TY - GEN
T1 - Towards virtualization of software-defined networks
T2 - 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
AU - Blenk, Andreas
AU - Kellerer, Wolfgang
N1 - Publisher Copyright:
© 2019 IFIP.
PY - 2019/5/16
Y1 - 2019/5/16
N2 - Today's networks lack the support to satisfy the highly diverse and fast changing demands of emerging applications and services. The paradigms Network Virtualization (NV) and Software-Defined Networking (SDN) can potentially overcome this impasse. The virtualization of software-defined networks is expected to bring dynamic resource sharing with guaranteed performance through NV and programmability through SDN; for the first time, tenants can program their requested network resources according to their service demands in a timely manner. However, the virtualization of SDN-based networks introduces new challenges for operators, e.g., a virtualization layer that provides low and guaranteed control plane latencies for tenants. Moreover, tenants' expectations range from a fast, nearly-instantaneous provisioning of virtual networks to predictable operations of virtual networks. With this paper, we give a comprehensive overview of the thesis, which can be split into three parts - a journey in three acts. The thesis first presents a measurement procedure and a flexible virtualization layer design for the virtualization of software-defined networks. Focusing on the control plane, it introduces mathematical models for analyzing four virtualization layer architectures. Third, for a fast and efficient virtual network provisioning on the data plane, the thesis proposes optimization systems using Machine Learning and Neural Computation.
AB - Today's networks lack the support to satisfy the highly diverse and fast changing demands of emerging applications and services. The paradigms Network Virtualization (NV) and Software-Defined Networking (SDN) can potentially overcome this impasse. The virtualization of software-defined networks is expected to bring dynamic resource sharing with guaranteed performance through NV and programmability through SDN; for the first time, tenants can program their requested network resources according to their service demands in a timely manner. However, the virtualization of SDN-based networks introduces new challenges for operators, e.g., a virtualization layer that provides low and guaranteed control plane latencies for tenants. Moreover, tenants' expectations range from a fast, nearly-instantaneous provisioning of virtual networks to predictable operations of virtual networks. With this paper, we give a comprehensive overview of the thesis, which can be split into three parts - a journey in three acts. The thesis first presents a measurement procedure and a flexible virtualization layer design for the virtualization of software-defined networks. Focusing on the control plane, it introduces mathematical models for analyzing four virtualization layer architectures. Third, for a fast and efficient virtual network provisioning on the data plane, the thesis proposes optimization systems using Machine Learning and Neural Computation.
KW - Machine Learning
KW - Network Measurements
KW - Network Virtualization
KW - Neural Computation
KW - Optimization
KW - Software-Defined Networking
UR - http://www.scopus.com/inward/record.url?scp=85067001784&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85067001784
T3 - 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
SP - 677
EP - 682
BT - 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 April 2019 through 12 April 2019
ER -