Quantum Key Distribution‐Adaptation‐Based Security Enhancement of Software‐Defined Optical Network via Dynamic Quantum Resource Management

The demand for secure communication in the age of quantum technologies has driven progress in quantum key distribution (QKD) techniques for optical networks. This research addresses the issues of high blocking probabilities (BPs) and the proper utilization of quantum resources in varying network loads by introducing a novel heuristic approach, termed dynamic security-aware quantum resource allocation (D-SQRA), designed for dynamic resource allocation in QKD-enabled optical networks. We propose two D-SQRA algorithms to employ an adaptive resource assignment (RA) strategy that concurrently addresses routing, wavelength, and time-slot selection while dynamically modifying security levels according to the real-time network load and resource availability. We evaluate the proposed D-SQRA performance against two conventional methods, namely, fixed security quantum resource allocation (F-SQRA) and baseline quantum resource allocation (B-QRA). We discuss the results for NSFNET and UBN24 topologies for network security performance metrics such as network security performance (NSP), BP, quantum key utilization (QKU), and time-slot utilization. The results show that the proposed D-SQRA algorithms provide significant improvement with respect to conventional techniques in addressing proper resource utilization and management by reducing BPs of the new incoming connection requests.

The enhanced capacity of optical networks is a significant advantage within the global telecommunications industry. Optical networks provides transmission of information over large distances with reduced latency. However, the growing intricacy of network topologies poses a significant challenge to network adaptability, network resilience, device compatibility, and service quality in the contemporary era of technology and 5G networks. In light of these challenges, recent studies leverages on disaggregation in the context of Software Defined Network (SDN) and network service orchestrators as a viable remedy. Disaggregated optical systems offer SDON (Software‐Defined Optical Networking) enhanced control options and third‐party dynamism streamlining upgrades and diminishing single vendor dependency. Although, the advancement of disaggregation improves network flexibility and vendor neutrality of Software Defined Optical Networking (SDON), this improvement comes at the cost of reduced scalability and network controllability performance. The current research paper posits two potential resolutions to the aforementioned challenge. The authors present recommendations and an enhanced architecture that leverages Open Network Operating System (ONOS) containers and Kubernetes orchestration to improve scalability inside the Software‐Defined Optical Networking (SDON) architecture. The suggested architectural design has underlining novel flow charts and algorithms that enhances scalability performance by 33% while also preserving flexibility and controllability in comparison to pre‐existing SDON architectures. This architecture also makes use of the Mininet‐Optical physical‐layer architecture to simulate a real‐time scenario, as well as yang models from the Open Disaggregated Transport Network (ODTN) working group, the pioneers of SDONs. A detailed analysis of the rules and procedural processes involved in the implementation of the proposed architecture. In order to demonstrate the practical application of this architectural framework to a real‐world Software‐Defined Optical Network (SDON) system, the pre‐existing SDON ONOS architecture within the Optical Transport Domain Networking (OTDN) working group was adjusted and refined. This adaptation aimed to illustrate the use of ONOS in conjunction with established optical network systems, highlighting the advantages it offers.

There is a need of the networks which can accommodate the requirements of high data rate with low blocking probability. Optical networks are used most commonly as the backbone. They support high data rates, but need to be provisioned with low blocking probability. Due to the use of traditional Wavelength Division Multiplexing (WDM), optical network are not that much bandwidth efficient as they could be. Elastic Optical Network (EON) configuration can be used to resolve this problem if Routing and Spectrum Assignment (RSA) problem is done efficiently. We are addressing the routing problem through an artificial intelligence-based algorithm. In earlier works, several optimization methods have been used for path-finding in the networks. Some of these studies has problem in terms of higher time complexity and incompleteness, due to which there is no guaranteed optimal solution. In this paper, we are using A* algorithm for routing, which is known for its best-estimated cost of a path from a source to a destination. To the best of our knowledge, this is the first attempt to study the use of A* algorithm in routing and spectrum allocation in the EON. We have modified this algorithm in a way such that, while reconstructing the path, we simultaneously check how many contiguous and continuous slots are available. So, while allocating the spectrum if the required slots are not available at that instant, we can either block or re-route the path. It saves time and decreases the blocking probability. Modified A* algorithm is expected to solve the problem only if initial seed values of the cost of the paths are not over estimated beyond a threshold. For the spectrum assignment, after evaluation of available slots while reconstructing the path, whichever slots are contiguously and continuously available, are allocated using different modulation formats. We have studied the algorithm for different network topologies, to check its validity and compared the results with the previously used algorithms.

Static and dynamic routing and wavelength assignment algorithms for future transport networks

Optical network security is attracting increasing research interest. Currently, software defined optical network (SDON) has been proposed to increase network intelligence, which is gradually moving towards industrialization. However, new threats are emerging, e.g., control messages transferred via the control channels are vulnerable to eavesdropping and interception. Data encryption is an effective way to enhance the security of connection as well as control messages transferred in SDON. Noticeably, quantum key distribution (QKD) is being considered as a secure mechanism to provision keys for confidential data encryption, which is a potential technique to protect communications from security attacks in SDONs. How to utilize QKD to enhance the SDON security is emerging as a new issue. This paper proposes a novel QKD-enabled SDON architecture, and discusses some open issues and challenges such as resource allocation, security level, trusted repeater node placement and resiliency under such an architecture. Moreover, this paper also proposes an algorithmic solution to resource allocation issue and describes some illustrative simulation results.

In this paper, we propose a new ant-based algorithm for the dynamic routing and wavelength assignment (RWA) problem in optical WDM networks under the wavelength continuity constraint. Unlike conventional approaches, which usually require centralized global network information, our new RWA algorithm constructs the routing solution in a distributed manner by means of cooperative ants. To facilitate the ants’ foraging task, we adopt in our algorithm a probabilistic routing table structure for route selection. The new algorithm is highly adaptive in that it always keeps a suitable number of ants in the network to cooperatively explore the network states and continuously update the routing tables, so that the route for a connection request can be determined promptly by the current states of routing tables with only a small setup delay. Some new schemes for path scoring and path searching are also proposed to enhance the performance of our ant-based algorithm. Extensive simulation results upon three typical network topologies indicate that the proposed algorithm has a very good adaptability to traffic variations and it outperforms both the fixed routing algorithm and the promising fixed–alternate routing algorithm in terms of blocking probability. The ability to guarantee both a low blocking probability and a small setup delay makes the new ant-based routing algorithm very attractive for both the optical circuit switching networks and future optical burst switching networks

Pruned optical banyan networks on vertical stacking scheme for faster connection establishment

A traffic anomaly detection scheme for non-directional denial of service attacks in software-defined optical network

Quantum key distribution (QKD) has potential of providing long-term security as it is based on the fundamental principles of quantum mechanics. Security of information transmitted through optical networks has attracted attention recently due to its vulnerability to attacks post quantum computers. Hence, the integration of QKD into optical networks can significantly improve the security of existing and future optical networks. In QKD-secured optical networks, the quantum channel and the classical channel are integrated into a single optical fiber to reduce the deployment cost of the network infrastructure. However, due to limited network resources in a single optical fiber, the efficient provisioning of network resources (wavelengths and time slots) and blocking are two major issues in such networks. In this work, a resource assignment strategy with scheduling, namely, resource assignment with scheduling (RA-WS) has been proposed for utilization of network resources efficiently. In the RA-WS strategy, the QKD lightpath requests (QLPRs) have been served according to the proposed scheduling criterion. Moreover, the proposed scheduling criterion for routing, wavelength and time-slot assignment (RWTA) can reduce the effect of QLPRs blocking because of the limited network resources. The performance of the proposed RA-WS strategy has been analyzed in terms of blocking probability (BP), success probability of update secret key ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SP_{USK}$</tex> ), and time-slot utilization ratio (TSUR) with different reserved wavelengths for quantum signal channel (QSCh), i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W_{Q}$</tex> and different time slots ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n_{tr}$</tex> ). Simulations performed on NSFNET network topology indicate that the RA-WS strategy performs better than the baseline strategy, i.e., resource assignment without scheduling (RA-WOS) in terms of all the considered metrics.

In this article, the network performance of equilibrium allocation of quantum key resources was investigated in quantum key distribution (QKD)-enabled optical data center networks. To effectively use quantum key resources, we propose three novel efficient load balancing routing, wavelength, and time-slot assignment (LB-RWTA) approaches, including LB-RWTA with flexible security level (LB-RWTA-FSL), LB-RWTA with specific security level (LB-RWTA-SSL), and LB-RWTA without security level (LB-RWTA-NSL). Particularly, the proposed LB-RWTA-FSL approach is uniquely featured by adaptive security level classification (ASLC) and load balancing (LB), aiming to demarcate the security level (SL) and reduce the overall network congestion. We introduce an existing routing, wavelength, and time-slot assignment (E-RWTA) approach without SL, called E-RWTA, for comparison. Simulation results show the effectiveness of our proposed approaches in terms of much higher quantum key resource efficiency and thus much higher network security performance than the state-of-the-art quantum key resource allocation approaches compared with the E-RWTA approach in QKD-enabled optical data center networks.

By integrating communications in different domains, integrated radio and optical networks can serve a wider range of applications and services. Integrated radio and optical network scenarios will involve more weak-computation-ability network nodes, such as small-cell base stations. To pursue efficient integrated radio and optical networks, more efficient ways to conduct transmission under the demand of edge and cloud collaboration are required. The lack of forward-looking resource allocation may easily lead to a waste of network resources without an expected return. Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edge-cloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. We maximize the utilization of limited resources to improve the awareness of network status. We present three evaluation indicators and build a network architecture to evaluate our resource allocation scheme. Through edge-cloud collaboration, our proposal can improve traffic prediction accuracy by 9.5% compared with single-point traffic prediction, and resource utilization is also improved by edge-cloud collaborative traffic prediction.

We propose a cognitive algorithm based on Fuzzy C-Means (FCM) technique for the learning and decision-making functionalities of software-defined optical networks (SDONs). SDON is a new optical network paradigm where the control plane is decoupled from the data plane, thus providing a degree of software programmability to the network. Our proposal is to add the FCM algorithm to the SDON control plane in order to achieve a better network performance, when compared with a non-cognitive control plane. In this context, we illustrate the use of the FCM algorithm for determining, in real time and autonomously, the modulation format of high-speed flexible rate transponders in accordance with the quality of transmission of optical channels. The performance of this FCM algorithm is evaluated via computational simulations for a long-haul network and compared to the case-based reasoning (CBR) algorithm, which is commonly used in optical cognitive networks. We demonstrate that FCM outperforms CBR in both fastness and error avoidance, achieving 100 % of successful classifications, being two orders of magnitude faster. Additionally, we propose a definition of cognitive optical networking and an architecture for the SDON control plane including the FCM engine.

The unprecedented growth in Internet traffic has driven the innovations in provisioning of optical resources as per the need of bandwidth demands such that the resource utilization and spectrum efficiency could be maximized. With the advent of the next generation flexible optical transponders and switches, the flexible-grid-based elastic optical network (EON) is foreseen as an alternative to the widely deployed fixed-grid-based wavelength division multiplexing networks. At the same time, the flexible resource provisioning also raises new challenges for EONs. One such challenge is the spectrum fragmentation. As network traffic varies over time, spectrum gets fragmented due to the setting up and tearing down of non-uniform bandwidth requests over aligned (i.e., continuous) and adjacent (i.e., contiguous) spectrum slices, which leads to a non-optimal spectrum allocation, and generally results in higher blocking probability and lower spectrum utilization in EONs. To address this issue, the allocation and reallocation of optical resources are required to be modeled accurately, and managed efficiently and intelligently. The modeling of routing and spectrum allocation in EONs with the spectrum contiguity and spectrum continuity constraints is well-investigated, but existing models do not consider the fragmentation issue resulted by these constraints and non-uniform bandwidth demands. This thesis addresses this issue and considers both the constraints to computing exact blocking probabilities in EONs with and without spectrum conversion, and with spectrum reallocation (known as defragmentation) for the first time using the Markovian approach. As the exact network models are not scalable with respect to the network size and capacity, this thesis proposes load-independent and load-dependent approximate models to compute approximate blocking probabilities in EONs. Results show that the connection blocking due to fragmentation can be reduced by using a spectrum conversion or a defragmentation approach, but it can not be eliminated in a mesh network topology. This thesis also deals with the important network resource provisioning task in EONs. To this end, it first presents algorithmic solutions to efficiently allocate and reallocate spectrum resources using the fragmentation factor along spectral, time, and spatial dimensions. Furthermore, this thesis highlights the role of machine learning techniques in alleviating issues in static provisioning of optical resources, and presents two use-cases: handling time-varying traffic in optical data center networks, and reducing energy consumption and allocating spectrum proportionately to traffic classes in fiber-wireless networks.

In practical application scenarios, the division of different operators or the same operator in different regions constitutes a natural network region, forming a multi-domain optical network. At the beginning, this paper introduces the hierarchical architecture of software-defined optical network (SDON) and the functional model of control plane. Then, it describes a control system of software-defined multi-domain optical network based on hierarchical control mode and briefly analyzes their basic functional model. In the end, around the problems of resource allocation between scenarios in multi-domain optical network, this paper respectively analyzes the optimization of routing resources in inter-domain and the mechanism of spectrum defragmentation. At the same time, it separately simulates the resource optimization algorithms of single control architecture and hierarchical control architecture to verify the superiority of hierarchical control system.

In this article, two static routing algorithms have been proposed and compared to some of the existing algorithms on the basis of blocking probability. The two proposed static routing and wavelength assignment algorithms reduce the blocking probability to maximize the utilization of the network. All of these algorithms are analyzed and compared with four wavelength assignment schemes, which are first-fit, random, most used, and least used. It is shown that our proposed static algorithms give the best performance for first-fit wavelength assignment and most used wavelength assignment strategies with reduced complexity. For least used wavelength assignment and random wavelength assignment, 1 fixed and 2 alternate routing algorithm gives the lowest blocking probability.