Machine Learning Methods for Traffic Prediction in Dynamic Optical Networks with Service Chains

Szostak, Daniel; Walkowiak, Krzysztof

doi:10.1109/icton.2019.8840301

Cited by 8 publications

(3 citation statements)

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“…This work is a continuation and extension of our recent paper [23]. We focus on predicting future requests flow in optical networks with SFCs.…”

Section: Introductionmentioning

confidence: 88%

“…The motivation behind such setup is that -in our opinion -it is close to real traffic: the single VNF can appear in more than one chain and thanks to the determined number of chains in case of each topology we can compare results obtained based on different topologies. Moreover, similar setup brought the best classification quality in [22]. Number of requests in each TI was generated using the Poisson distribution.…”

Section: Used Datasetsmentioning

confidence: 99%

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

Szostak

Walkowiak

2020

Foundations of Computing and Decision Sciences

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Knowledge 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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“…This work is a continuation and extension of our recent paper [23]. We focus on predicting future requests flow in optical networks with SFCs.…”

Section: Introductionmentioning

confidence: 88%

Section: Used Datasetsmentioning

confidence: 99%

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

Szostak

Walkowiak

2020

Foundations of Computing and Decision Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…The obtained MAPE values varied between 1 and 10%. Authors in [20], [21] and [22] present future traffic forecasting by predicting the occurrence of future requests in the network. Each request consists of a source node, a destination node, and request volume information.…”

Section: Related Workmentioning

confidence: 99%