A Novel QoS provisioning Scheme for OBS networks

This paper presents Classified Cloning, a novel QoS provisioning mechanism for OBS networks carrying real-time applications (such as video on demand, Voice over IP, online gaming and Grid computing). It provides such applications with a minimum loss rate while minimizing end-to-end delay and jitter. ns-2 has been used as the simulation tool, with new OBS modules having been developed for performance evaluation purposes. Ingress node performance has been investigated, as well as the overall performance of the suggested scheme. The results obtained showed that new scheme has superior performance to classical cloning. In particular, QoS provisioning offers a guaranteed burst loss rate, delay and expected value of jitter, unlike existing proposals for QoS implementation in OBS which use the burst offset time to provide such differentiation. Indeed, classical schemes increase both end-to-end delay and jitter. It is shown that the burst loss rate is reduced by 50% reduced over classical cloning

Fair Bandwidth Allocation in Optical Burst Switching Networks

Optical burst switching (OBS) is a promising switching technology for next-generation Internet backbone networks. One of the design challenges is how to provide fair bandwidth allocation in OBS networks; the schemes proposed for general store-and-forward IP switching networks can not be used because of the non-buffering and un-fully utilized bandwidth characteristics of OBS networks. We propose a rate fairness preemption (RFP) scheme to achieve approximately weighted max-min fair bandwidth allocation in OBS networks. We present an analysis of the burst loss probability in RFP-based OBS networks. The analysis and simulation results show that the RFP scheme provides fair bandwidth allocation in OBS network

Traffic and performance evaluation for optical networks. An Investigation into Modelling and Characterisation of Traffic Flows and Performance Analysis and Engineering for Optical Network Architectures.

The convergence of multiservice heterogeneous networks and ever increasing Internet applications, like peer to peer networking and the increased number of users and services, demand a more efficient bandwidth allocation in optical networks. In this context, new architectures and protocols are needed in conjuction with cost effective quantitative methodologies in order to provide an insight into the performance aspects of the next and future generation Internets. This thesis reports an investigation, based on efficient simulation methodologies, in order to assess existing high performance algorithms and to propose new ones. The analysis of the traffic characteristics of an OC-192 link (9953.28 Mbps) is initially conducted, a requirement due to the discovery of self-similar long-range dependent properties in network traffic, and the suitability of the GE distribution for modelling interarrival times of bursty traffic in short time scales is presented. Consequently, using a heuristic approach, the self-similar properties of the GE/G/¿ are being presented, providing a method to generate self-similar traffic that takes into consideration burstiness in small time scales. A description of the state of the art in optical networking providing a deeper insight into the current technologies, protocols and architectures in the field, which creates the motivation for more research into the promising switching technique of ¿Optical Burst Switching¿ (OBS). An investigation into the performance impact of various burst assembly strategies on an OBS edge node¿s mean buffer length is conducted. Realistic traffic characteristics are considered based on the analysis of the OC-192 backbone traffic traces. In addition the effect of burstiness in the small time scales on mean assembly time and burst size distribution is investigated. A new Dynamic OBS Offset Allocation Protocol is devised and favourable comparisons are carried out between the proposed OBS protocol and the Just Enough Time (JET) protocol, in terms of mean queue length, blocking and throughput. Finally the research focuses on simulation methodologies employed throughout the thesis using the Graphics Processing Unit (GPU) on a commercial NVidia GeForce 8800 GTX, which was initially designed for gaming computers. Parallel generators of Optical Bursts are implemented and simulated in ¿Compute Unified Device Architecture¿ (CUDA) and compared with simulations run on general-purpose CPU proving the GPU to be a cost-effective platform which can significantly speed-up calculations in order to make simulations of more complex and demanding networks easier to develop

A Machine Learning Approach For Enhancing Security And Quality Of Service Of Optical Burst Switching Networks

The Optical Bust Switching (OBS) network has become one of the most promising switching technologies for building the next-generation of internet backbone infrastructure. However, OBS networks still face a number of security and Quality of Service (QoS) challenges, particularly from Burst Header Packet (BHP) flooding attacks. In OBS, a core switch handles requests, reserving one of the unoccupied channels for incoming data bursts (DB) through BHP. An attacker can exploit this fact and send malicious BHP without the corresponding DB. If unresolved, threats such as BHP flooding attacks can result in low bandwidth utilization, limited network performance, high burst loss rate, and eventually, denial of service (DoS). In this dissertation, we focus our investigations on the network security and QoS in the presence of BHP flooding attacks. First, we proposed and developed a new security model that can be embedded into OBS core switch architecture to prevent BHP flooding attacks. The countermeasure security model allows the OBS core switch to classify the ingress nodes based on their behavior and the amount of reserved resources not being utilized. A malicious node causing a BHP flooding attack will be blocked by the developed model until the risk disappears or the malicious node redeems itself. Using our security model, we can effectively and preemptively prevent a BHP flooding attack regardless of the strength of the attacker. In the second part of this dissertation, we investigated the potential use of machine learning (ML) in countering the risk of the BHP flood attack problem. In particular, we proposed and developed a new series of rules, using the decision tree method to prevent the risk of a BHP flooding attack. The proposed classification rule models were evaluated using different metrics to measure the overall performance of this approach. The experiments showed that using rules derived from the decision trees did indeed counter BHP flooding attacks, and enabled the automatic classification of edge nodes at an early stage. In the third part of this dissertation, we performed a comparative study, evaluating a number of ML techniques in classifying edge nodes, to determine the most suitable ML method to prevent this type of attack. The experimental results from a preprocessed dataset related to BHP flooding attacks showed that rule-based classifiers, in particular decision trees (C4.5), Bagging, and RIDOR, consistently derive classifiers that are more predictive, compared to alternate ML algorithms, including AdaBoost, Logistic Regression, Naïve Bayes, SVM-SMO and ANN-MultilayerPerceptron. Moreover, the harmonic mean, recall and precision results of the rule-based and tree classifiers were more competitive than those of the remaining ML algorithms. Lastly, the runtime results in ms showed that decision tree classifiers are not only more predictive, but are also more efficient than other algorithms. Thus, our findings show that decision tree identifier is the most appropriate technique for classifying ingress nodes to combat the BHP flooding attack problem

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Deflection Routing Strategies for Optical Burst Switching Networks: Contemporary Affirmation of the Recent Literature

A promising option to raising busty interchange in system communication could be Optical Burst Switched (OBS) networks among scalable and support routing effective. The routing schemes with disputation resolution got much interest, because the OBS network is buffer less in character. Because the deflection steering can use limited optical buffering or actually no buffering thus the choice or deflection routing techniques can be critical. Within this paper we investigate the affirmation of the current literature on alternate (deflection) routing strategies accessible for OBS networks