Optical Burst Switching Research Articles

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.

A model of service differentiation burst assembling and padding for improving transmission efficiency in OBS networks

Service differentiation is an indispensable requirement for transmission in optical burst switching (OBS) networks, which can be based on offset-time, burst-length, or both, offset-time and burst-length. The offset time based approach sets a large offset time for high priority bursts and a small offset time for low priority bursts. Whereas, with burst length based approach, high priority bursts are short in size and low priority bursts are long in length. A combination of these two approaches promises to provide flexible service differentiation. The paper proposes a model of service differentiation burst assembling and padding, in which the assembly time threshold is set to reduce the end-to-end delay, but does not effect the burst length prediction accuracy; the length of generated bursts are flexibly adjusted based on feedbacked void size; and the high-priority burst with short length is padded by the lower priority data. Simulation results and analysis show that the proposed model is more efficient than previous similar models in terms of bandwidth utilization, byte loss, throughput fairness and estimation error.

Modeling of a controlled retransmission scheme for loss recovery in optical burst switching networks

Retransmission in optical burst switching networks is a solution to recover data loss by retransmitting the dropped burst. The ingress node temporarily stores a copy of the complete burst and sends it each time it receives a retransmission request from the core node. Some retransmission schemes have been suggested, but uncontrolled retransmission often increases the network load, consumes more bandwidth, and consequently, increases the probability of contention. Controlled retransmission is therefore essential. This paper proposes a new controlled retransmission scheme for loss recovery, where the available bandwidth of wavelength channels and the burst lifetime are referred to as network conditions to determine whether to transmit a dropped burst. A retrial queue-based analysis model is also constructed to validate the proposed retransmission scheme. The simulation and analysis results show that the controlled retransmission scheme is more efficient than the previously suggested schemes regarding byte loss probability, successful retransmission rate, and network throughput.

Allocation of channels over optical burst switching (OBS) networks in smart cities using integrated statistical techniques

Allocation of channels over optical burst switching (OBS) networks in smart cities using integrated statistical techniques

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

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 Naive Bayes and K-Nearest Neighbor algorithms. Using performance metrics obtained both after testing on the same set and after applying 10-fold cross validation, the performance of Naive Bayes and K-Nearest Neighbor algorithms is compared based on commonly used performance metrics. As the results show, compared to Naive Bayes, K-Nearest Neighbor algorithm is more suitable for predicting Burst Header Packet Flooding attacks.

A Comparative Analysis of Semi-Supervised Learning in Detecting Burst Header Packet Flooding Attack in Optical Burst Switching Network

This paper presents a comparative analysis of four semi-supervised machine learning (SSML) algorithms for detecting malicious nodes in an optical burst switching (OBS) network. The SSML approaches include a modified version of K-means clustering, a Gaussian mixture model (GMM), a classical self-training (ST) model, and a modified version of self-training (MST) model. All the four approaches work in semi-supervised fashion, while the MST uses an ensemble of classifiers for the final decision making. SSML approaches are particularly useful when a limited number of labeled data is available for training and validation of the classification model. Manual labeling of a large dataset is complex and time consuming. It is even worse for the OBS network data. SSML can be used to leverage the unlabeled data for making a better prediction than using a smaller set of labelled data. We evaluated the performance of four SSML approaches for two (Behaving, Not-behaving), three (Behaving, Not-behaving, and Potentially Not-behaving), and four (No-Block, Block, NB- wait and NB-No-Block) class classifications using precision, recall, and F1 score. In case of the two-class classification, the K-means and GMM-based approaches performed better than the others. In case of the three-class classification, the K-means and the classical ST approaches performed better than the others. In case of the four-class classification, the MST showed the best performance. Finally, the SSML approaches were compared with two supervised learning (SL) based approaches. The comparison results showed that the SSML based approaches outperform when a smaller sized labeled data is available to train the classification models.

The intelligent water drops algorithm for improving quality of service in OBS network

The most significant difficulties in optical burst switching networks is the nature of service arrangement. A few propositions have been made and a few restrictions have been watched taking into account the material used and existing contention resolution techniques in optical networks. An intelligent effective strategy that improves the quality of service in such networks dependent on intelligent water drops(IWDs). Movement of burst depends upon the material used in optical fibre and scheduling algorithms used for scheduling of bursts. Intelligent water drops strategy manages optical burst misfortune during conflict goal measure and offers a separated support procedure utilizing organized dispute goal arrangements in the network centre. Likewise, wasteful information channel arrangement calculation element burst misfortune favored OBS. The proposed strategy is researched through broad simulations. The simulation results show that using intelligent water drops burst loss rate can be decreased for both low and high traffic loads contrasted with existing schemes.

The optical properties of different materials used for thermo-optical switches

Abstract With growing demands of internet protocol services for transmission capacity and speed, the solution for future high speed optical networks is optical burst switching that is a technology for transmitting large amounts of data bursts through a transparent optical switching network the optical switches in optical burst switching networks play important role in the resource reservation and are very important to ensure reliability and flexibility of the network. This paper is focused on the very important components of Optical Burst Switching networks, ieo ptical switches, specifically thermo-optical switches. In this paper are presented the simulation analysis of performance evaluation of thermo-optical switches executed in the model of Optical Burst Switching network and simulation study of investigation of influence of roughness and layer thickness on the optical properties (spectral reflectance, transmittance) of selected materials (SiO2, Ta2O5, Al2O3) for thermooptical switches.

Detection of Flooding Attack on OBS Network Using ant Colony Optimization and Machine Learning

Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS). However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.

An improvement over TCP Vegas to enhance its performance in optical burst switching networks

The demand for high bandwidth on the Internet is growing drastically, and one of the solutions for tackling this problem is using optical networks. Burst switching is one of the techniques that can be used in optical networks to handle high traffic. Aside from the many advantages that this technique has, it suffers from a big flaw called burst contention. Optical burst switching (OBS) is a switching technique without any buffering system. As a result, when two bursts are trying to reserve one resource, one of them drops. This drawback can have a significant impact on the performance of some protocols like TCP because they have not been designed to perform in a network without any queuing system and cannot distinguish a drop is because of the congestion or contention. In this paper, a new algorithm called AVGR (Average of RTTs) is proposed based on some mathematical equations to prevent the degradation of TCP. It tries to calculate averages for some RTTs in three different periods. Then base on the obtained results, the congestion control mechanism will be modified. The primary goal of the algorithm is to determine the current status of the network and make proper decisions based on it.

Optimisation of optical burst switched networks using multi-objective BAT algorithm

There is a growing demand for OBS (optical burst switching) networks, which is based on the concept of providing switching of data bursts without any intermediate switching and O/E/O conversions. The OBS technology is very economical and flexible to create new definitions for networking processes. OBS networks can also help in creating several types of new services which involve more bandwidth usage. The present research paper focuses on the OBS network and its benefits. The major problems of burst contention and congestion control using OBS networks are addressed in this research. In this paper, a possible solution to burst contention and congestion control is presented by the use of an algorithm, called BAT algorithm. The multi-objective optimisation problem is solved using a multi-objective BAT algorithm (MOBA). The proposed algorithm presented effective results to maximise the traffic demand, burst loss ratio minimisation, and reduction in overall burst block probability.

Modified Optical Burst Switching (OBS) Based Edge Node Architecture Using Real-Time Scheduling Techniques

Contention is the main problem in optical burst switching (OBS) systems that lead to loss more burst by dropping the contended burst. To deal with this problem, we propose a new proactive method which is based on the integration of Real-Time Scheduling Techniques: Earliest-deadline-first (EDF) and round-robin (RR). Based on this consideration, this paper proposes a series of novel coherent interdependence methods to propose a novel OBS edge node Architecture: (i) An adaptive priority system to obtain QoS guarantees, (ii) Admission control and bandwidth distribution to maximize the link throughput. The proposed technique classifies the incoming data packets into two categories: granted service (GS) for high priority and best effort (BE) for low priority. The RR technique is used for scheduling different BE and GS packets when they have the same deadlines while the EDF is used to manage GS packets with different deadlines. In addition, the admission and bandwidth assignment policies vary in function of traffic characteristics. For GS traffic, only the bandwidth used is needed, while the remaining bandwidth is assigned to best effort traffic. With the EDF and RR scheduling algorithms, the proposed edge node design obtains a brand new architecture. The proposed OBS architecture has been tested for dynamic traffic in which both BE and GS traffic arrives according to a Poisson distribution for different scenarios based on traffic distribution and switch resources reservation. In scenario#1, 90% GS traffic and 10% BE traffic and reserve 7% of the switch resources for BE traffic are considered. Therefore, the proposed architecture provides a higher grade of service to the end-users with the guaranteed service level of agreements than the existing architecture that does not implement resource reservation protocols. In addition, the modified OBS-edge node is designed and implemented on FPGA Virtex-11C2V40. The hardware performance analysis shows that the network dimension is a key factor in latency measurement. A 2×2 network dimension can serve approximately 230 Mbps throughput and approximately 400 Mbps in the case when the number of flits (segment of burst) is twice.

Optimization Technology of Optical Fiber Communication Network Based on Service Classification

Optical fiber has huge bandwidth, as a physical medium, it has good anti electromagnetic interference characteristics and excellent security performance, which is very suitable for the establishment of backbone transmission network. Wavelength division multiplexing(WDM) network based on optical fiber has been widely used. Using optical fiber for information transmission can give full play to the nearly unlimited transmission capacity of optical fiber. With the advent of the information age, data service has become the mainstream business, replacing the original telephone voice service. The great development of data transmission service further promotes the continuous development of WDM network technology. Among all kinds of services in optical network, IP service accounts for the largest proportion. In optical networks, switching is an important technology. Network performance is directly related to switching technology. With the development of optical communication technology, a variety of optical switching devices are introduced into WDM optical network, such as optical packet switching, optical burst switching and so on. This makes WDM layer have the ability of dynamic path establishment and removal similar to IP layer. In order to improve the performance of the optical network and reduce the blocking rate of the network, in WDM optical network, the services are designed in different levels. For some very important services, the highest priority is given, and the optical path channel with better survivability and higher fault tolerance is selected. For general services, it can be designed as a lower level service, and choose the appropriate optical path channel for transmission. The experimental results show that this hierarchical service design method can make more effective use of network resources, reduce the network blocking rate, and improve the overall performance of the network.

Enhanced packet routing queuing model in optical burst switching network using queue-based dynamic optical route scheduling

Optical Burst Switching (OBS) for all future optical network technology signatures. In OBS, the IP packet is known as a large explosion of data that is assembled in the input packets. The pre-assembly burst is sent to its intended location for offset time. It has shown that there are some security issues, but they do not resolve the node's ability to separate them. When they have suffered great losses to ensure that they are indispensable. If they are not safe, it may not waste resources on the optimal path.Current communication paradigms such as broadcast and multicast. Therefore, in optical networks, it is necessary to support these paradigms. Implementation of IP QoS policies does not apply to Optical Burst Switching (OBS) networks. These QoS requirements include controversy, optical signal quality, reliability, and delay.This work recommends the use of shared encryption for secure message traffic on OBS networks. The information is divided into as many strands as possible, and then it transmits at different times between pair of nodes and the different paths or routes. The proposed method to analyze the three strategies likely traffic analysis and route scheduling, packet header analysis,and authentication key verification.This proposed method,Queue-Based Dynamic Optical Route Scheduling (QDORS), provides flexible and efficient integrated packet-based traffic in OBS functions and Internet-based networks. It considered exploding a valid huge amount of data plans to reduce better channel usage, higher efficiency, and blocking probability.

A Bursts Contention Avoidance Scheme Based on Streamline Effect Awareness and Limited Intermediate Node Buffering in the Core Network

In an Optical Burst Switched (OBS) network, data packets sourced from peripheral networks are assembled into huge sized data bursts. For each assembled data burst, an associated control signal in the form of a burst control packet is (BCP) is generated and scheduled at an offset time ahead of the data burst. The offset timing is to allow for the pre-configuration of required resources at all subsequent intermediate nodes prior to the actual data burst’s arrival. In that way, the data burst will fly-by each node and hence no requirement for temporary buffering at all intermediate nodes. An operational requirement of an OBS network is that it be loss-less as in that way a consistent as well as acceptable quality of service (QoS) for all applications and services it serves as a platform can be guaranteed. Losses in such a network are mainly caused by improper provisioning as well as dimensioning of resources thus leading to contentions among bursts and consequently discarding of some of the contending data bursts. Key to both provisioning as well as proper dimensioning of the available resources in an optimized way is the implementation of effective routing and wavelength (RWA) that will seclude any data losses due to contention occurrences. On the basis of the effects of the streamline effect (SLE), that is, effectively secluding primary contention among flows (streams) in the network, we propose in this paper a limited intermediate buffering that couples with SLE aware prioritized RWA (LIB-PRWA) scheme that combats secondary contention as well. The scheme makes routing decisions such as selection of primary and deflection routes based on current resources states in the candidate paths. A performance comparison of the proposed scheme is carried out and simulation results demonstrate its comparative abilities to effectively reduce losses as well as maintaining both high network resources utilization as well as QoS.

An Efficient and Effective Approach for Flooding Attack Detection in Optical Burst Switching Networks

Optical burst switching (OBS) networks are frequently compromised by attackers who can flood the networks with burst header packets (BHPs), causing a denial of service (DoS) attack, also known as a BHP flooding attack. Nowadays, a set of machine learning (ML) methods have been embedded into OBS core switches to detect these BHP flooding attacks. However, due to the redundant features of BHP data and the limited capability of OBS core switches, the existing technology still requires major improvements to work effectively and efficiently. In this paper, an efficient and effective ML-based security approach is proposed for detecting BHP flooding attacks. The proposed approach consists of a feature selection phase and a classification phase. The feature selection phase uses the information gain (IG) method to select the most important features, enhancing the efficiency of detection. For the classification phase, a decision tree (DT) classifier is used to build the model based on the selected features of BHPs, reducing the overfitting problem and improving the accuracy of detection. A set of experiments are conducted on a public dataset of OBS networks using 10-fold cross-validation and holdout techniques. Experimental results show that the proposed approach achieved the highest possible classification accuracy of 100% by using only three features.

Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network

Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.

Channel Scheduling Based on Orchestrator Live Node-Wavelength Reservation for Optical Burst Switching Networks

Background: Dedicated wavelength utilization and the isolation of control plane from the data plane are the important features in the design of Optical Burst Switching (OBS). The contention in bursts, link congestion and the reservation cause the burst dropping in optical networks. The slotted time and the burst assembly models incorporate the wavelength assignment and the channel reservation schemes to reduce the dropping probability. The reservation of resources prior to burst arrival and the additional delay due to the burst assembly and the offset time are the major issues in the reduction of probability. Besides, the traditional one-to-one packet transmission consumes more time due to a large number of packets handling. Materials and Methods: This paper proposes the novel OBS model that incorporates the three processes such as Open-Flow (OF)-based Orchestrator Live Node (OLN) modeling, fuzzy logic based ranking and the offset time-based reservation (without/with void filling) to overcome the issues in the traditional methods. Initially, the OLN modeling based on OF analysis includes the Flow Information Base (FIB) table for the periodical update of the link information. The fuzzy logic- based ranking of channels followed by OF-OLN predicts the status of the wavelength such as free, used and conversion. Based on the status, the channels are reserved without and with void filling to schedule the bursts effectively. The reservation scheme employs the Offset-Time Burst Assembly algorithm to allow the resource reservation prior to burst arrival. Through these processes, the reuse of wavelength and the reallocation of resources are possible in OBS. Results & Conclusion: The controlling of maximum burst transfer delay by the OTBA efficiently reduces the end-to-end delay for data traffic. The comparative analysis between the proposed OLN-WR with the existing Hybrid Burst Assembly (HBA), Fuzzy-based Adaptive Threshold (FAT) and Fuzzy-based Adaptive Hybrid Burst Assembly (FAHBA) in terms of end-to-end delay and transmitted amount of bursts assures the applicability of OLN-WR in scheduling and communication activities in OBS networks.

A controlled deflection routing and wavelength assignment based scheme in Optical Burst Switched (OBS) networks

Abstract Heterogeneous IoT-enabled networks generally accommodate both jitter tolerant and intolerant traffic. Optical Burst Switched (OBS) backbone networks handle the resultant volumes of such traffic by transmitting it in huge size chunks called bursts. Because of the lack of or limited buffering capabilities within the core network, burst contentions may frequently occur and thus affect overall supportable quality of service (QoS). Burst contention(s) in the core network is generally characterized by frequent burst losses as well as differential delays especially when traffic levels surge. Burst contention can be resolved in the core network by way of partial buffering using fiber delay lines (FDLs), wavelength conversion using wavelength converters (WCs) or deflection routing. In this paper, we assume that burst contention is resolved by way of deflecting contending bursts to other less congested paths even though this may lead to differential delays incurred by bursts as they traverse the network. This will contribute to undesirable jitter that may ultimately compromise overall QoS. Noting that jitter is mostly caused by deflection routing which itself is a result of poor wavelength and routing assigning, the paper proposes a controlled deflection routing (CDR) and wavelength assignment based scheme that allows the deflection of bursts to alternate paths only after controller buffer preset thresholds are surpassed. In this way, bursts (or burst fragments) intended for a common destination are always most likely to be routed on the same or least cost path end-to-end. We describe the scheme as well as compare its performance to other existing approaches. Overall, both analytical and simulation results show that the proposed scheme does lower both congestion (on deflection routes) as well as jitter, thus also improving throughput as well as avoiding congestion on deflection paths.

A Node-Regulated Deflection Routing Framework for Contention Minimization

Optical Burst Switching (OBS) paradigm coupled with Dense Wavelength Division Multiplexing (DWDM) has become a practical candidate solution for the next-generation optical backbone networks. In its practical deployment only the edge nodes are provisioned with buffering capabilities, whereas all interior (core) nodes remain buffer-less. In that way the implementation becomes quite simple as well as cost effective as there will be no need for optical buffers in the interior. However, the buffer-less nature of the interior nodes makes such networks prone to data burst contention occurrences that lead to a degradation in overall network performance as a result of sporadic heavy burst losses. Such drawbacks can be partly countered by appropriately dimensioning available network resources and reactively by way of deflecting excess as well as contending data bursts to available least-cost alternate paths. However, the deflected data bursts (traffic) must not cause network performance degradations in the deflection routes. Because minimizing contention occurrences is key to provisioning a consistent Quality of Service (QoS), we therefore in this paper propose and analyze a framework (scheme) that seeks to intelligently deflect traffic in the core network such that QoS degradations caused by contention occurrences are minimized. This is by way of regulated deflection routing (rDr) in which neural network agents are utilized in reinforcing the deflection route choices at core nodes. The framework primarily relies on both reactive and proactive regulated deflection routing approaches in order to prevent or resolve data burst contentions. Simulation results show that the scheme does effectively improve overall network performance when compared with existing contention resolution approaches. Notably, the scheme minimizes burst losses, end-to-end delays, frequency of contention occurrences, and burst deflections.