Software-defined Optical Networks Research Articles

A Robust Controller Deployment Algorithm under Multiple Constraints for SDON

The deployment of controller for SDON (Software Defined Optical Networks) is an NP-hard multi-objective optimization problem, and it is difficult to find an optimal solution that satisfies all objectives. Therefore, to improve the performance of control plane, the model we proposed is constructed based on three constraints on the survivability, delay, and control redundancy, which has three advantages. first, the survivability is improved by not sharing the risk between the main control channel and the protection control channel, which also improves the utilization of network resources. Second, the accuracy of delay calculation is improved by using a delay optimization model, and a threshold value is set to constrain the delay. Finally, the greedy strategy idea is used to achieve the full network coverage of the control range and improve the convergence speed. Simulation results show that the algorithm improves the fault recovery probability by an average of 15% compared with the algorithm of KSP computed routing. In addition, the algorithm in this paper achieves the same survivability as the C-MPC algorithm while using an average 11.1% reduction in the number of controllers.

Improving the accuracy of QoT estimation with insertion loss distribution evaluation for C + L band transmission systems

The accuracy of quality of transmission (QoT) estimation is crucial for software-defined optical networks (SDONs), where network planning and system management of the centralized controller depend on reliable performance evaluation. The insertion loss (IL) commonly exists along the light path between the transmitter side (Tx) and the receiver side (Rx) in fiber optical transmission systems, and the IL values vary for each connector and pigtail. In the case of C+L band transmission systems that are about to be deployed on a large scale, in the presence of severe stimulated Raman scattering (SRS), IL further influences the launch power profile (LPP) as well as the noise spectrum, and the process in the QoT estimation will be disturbed without available IL values. Therefore, there is a great need for an accurate calculation method of IL distribution. In this paper, we propose a method to estimate IL distribution in fiber optical transmission links to improve the accuracy of QoT estimation based on the SRS effect. The results demonstrate that, compared with other common methods, our proposed method greatly improves the accuracy of QoT estimation for C+L band transmission systems with accurate IL distribution evaluation. The high accuracy, robustness, and ability to adapt dynamic conditions make our proposed method a promising QoT estimation scheme in practical C+L band fiber optical communication systems.

Flexible open network operating system architecture for implementing higher scalability using disaggregated software‐defined optical networking

AbstractThe 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.

CPSR-HQKDN: A Hybrid Trusted Relay Quantum Key Distribution Network Routing Scheme Based on Classification of Packet Security Requirements

To ensure the security of information exchange in software-defined optical networks, quantum key distribution (QKD) based on quantum mechanics is introduced. However, the slow and valuable process of generating quantum key resources contradicts the high-speed data transmission requirements of optical networks. To address this issue, this paper proposes the CPSR-HQKDN scheme, which takes into account factors such as security requests, key demand, key residual, and key update rates for trusted and untrusted links. This approach improves resource utilization and service efficiency by optimizing the processing order of key requests. Moreover, the routing strategy dynamically adjusts based on the network resource environment, thereby increasing the success rate of key requests. Through simulation experiments comparing the performance of the CPSR-HQKDN routing scheme with existing schemes, it is observed that in high-concurrent scenarios, the CPSR-HQKDN routing scheme can improve the success rate of key requests by at least 5%.

Blockchain-enhanced cross-ISP spectrum assignment framework in SDONs: SpectrumChain

Huge potentials of optical networks provide an excellent choice in meeting the exponential growth in bandwidth demand. In particular, Elastic Optical Networks (EONs), with its promising potentials in increased availability, failure resilience, load-balancing, and efficient resource allocations, are good candidates for the future high-speed networks. When EONs are augmented with the emerging Software Defined Networking (SDN) architecture, which decouples the data and control planes, an agile and synergistic combination emerges under a general term of Software Defined Optical Networking (SDON), albeit with operational challenges such optimal and efficient spectrum assignments. In this paper, we present a blockchain-enhanced QoS-concentrated cross-Internet Service Provider (ISP) spectrum assignment framework for SDONs, SpectrumChain (SC), to eliminate centralized mediators while coordinating QoS-based inter-ISP traffic. This study introduces a new blockchain use-case with reduced QoS signaling overhead while performing inter-ISP routing in SDONs. We also experience the proposed framework under Hop-by-Hop Wavelength Switching (HWS), Border-Node-Only Wavelength Switching (BWS), and No Wavelength Switching (NWS) scenarios to analyze its performance. Our extensive simulation results show that the SC framework can effectively handle the QoS-enabled inter-ISP routing in SDON architecture in terms of Flow Setup Time (FST), Messages Processed (MEP), Requests Serviced (RS), and Acceptance Ratio (AR) metrics under HWS, BWS, and NWS scenarios.

A comparative analysis of SDON and traditional optical networks in elastic optical environments

The advent of Software-Defined Optical Networking (SDON) and its integration into Elastic Optical Networks (EON) have significantly advanced the evolution of communications networks. In the context of elastic optical environments, this paper provides a thorough comparison of SDON and conventional optical networks. The goal of the study is to examine the unique traits, benefits, and difficulties of these two network technologies in order to highlight their individual potentials in contemporary communication infrastructures. This paper consists of comparison of SDON, SDN and NFV technologies. The paper concludes by looking at potential directions for future research and development in SDON and EON. The results of this comparative analysis highlight the growing role that SDON is playing in reshaping the telecom industry and present a vision for a more flexible and effective communication infrastructure.

All-fiber optical nonreciprocity based on parity-time-symmetric Fabry-Perot resonators

Nonreciprocal light transmission in an all-fiber platform is critical in modern optical communication systems, which can avoid the packaging and integration process required in current devices based on magneto-optical or nonlinear materials. Here we propose and demonstrate an all-fiber device with remotely tunable isolation ratio and switchable isolation direction by constructing two mutually coupled Fabry-Perot (FP) resonators with identical geometry and balanced gain and loss. By controlling the pumping power, strong optical nonreciprocity is achieved due to gain saturation nonlinearity that is enhanced by the broken parity-time symmetry. Nonreciprocal light transmission with an isolation ratio of 8.58 dB at 1550 nm and an insertion loss of 2.5 dB is demonstrated. The isolation bandwidth is 125 MHz, which is determined by the bandwidths of the two coupled FP resonators. The proposed approach provides an all-fiber solution for a remotely tunable and optically controlled isolator, which may find applications in software-defined optical networks.

A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement Learning

Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally allocate routes and maximize network capacities. By employing deep reinforcement learning (DRL)- and software-defined networking (SDN)-based solutions, the capacity of optical networks can be effectively increased. However, because most DRL-based routing optimization methods have low sample usage and difficulty in coping with sudden network connectivity changes, converging in software-defined OTN scenarios is challenging. Additionally, the generalization ability of these methods is weak. This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address this problem. To effectively explore the environment and improve agent performance, the multiple EMDQN agents select actions based on the highest upper-confidence bounds. Furthermore, the EMDQN agent captures the network's spatial feature information using a message passing neural network (MPNN)-based DRL policy network, which enables the DRL agent to have generalization capability. The experimental results show that the EMDQN algorithm proposed in this paper performs better in terms of convergence. EMDQN effectively improves the throughput rate and link utilization of optical networks and has better generalization capabilities.

Cross-Domain Resource Allocation Scheme with Unified Control Architecture in Software Defined Optical Access Network

With the rapid development of communication and the rise of new network services, the resource provisioning of the optical access network becomes more significant than before, especially under the multiple-domain networking situation with the requirement of cross-domain service support. For the sake of high resource utilization to establish more connections of services, this paper proposes a cross-domain resource allocation (CDRA) scheme in a software-defined optical access network to meet the huge bandwidth supporting the requirement of new network services. To achieve this purpose, the global evaluation strategy with the consideration of the traffic situation in each node in its domain is presented in the CDRA scheme, and its interaction process makes decisions for comprehensive optimal resource allocation by integrating radio resources and optical aggregation resources of the entire access network. Furthermore, to manage the resources among multiple domains and support the interaction process of the CDRA scheme, a cross-domain unified control architecture is firstly upgraded by using software-defined networking technology, which includes the further design of CDRA function modules with the global evaluation strategy. The simulation results verify the feasibility of the upgraded architecture and further show that the proposed CDRA scheme can effectively decrease the blocking probability with a 29.35% improvement, balance the network load, and enhance the utilization of network resources of the network.

GNPy model of the physical layer for open and disaggregated optical networking [Invited]

Networking technologies are fast evolving to support the request for ubiquitous Internet access that is becoming a fundamental need for the modern and inclusive society, with a dramatic speed-up caused by the COVID-19 emergency. Such evolution needs the development of networks into disaggregated and programmable systems according to the software-defined networking (SDN) paradigm. Wavelength-division multiplexed (WDM) optical transmission and networking is expanding as physical layer technology from core and metro networks to 5G x -hauling and inter- and intra-data-center connections requiring the application of the SDN paradigm at the optical layer based on the WDM optical data transport virtualization. We present the fundamental principles of the open-source project Gaussian Noise in Python (GNPy) for the optical transport virtualization in modeling the WDM optical transmission for open and disaggregated networking. GNPy approximates transparent lightpaths as additive white and Gaussian noise channels and can be used as a vendor-agnostic digital twin for open network planning and management. The quality-of-transmission degradation of each network element is independently modeled to allow disaggregated network management. We describe the GNPy models for fiber propagation, optical amplifiers, and reconfigurable add/drop multiplexers together with modeling of coherent transceivers from the back-to-back characterization. We address the use of GNPy as a vendor-agnostic design and planning tool and as physical layer virtualization in software-defined optical networking.

Architectural Design and Life Cycle Management of Network Slicing for Software-Defined Optical Access Networks

Optical access networks (OANs) with passive optical network technology have been globally deployed on a massive scale, serving as the fundamental information infrastructure of teleCommunication networks. Some telecom service providers are re-architecting their networks based on cloud central office with software-defined networking and network functions virtualization capabilities to provide service agility, intelligence, and scalability. In this article, we present a synthesized analysis of the inherent value and practical use cases of access network (AN) slicing in the context of software-defined OANs. A novel hierarchical abstraction model and a workflow of end-to-end network slicing are presented to illustrate the support of multi-service and multi-tenancy applications over a converged OAN platform by enabling automatic life cycle management of AN slicing. Two successful trials utilizing the model and the workflow are also demonstrated.

A survey on QoT prediction using machine learning in optical networks

In optical networks, a connection (e.g., light-path and light-tree) is set up to carry data from its source to destination(s). When the optical signal transmits through the fiber links and optical devices, the quality of transmission (QoT) degrades due to various physical layer impairments (PLIs), including linear and nonlinear impairments. QoT is an important metric that determines the availability of a connection. Therefore, the QoT guarantee is the premise of successful connection establishment in optical networks. QoT prediction before connections establishment can provide guidance for the routing and resources allocation of connections. In order to receive the correct signal at the receiving end, during network planning design margins are introduced to compensate the inaccuracy of the QoT prediction model itself and its inputs. Improving the accuracy of prediction can make better use of network resources and reduce margins. With the help of strong computing power and data acquisition based on software defined optical network (SDON), machine learning (ML) based models are more suitable for QoT prediction than analytical models that are difficult to derive and computationally heavy. This paper provides an overview on the applications of ML technologies in QoT prediction. Firstly, we elaborate the QoT problem in optical networks, including main QoT influence factors, QoT metrics, and QoT prediction strategies. Then, suitable ML algorithms, the generation of sample data, ML frameworks and the construction of QoT prediction model, are briefly introduced. Next, three solutions of QoT prediction using various ML technologies in recent studies and their practical feasibility are reviewed and discussed in detail. Finally, based on the existing researches, we present some future research directions about the improvement of QoT prediction.

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

As a promising centralized control architecture, software-defined network (SDN) has been widely used and developed in the field of optical access network. Though, its centralized control architecture has many advantages, it is also hindered by various security threats. Among all the threats, the attack, Denial-of-Service (DoS) is the most severe attack into the software defined optical network (SDON). In spite of, so many developments in tools and technology, there are few effective schemes to detect denial of service attacks in SDON. In our research work, we proposed a traffic anomaly detection scheme by analyzing and defining the specific security threat non-directional denial of service attack (ND-DoS) faced by the SDON. In this scheme, we first designed the function construction of the controller and the extension of the OpenFlow protocol, and then used the adaptive threshold detection algorithm based on time sliding window (TSW-ATD) and the repeated flow detection algorithm (RFD) to complete the first detection and re-detection detection of abnormal traffic, and finally designed a general formulaic measurement method. The proposed scheme is verified by simulation experiments. The experimental results show that compared with the existing related solutions, the forwarding success rate of this scheme is increased by about 29.4%, the data processing rate in the unit window is increased by about 39.3%, and the CPU occupancy rate is reduced by about 17.5%. Therefore, this scheme can effectively deal with DoS attacks in SDON with a higher detection rate and lower resource overhead.

Autonomous Raman amplifiers in multi-band software-defined optical transport networks

A controller architecture for managing multi-band Raman amplification within software-defined optical transport networks is presented. In the perspective of optical fiber network softwarization and disaggregation, this work provides an operative description of a system capable of autonomously handling Raman amplification on a single fiber span, achieving the working point requested by the control plane in terms of mean gain and tilt. The developed architecture is composed of two software modules: the Raman design unit (RDU) and the Raman controller unit (RCU). First, the RDU defines the nominal optimal working point designing the power level configuration of the Raman card pumps according to the required amplification constraints without considering any channel spectral load. Then, due to the telemetry feedback, the RCU performs a tracking operation of the mean gain towards the target one, linearizing the problem at the nominal optimal working point. The fiber physical parameters needed by the controller for correct operation of the system are extracted through a conceived probing procedure. The proposal shows a high degree of adaptability of the defined Raman amplifier to the particular in-field scenario in which it is deployed, also counteracting spectral load modifications. The behavior of the described system is validated by means of an experimental session, confirming the effectiveness of the controller architecture design and the high accuracy achieved in terms of performance.

Fault Localization based on Knowledge Graph in Software-Defined Optical Networks

In the era of the fifth-generation fixed network (F5G), optical networks must be developed to support large bandwidth, low latency, high reliability, and intelligent management. Studies have shown that software-defined optical networks (SDON) and artificial intelligence can help improve the performance and management capabilities of optical networks. Inside a large-scale optical network, many types of alarms are reported that indicate network anomalies. Relationships between the alarms are complicated, making it difficult to accurately locate the source of the fault(s). In this work, we propose a knowledge-guided fault localization method, using network alarm knowledge to analyze network abnormalities. Our method introduces knowledge graphs (KGs) into the alarm analysis process. We also propose a reasoning model based on graph neural network (GNN), to perform relational reasoning on alarm KGs and locate the network faults. We develop an ONOS-based SDON platform for experimental verification, which includes a set of processes for the construction and application of alarm KGs. The experimental results show the proposed method has high accuracy and provide motivation for the industry-scale use of KGs for alarm analysis and fault localization.

On the Bilevel Optimization to Design Control Plane for SDONs in Consideration of Planned Physical-Layer Attacks

In the network planning of software-defined optical networks (SDONs), the control plane design is of great importance because it directly affects the performance and reliability of network control and management (NC&M). In this article, we consider the planned physical-layer attacks from a rational attacker, which can analyze the control plane of an SDON and target its attacks to the most vulnerable part. To address such attacks, we model the control plane design as a bilevel optimization, where the upper-level optimization is for the network planner to design the control plane whose vulnerability to planned attacks is minimized, while the lower-level optimization is for the attacker to plan its attacks such that the control plane can be disturbed as severely as possible. We first develop two approaches to solve the bilevel model exactly. Specifically, we first leverage the cutting plane method to solve it directly, and then transform it into a single-level mixed integer linear programming (MILP) model with the Bellman method for problem solving. To improve the time efficiency for large-scale problems, we also propose a polynomial-time approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations with various physical topologies verify the effectiveness of our proposals.

Deadline-Aware Multicast Transfers in Software-Defined Optical Wide-Area Networks

The increasing amount of data replication across datacenters introduces a need for efficient bulk data transfer protocols which provide certain guarantees, most notably timely transfer completion. We present DaRTree which leverages emerging optical reconfiguration technologies, to jointly optimize topology and multicast transfers in software-defined optical Wide-Area Networks (WANs), and thereby maximize throughput and acceptance ratio of transfer requests subject to transfer deadlines. DaRTree is based on a novel integer linear program relaxation and deterministic rounding scheme. To this end, DaRTree uses Steiner trees for forwarding and adaptive routing based on the current network load. DaRTree provides transfer completion guarantees without the need for rescheduling or preemption. Our evaluations show that DaRTree increases the network throughput and the number of accepted requests by up to 1.7×, especially for larger WANs. Moreover, DaRTree even outperforms state-of-the-art solutions when the traffic demands are only unicast transfers or when the WAN topology cannot be reconfigured. While DaRTree determines the rate and route to serve a request at the time of (online) admission control, we show that the acceptance ratio and throughput can be improved by up to 1.3× even further when DaRTree updates the rate and route of admitted transfers also at runtime.

Naive echo-state-network based services awareness algorithm of software defined optical networks

As internet services newly emerge with diversity and complexity, great challenges and demands are presented to the OpenFlow controlled software defined optical networks (SDON) to achieve better match between services and SDON. With this aim, this paper proposes a naive Echo-State-Network (Naive-ESN) based services awareness algorithm of the software defined optical network, where the naive ESN model adopts the ring topology structure and generates the probability output result to determine the QoS policy of SDON. Moreover, the Naive-ESN engine is also designed in controller node of SDON to perform services awareness by obtaining service traffic features from data plan, together with some necessary extension of the OpenFlow protocol. Test results show that the proposed approach is able to improved services-oriented supporting ability of SDON.

Inter-domain resource optimization method of software-defined multi-domain optical network

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.

An extended software defined optical networks slicing architecture

Optical Networks are composed of multiple devices, from multiple vendors. Normally these networks have a huge transmission capacity. The Slicing of Optical Networks is not a new concept, but continues to be very important, since the capacity of Optical Networks keeps evolving. Most of the time the slicing is manually configured by system operators. Besides being laborious and error-prone, such configuration limits the clients’ ability to customize and configure the network according to their own needs. One way out of this problem is to separate from these devices the control of and the data from the planes. The Software Defined Networks (SDNs) propose the separation of planes while also offering network operators the flexibility to create and manage applications, enabling them to reduce the network costs by globally optimizing the network’s resources, reducing the staff needed to configure it, and contributing for less violation of the service level agreement (SLA). SDN can also help operators to maximize their profit by generating more revenue through mechanisms that increase availability and failure resiliency, maximize throughput, allow for fast dynamic reprovisioning and enable network virtualization. The goal of this paper is to propose a Software Defined Optical Networks Slicing Architecture (SONA) extension (eSONA). that permits defining components such as topology manager, inventory manager, slice manager and path provisioner, and thus enable Optical Networks slicing. It has proved to be capable of managing different slices and provisioning a path on a given slice over the same physical optical network. It has showed an excellent performance, taking little time to provision paths, even with a large number of nodes, which are crucial for optical environments.