Service Function-Oriented Topology Aggregation in Multi-Domain Inter-DC Elastic Optical Networks

2018 ◽  
Author(s):  
Boyuan Yan ◽  
Yongli Zhao ◽  
Xiaosong Yu ◽  
Wei Wang ◽  
Jie Zhang
Keyword(s):  
Optical Networks ◽  
Service Function ◽  
Topology Aggregation

Service Function Path Provisioning With Topology Aggregation in Multi-Domain Optical Networks

2020 ◽  
Vol 28 (6) ◽  
pp. 2755-2767
Author(s):  
Boyuan Yan ◽  
Yongli Zhao ◽  
Xiaosong Yu ◽  
Yajie Li ◽  
Sabidur Rahman ◽  
...  
Keyword(s):  
Optical Networks ◽  
Service Function ◽  
Topology Aggregation

Dependence-Aware Service Function Chain Embedding in Optical Networks

2019 ◽  
Author(s):  
Danyang Zheng ◽  
Evrim Guler ◽  
Chengzong Peng ◽  
Guangchun Luo ◽  
Ling Tian ◽  
...  
Keyword(s):  
Optical Networks ◽  
Service Function ◽  
Service Function Chain

scholarly journals Application of Machine Learning Algorithms for Traffic Forecasting in Dynamic Optical Networks with Service Function Chains

2020 ◽  
Vol 45 (3) ◽  
pp. 217-232
Author(s):  
Daniel Szostak ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Machine Learning Algorithms ◽  
Network Function Virtualization ◽  
Service Function ◽  
Traffic Forecasting ◽  
Network Function ◽  
Traffic Characteristics

AbstractKnowledge about future optical network traffic can be beneficial for network operators in terms of decreasing an operational cost due to efficient resource management. Machine Learning (ML) algorithms can be employed for forecasting traffic with high accuracy. In this paper we describe a methodology for predicting traffic in a dynamic optical network with service function chains (SFC). We assume that SFC is based on the Network Function Virtualization (NFV) paradigm. Moreover, other type of traffic, i.e. regular traffic, can also occur in the network. As a proof of effectiveness of our methodology we present and discuss numerical results of experiments run on three benchmark networks. We examine six ML classifiers. Our research shows that it is possible to predict a future traffic in an optical network, where SFC can be distinguished. However, there is no one universal classifier that can be used for each network. Choice of an ML algorithm should be done based on a network traffic characteristics analysis.

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