0018-9219
VL - 107
SP - 711
EP - 731
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
IS - 4
M1 - 8653330
ER -