Optical Burst Switching Networks Research Articles

An architecture to improve performance of software-defined optical networks

The software-defined optical network (SDON) is a revolutionary approach in the field of optical networks. The separation of the control plane and data plane in software-defined networking (SDN) provides enhanced security and simplified network administration. Nevertheless, performance and control plane scalability are significant issues in SDN. SDN performance can be evaluated using parameters such as burst loss, delay, channel occupancy, packet loss, throughput, and average response time. The number of messages exchanged between the data plane and the control plane is used as a metric to determine controller scalability. As the network load increases, the controller experiences a higher flow of messages. It causes delay and burst loss in transmitting the burst. Occasionally, bursts exceed the capacity of the fixed-sized burstifier and are discarded because it takes a long time to identify a suitable route for the burst. Hence, it is essential to minimize the volume of messages exchanged between the control plane and the data plane to improve performance and controller scalability. In this paper, we propose a scalable SDN optical network architecture that minimizes the number of messages exchanged between the data plane and the control plane. We proposed mechanisms like channel reservation, transmission cycles, and guard time between cycles to enhance both the speed and the quality of burst transmission. Prior to transmission, resources or channels are allocated to bursts to minimize the possibility of burst collision and loss. The data plane comprises an optical burst switching (OBS) network, and the flow table entries are periodically updated to minimize inter-plane communication. We perform simulations to evaluate and compare the performance of the proposed architecture with the existing state-of-the-art architecture reported in the literature. The proposed architecture performs better than the existing state-of-the-art in terms of metrics including burst loss, delay, channel occupancy, packet loss, throughput, average response time, and reduction in the number of messages exchanged between the data plane and the control plane. Experimental results indicate a 41% reduction in mean burst loss probability and a 40.5% reduction in mean burst sending delay compared to existing architectures. Additionally, 42.1% fewer messages are exchanged between the control plane and the data plane compared to the number of exchanged messages in existing architectures.

End-to-End Data Flows Management in the Decentralized 5G/6G Mobile Networks

The evolution of 5G and the anticipated emergence of 6G networks demand significant enhancements in optical backhaul infrastructure to support higher bandwidth, low latency, and increased reliability. Considering a wide range of feasible applications with different quality requirements, the key challenge for the underlying backhaul and optical transport network is in the high load variance in the optical switching nodes and complicated data flows management. In this paper, a novel approach is proposed for end-to-end data flows management in decentralized 5G/6G mobile networks, which are interconnected by the optical burst switching transport infrastructure. The key idea is to train a deep recurrent neural network over a real network statistic obtain at the different network segments and service slices. Then, predictions made by deep neural network are used for predictive resource allocation in each node of the optical burst switching network to ensure a target quality of service for each end-to-end data flow. The experimental results show that proposed approach provides 90% accuracy of predictions and allows to effectively utilize the resources of optical network.

Ocean Bottom Distributed Acoustic Sensing for Oceanic Seismicity Detection and Seismic Ocean Thermometry

AbstractA T‐wave is a seismo‐acoustic wave that can travel a long distance in the ocean with little attenuation, making it valuable for monitoring remote tectonic activity and changes in ocean temperature using seismic ocean thermometry (SOT). However, current high‐quality T‐wave stations are sparsely distributed, limiting the detectability of oceanic seismicity and the spatial resolution of global SOT. The use of ocean bottom distributed acoustic sensing (OBDAS), through the conversion of telecommunication cables into dense seismic arrays, is a cost‐effective and scalable means to complement existing seismic stations. Here, we systematically investigate the performance of OBDAS for oceanic seismicity detection and SOT using a 4‐day Ocean Observatories Initiative community experiment offshore Oregon. We first present T‐wave observations from distant and regional earthquakes and develop a curvelet denoising scheme to enhance T‐wave signals on OBDAS. After denoising, we show that OBDAS can detect and locate more and smaller T‐wave events than regional OBS network. During the 4‐day experiment, we detect 92 oceanic earthquakes, most of which are missing from existing catalogs. Leveraging the sensor density and cable directionality, we demonstrate the feasibility of source azimuth estimation for regional Blanco earthquakes. We also evaluate the SOT performance of OBDAS using pseudo‐repeating earthquake T‐waves. Our results show that OBDAS can utilize repeating earthquakes as small as M3.5 for SOT, outperforming ocean bottom seismometers. However, ocean ambient natural and instrumental noise strongly affects the performance of OBDAS for oceanic seismicity detection and SOT, requiring further investigation.

A Hybrid Burst Assembly Algorithm Based on Transition Count Number for OBS Network

Background: Optical transport has emerged as a candidate solution to cope with the rising data transmission challenges of enormously evolving data. In Optical Burst Switching (OBS) networks, determining an adaptive burst size is a difficult task that must be performed efficiently during burst assembling. Methods: This research proposes a hybrid burst assembly algorithm that determines the optimal burst size during the burst creation time. The proposed algorithm uses the Transition Count Number (TCN) based method to maintain the optimal burst size when the incoming traffic is unpredictable. The efficiency of the proposed approach is investigated in terms of queuing delay, burst utilization, burst size, and burst size consistency. Findings: Three types of traffic variations (H = 0.5, H = 0.6, and H = 0.7) are imposed to evaluate the performance of the proposed burst assembly approach. As compared to the E-hybrid (time/length) strategy, the research outcomes demonstrate a 13.15% reduction in average queuing latency and a 21.26% improvement in average burst utilization. Novelty: A new burst assembly approach (hybrid burst assembly) has been proposed for OBS networks. Keywords: Burst assembly, Optical Burst Switching (OBS), burstification, burst consistency

Retracted: Dynamic Wavelength Scheduling by Multiobjectives in OBS Networks

Retracted: Dynamic Wavelength Scheduling by Multiobjectives in OBS Networks

Detection of Burst Header Packet Flooding Attacks via Optimization based Deep Learning Framework in Optical Burst Switching Network

Detection of Burst Header Packet Flooding Attacks via Optimization based Deep Learning Framework in Optical Burst Switching Network

Next-Generation OBS Architecture Transforms 5G Networks Powered by Machine Learning, Probabilistic Modeling and Algorithm Optimisation

Next-generation 5G networks require a high-speed, low-latency, and robust communication backbone to support new applications such as IoT, cloud computing, and virtual reality. Optical burst switching (OBS) is a promising method for 5G networks due to its ability to handle high-speed data transit and excellent bandwidth utilisation. Traditional OBS networks, on the other hand, have a high blocking probability, low resource utilisation, and limited scalability. To address these challenges, this work provides a unique OBS design that integrates machine learning, probabilistic modelling, and efficient algorithms. The usage of machine learning-based burst assembly algorithms, which dynamically predict the best resource allocation for each burst based on network conditions and QoS requirements, is a key component of the proposed architecture. A complete simulation analysis is performed using a typical Wavelength Division Multiplexing (WDM) traffic dataset to evaluate the performance of the proposed architecture. The simulation results show that, as compared to standard OBS networks, the suggested architecture reduces the likelihood of obstruction and improves resource utilisation significantly. Furthermore, when compared to previous OBS systems, the suggested design is more efficient at managing dynamic traffic and enables greater scalability. The simulation study's performance tests demonstrate that the suggested architecture has a blocking probability of less than 10-6, a throughput of more than 95%, and a latency of less than 4 milliseconds. These findings show that the suggested OBS design for next-generation 5G networks is both feasible and effective.

Congestion aware ant colony optimisation algorithm for routing and spectrum assignment in flexi-grid optical burst switching networks

This paper presents an improved distributed ant colony optimisation algorithm for routing and spectrum assignment (RSA) on optical burst switched (OBS), flexible spectrum networks, with the spectrum continuity constraint imposed. The improved algorithm incorporates a distributed method for monitoring the congestion along the links and using this information to determine what route-spectrum combination should be selected to minimise the burst loss probability (BLP). Evaluations were performed on an optical burst switching simulator to determine the effect on BLP when a dynamic route congestion measure is considered as opposed to the use of the static route length measure. The evaluations, considering the effects of optical impairments, were made on different network topologies, with multiple spectrum widths provisioned on the network, and with different loads. The incorporation of congestion measures into the algorithm showed improvements in BLP over previous work in the range of 2–32% depending on the evaluation conditions.

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

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.

Impact of buffering time reduction on false congestion detection in TCP vegas over OBS networks

Since Transmission Control Protocol (TCP) is the dominant protocol on the Internet and optical burst switching (OBS) networks represent the optical transmission solution that can meet future high bandwidth requirements, TCP and OBS networks represent a possible combination for the next-generation Internet. However, a problem with this combination model is that operations at the OBS layer need to be controlled so that false congestion detection at the TCP layer is minimised. Considering the combination of TCP Vegas and OBS, congestion detection at the TCP layer is based on the round-trip time (RTT), where congestion is detected if the current RTT exceeds a given RTTmax. The extra delay may be due to some actual loss, but it may also be due to the extended execution time caused by some OBS layer operations. Therefore, reducing the time of operations at the OBS layer will reduce false congestion detection at the TCP layer. This paper investigates the impact of reducing the buffering time at the ingress node on the false congestion detection rate at the TCP layer. The reduction of the burst buffering time is accomplished by nesting the offset time in the assembly time. The simulation results and analysis show that the transmission efficiency in TCP Vegas over OBS networks is significantly improved; the throughput remains high, and the delivery success rate is significantly increased compared to the conventional buffering mode.

A Combined Delay-Throughput Fairness Model for Optical Burst Switched Networks

Fairness is an important feature of communication networks. It is the distribution, allocation, and provision of approximately equal or equal performance parameters, such as throughput, bandwidth, loss rate, and delay. In an optical burst switched (OBS) network, fairness is considered in three aspects: distance, throughput, and delay. Studies on these three types of fairness have been conducted; however, they have usually been considered in isolation. These fairness types should be considered together to improve the communication performance of the entire OBS network. This paper proposes a combined delay-throughput fairness model, where burst assembly and bandwidth allocation are improved to achieve both delay fairness and throughput fairness at ingress OBS nodes. The delay fairness and throughput fairness indices are recommended as metrics for adjusting the assembly queue length and allocated bandwidth for priority flows. The simulation results showed that delay and throughput fairness could be achieved simultaneously, improving the overall communication performance of the entire OBS network.

Performance Comparison of JIT and JET Protocols on the OBS Networks

Over the past few years, internet traffic has been explosive growth due to users' need for more applications and services. All these developments on the internet need more bandwidth. The Optical Burst Switching (OBS) mechanism along with Wavelength-Division-Multiplexing (WDM) technology is very useful in meeting the high bandwidth requirements. However, the OBS network uses a one-way resource reservation mechanism, as well as it is a buffer lack at the intermediate (core) node. Therefore, the OBS network depends on contention resolution mechanisms to achieve acceptable levels of performance. In this paper, the Deflection Routing (DR) mechanism is used to overcome contention. The performance of the OBS network has been estimated by executing Just-In-Time (JIT) and Just-Enough-Time (JET) signaling reservation protocols with the DR. NCTUns-6.0 simulator was employed to verify the performance of the OBS network based on two metrics: throughput and packets dropped in the network. The results show that the bandwidth resources utilization in the OBS network with JET and DR (OBS-JET-DR) is better than in the OBS network with JIT and DR (OBS-JIT-DR). However, the average number of packets dropped in OBS-JET-DR is less than OBS-JIT-DR by 21%. In addition, the throughput of OBS-JET-DR has increased by 4% compared with OBS-JIT-DR.

Energy Efficient Waveband Translucent Optical Burst Switching Network

The fast development of translucent Optical Burst Switching (OBS) technology, which employs multiple wavelengths per fiber, increases the size (i.e., the number of ports) as well as the cost and power consumption of Optic Cross-Connects (OXCs). This paper studies the impact of OBS network parameter settings, where switching is performed at the Waveband Switching (WS) granularity using an end-to-end grouping method to reduce the number of lightpaths and switch ports. The simulation results illustrate that using OBS networks with WS by the suitable parameter settings, namely the Burstification Time (BT) and waveband granularity, offers considerable energy savings in the core network compared to using an OBS network without WS, where overall power consumption is decreased by maximizing BT values and waveband granularity.

Contention avoidance scheme using machine learning inspired deflection routing approach in optical burst switched network

SummaryOptical transport has emerged as a candidate solution to cope with the rising data transmission challenges of enormously evolving data. In the bufferless environment, only the ingress node beholds the buffering to speed up the data transfer in the form of data bursts. However, such networks are prone to data contention issues that degrade the overall performance and eventually result in burst loss. The paper proposes a machine learning (ML) inspired deflection routing (DR) algorithm to overcome contention issues in optical burst switching (OBS) network. The contending burst is first evaluated and assigned the priority order as per the available bits. Based on the burst traffic analysis, support vector machine (SVM) kernels are trained to select an alternate route and redirected the bursts to a new output node. It has been observed that for 65% of cases, radial basis function (RBF) kernel demonstrated the best results among the three kernel functions. The work further shows that ML offers a wiser decision to avoid and overcome contention in the deflection of the route of the contenting bursts. Moreover, the proposed DR scheme has achieved the highest throughput and packet delivery ratio (PDR) with an average delay of 9.66 s using an OBS network deployed with 10 groups of nodes. When compared against the contention scenario and the existing work, the proposed DR outperformed the existing technique with a 4% to 11% margin.

Performance analysis comparison of optical burst switching networks' contention resolution techniques

<p><span>Optical burst switching (OBS) is a transporting network for the optical internet in the years ahead vision. As OBS depends on statistical multiplexing, the effect of contention resolution is the main issue to achieve a low probability losing of burst. Basically, there are different ways to resolve contentions in the OBS networks like wavelength conversion, deflection routing, burst segmentation and optical buffering using fiber delay lines. Burst segmentation and deflection routing as well as fiber delay lines technologies, are discussed among the various accessible conflict resolution techniques in this study. However, the main aim of this study is to demonstrate that, the performance of fiber delay line (FDL) is better than other techniques to resolve contention by comparing the performance of these different schemes based on burst losing probabilities and the data handling capability. To evaluate the performance, appropriate mathematical formulae were used. Under the MATLAB environment, the performance was measured based on the probability of burst loss versus incoming traffic (load). The outcomes suggest that deflection routing outperforms fiber delay lines and burst segmentation in the OBS network in terms of resolve the contention. </span></p>

Extended Evaluation of Loss-Based TCP Variants Performance Over Optical Burst Switching (OBS) Networks for Case W x =W max

Extended Evaluation of Loss-Based TCP Variants Performance Over Optical Burst Switching (OBS) Networks for Case W x =W max

Retracted] Dynamic Wavelength Scheduling by Multiobjectives in OBS Networks

The burst dropping ratio is witnessed in the contemporary literature as a considerable constraint of optical burst switching (OBS) networks that attained many researchers’ efforts in the recent past. Among the multiple practices endeavoring to reduce the burst drop ratio, the optimal burst scheduling is one dimension in this regard. The transmission channel scheduling and appropriate wavelength allocation are critical objectives to achieve optimal burst scheduling in regard to minimal burst drop ratio. Many of the scheduling models depicted in the contemporary literature aimed to achieve the optimum scheduling by electing the channels, which depend on optimum utilization of idle time. Some of the studies tried to select channels by any metrics of quality, and significantly minimal amount of studies focused on wavelength allocation for lowering BDR. Moreover, in regard to this, this study tried to achieve optimum wavelength allocation beneath manifold objective QoS metrics, which is identified as “multiobjective dynamic wavelength scheduling (DyWaS).” The experimental study carried through the simulations evinced that the proposed model DyWaS escalated the optimality of burst scheduling through wavelength allocation compared with other existing methods represented in the contemporary literature.

QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

To optimize the QoS of optical burst switching networks, a QoS routing optimization algorithm based on a multi-objective genetic algorithm is proposed. A Bayesian network model is used to locate the fault of optical burst switching network and obtain the fault location of the transmission link of optical burst switching network; In this position, the routing optimization algorithm based on a multi-objective genetic algorithm transforms the multi constrained network quality of service routing optimization problem into a constrained multi-objective routing optimization problem. Under multiple constraints, the best path of optical burst switching network service is obtained to realize the optical burst switching network quality of service routing optimization. The results show that after applying the proposed algorithm, the average delay of video, text and picture transmission in an optical burst switching network is less than 400ms. The proposed algorithm can improve the packet delivery rate of information transmission in an optical burst switching network, reduce the transmission delay, blocking probability and use cost of an optical burst switching network, and optimize the service quality of an optical burst switching network.

Optimal Algorithms for Load Balancing in Optical Burst Switching Networks

Data packet drop can happen in Optical Burst-Switched (OBS) when two data bursts are competing on the same wavelength. Recently, many techniques have been developed to solve this problem but they do not consider the congestion. Also, it is necessary to balance the load system in the OBS network. The Ant Colony Optimization (ACO) technique can be applied to determine the straight and the safest route. However, the ACO technique raises both power utilization as well as the execution time. In this study, Cuckoo Search (CS) and ACO methods based approach is proposed to avoid the congestion and load balancing in the OBS network. This strategy evaluates the intensity of hotspot data and then launches the congestion rate optimization process that depends on their load situations. The congestion rate optimization represents the available bandwidth, data distribution rate, queue size, and access rate, and also these factors are optimized through the CS technique. The fitness utility in the CS technique adjusts the distribution rate in the OBS network, and the proposed ACO technique solves the energy utilization problem. The simulation results proved that the presented strategy evades both the end-to-end delay as well as the possibility of packet drop.

A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks

An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security.