Burst rate based optimized IO queue management for improved performance in Optical Burst Switching Networks

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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Impact of Fuzzy Offset Time on Delay and Burst Loss Ratio for Optical Burst Switching Networks

Naji¹,

Eltahir²,

Elsheikh³

2023

IJEAT

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Optical burst switching (OBS) is an optical switching technology; it uses an optical fiber's high bandwidth potential to transfer huge amounts of data in the form of huge packets, which are more commonly referred to as bursts. High burst loss brought on by numerous burst contentions is a significant problem in OBS. An intelligent fuzzy offset time algorithm (FOT) algorithm is suggested to overcome this problem. This study proposed Intelligent Fuzzy Offset Time (FOT) algorithms that adjust offset time (OT) in accordance with network and traffic conditions. The fuzzy input for FOT is made up of three parameters: burst size, distance, and time that burst spent in queuing. The suggested algorithm is assessed versus the Intelligent OT algorithms using the Five defuzzification techniques (Centroid (CM00), Bisector (BM04), largest of maximum (LM02), smallest of maximum (SM03), and mean of maximum (MM01) when Maximum (M) accumulation technique is used, when using Algebraic Sum (S) aggregation methods (Centroid (CS00), Bisector (BS04), largest of maximum (LS02), smallest of maximum (SS03), and mean of maximum (MS01). Simulation results have shown that FOT LM02, FOT LS02, FOT SM03 and FOT LS02 have effects on reducing BLR (burst loss ratio) and E2E (End-2-End) delayed respectively when compared to other defuzzification techniques algorithms. FOT LM02 and FOT SM03 can be used to intelligently adjust the offset parameter using the incoming traffic load and the three parameters.

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Burst rate based optimized IO queue management for improved performance in Optical Burst Switching Networks

Cited by 3 publications

References 13 publications

Machine Learning for Optical Communication to Solve Pervasive Issues of Internet of Things

Machine Learning for Optical Communication to Solve Pervasive Issues of Internet of Things

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

Impact of Fuzzy Offset Time on Delay and Burst Loss Ratio for Optical Burst Switching Networks

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