Modeling of optical burst switching networks

Holá, Michaela; Scholtz, Lubomir; Ladanyi, Libor; Müllerová, Jarmila

doi:10.1117/12.2518407

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Considering the fact that integrating some of the functionalities of security techniques require real-time processing of optical signal, the use of deep learning techniques for this purpose are questionable [11]. Since OBS networks are seen as the solution for future high-speed optical networks [19], research on potential threats should be continued. V. CONCLUSION Although optical networks have unique properties such as broadband operation, optical processing-instantaneous response, electromagnetic immunity, low latency and compactness, it is known that they are vulnerable to various attacks, including physical infrastructure attacks, jamming, eavesdropping, and interception; therefore, the physical layer security of optical networks cannot be overlooked.…”

Section: B Classifiersmentioning

confidence: 99%

Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks

Efeoglu

Tuna

2021

Balkan Journal of Electrical and Computer Engineering

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Optical burst switching networks are vulnerable to various threats including Burst Header Packet Flooding attack, Circulating Burst Header attack, Address Spoofing, and Replay attack. Therefore, detecting such threats play a key role in taking appropriate security measures. One of the major challenges in identifying the risks of Burst Header Packet flooding attacks is the lack or insufficiency of reliable historical data. In this paper, firstly, Burst Header Packet flooding attacks are classified into four categories, Misbehaving-Block, Behaving-No Block, Misbehaving-No Block and Misbehaving-Wait, using Sequential Minimal Optimization (SMO) and K* algorithms. Using performance metrics obtained both after testing on the same set and after applying 10-fold cross validation, the performance of SMO and K* algorithms is compared based on commonly used performance metrics. As the results show, compared to SMO, K* algorithm is more suitable for predicting Burst Header Packet Flooding attacks.

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Section: B Classifiersmentioning

confidence: 99%

Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks

Efeoglu

Tuna

2021

Balkan Journal of Electrical and Computer Engineering

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Study of Different Burst Scheduling Algorithms Using FDLs as QoS in Wavelength Division Multiplexing OBS Networks

Netak

Chowdhary

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Optical burst switching (OBS) is envisioned to be one of the most promising technology to support all-optical switching in the optical Internet. OBS is finding potential in bandwidth hungry applications like, grid computing, video conferencing, video telephony and video on demand. In this critical high speed Internet based applications, where bursts losses should be minimized. In OBS based networks, there exists the possibility that bursts may contend with one another at intermediate nodes. Typically, contention resolution in IP networks is implemented by storing excess packets in electronic buffer (RAM). However, electronic like buffer is unavailable and optical buffer is constructed from fiber delay lines (FDL). An FDL is simply the length of fiber and offer a deterministic delay. We present simulation results which show the effect of the use of FDLs of various delays to reduce bursts losses. The simulation results show that such limited optical buffers can significantly reduces burst losses in the network.

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Adaptive burst reordering for burst-cluster transmission to improve fairness in OBS networks

Nassar

Tachibana

2020

JHS

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Hop-based burst-cluster transmission has been proposed to improve fairness in optical burst switching networks, and a burst-cluster is generated from multiple bursts. By arranging bursts from the smallest number of hops to the largest one, the burst loss probability for a small (large) number of hops increases (decreases), improving fairness. However, depending on the amount of traffic on each link, the burst loss probability for a small number of hops becomes larger than that for a large number of hops for some source nodes. Therefore, all source nodes can not always improve fairness. In this paper, an adaptive burst reordering algorithm for the hop-based burst-cluster transmission is proposed. Here, each source node calculates the burst loss probability for each number of hops from the number of received ACK and NACK messages. Next, the source node changes the order of bursts within a burst-cluster dynamically. The performance of the proposed method has been evaluated in tandem networks and mesh networks such as NSFNET and ARPA2 by simulation. Numerical examples show that the proposed method is effective for improving the local fairness for each source node regardless of the amount of traffic on each link.

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Modeling of optical burst switching networks

Cited by 4 publications

References 7 publications

Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks

Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks

Study of Different Burst Scheduling Algorithms Using FDLs as QoS in Wavelength Division Multiplexing OBS Networks

Adaptive burst reordering for burst-cluster transmission to improve fairness in OBS networks

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