Realistic dynamic traffic generation for WDM Optical Networks

Gaizi, Kenza; Abdi, Farid; Abbou, F. M.

doi:10.1109/issc.2016.7528475

Cited by 6 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even recent research works presenting hot topic networking use cases rely on such model [14]. However, these M/M/1 memoryless processes do not fit with the behavior of real dynamic traffic, as suggested in [15]; this can be extended to the traffic generated by 5G services.…”

Section: Introductionmentioning

confidence: 99%

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

Ruiz¹,

Coltraro²,

Velasco³

2018

J. Opt. Commun. Netw.

View full text Add to dashboard Cite

The rapid availability of new services makes that network operators cannot exhaustively test their impact on the network or anticipate any capacity exhaustion. This situation will be worse with the imminent introduction of the 5G technology and the kind of totally new services that it will support. In addition, the increasing complexity of the network makes unreachable analyzing its behavior in front of the specific traffic that needs to be supported, which prevents from training human operators and much less, machine learning algorithms that might automatize network operation. In this paper, we present CURSA-SQ, a methodology to analyze the network behavior when the specific traffic that would be generated by groups of service consumers is injected. CURSA-SQ includes input traffic flow modelling with second and sub-second granularity based on specific service and consumer behaviors, as well as a continuous G/G/1/k queue model based on the logistic function. The methodology allows to accurately study traffic flows at the input and outputs of complex scenarios with multiples queues systems, as well as other metrics such as delays, while showing noticeable scalability. Application use cases include, packet and optical network planning, service introduction assessment, and autonomic networking, just to mention a few.

show abstract

Section: Introductionmentioning

confidence: 99%

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

Ruiz¹,

Coltraro²,

Velasco³

2018

J. Opt. Commun. Netw.

View full text Add to dashboard Cite

show abstract

“…Ref. [16] suggests to model traffic in wavelength division multiplexing (WDM) networks using Pareto process and shows its successful application. The authors of [17], [18] elaborate networkdedicated models based on the gathered observations of the traffic within specific time window.…”

Section: Related Workmentioning

confidence: 99%

“…If the current flow between a pair is higher than currently allocated flow for that pair (i.e., b cur (i, j) < f (t, i, j)), the values of b of f ered (t, i, j) and b of f ered (t) are updated (Alg. 1, lines [16][17] and the special function TDRSA HIGHER FLOW(...) is run in order to serve offered traffic b of f ered (t, i, j) (Alg. 1, line 18).…”

Section: Optimization Problemmentioning

confidence: 99%

See 1 more Smart Citation

Traffic-Aware Service Relocation in Cloud-Oriented Elastic Optical Networks

Goścień

2021

Preprint

View full text Add to dashboard Cite

In this paper, we study problem of efficient service relocation (i.e., changing assigned data center for a selected client node) in elastic optical networks (EON) in order to increase network performance (measured by the volume of accepted traffic).To this end, we first propose novel traffic model for cloud ready transport networks. The model takes into account four flow types (i.e., city-to-city, city-to-data center, data center-to-data center and data center-to-data center) while the flow characteristics are based on real economical and geographical parameters of the cities related to network nodes. Then, we propose dedicated flow allocation algorithm that can be supported by the service relocation process. We also introduce 21 different relocation policies, which use three types of data for decision makingnetwork topological characteristics, rejection history and traffic prediction. Eventually, we perform extensive numerical experiments in order to: (i) tune proposed optimization approaches and (ii) evaluate and compare their efficiency and select the best one. The results of the investigation prove high efficiency of the proposed policies. The propoerly designed relocation policy allowed to allocate up to 3% more traffic (compared to the allocation without that policy). The results also reveal that the most efficient relocation policy bases its decisions on two types of data simultaneously -the rejection history and traffic prediction.

show abstract

Machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks

Rai,

Garg

2024

Proceedings of Third International Conference on Computing and Communication Networks

View full text Add to dashboard Cite

Realistic dynamic traffic generation for WDM Optical Networks

Cited by 6 publications

References 2 publications

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

Traffic-Aware Service Relocation in Cloud-Oriented Elastic Optical Networks

Machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks

Contact Info

Product

Resources

About