Software Defined Networking Paradigm Research Articles

An Effective Approach for Controller Placement in Software-Defined Internet-of-Things (SD-IoT).

The Software-Defined Networking (SDN) paradigm has transferred network intelligence from network devices to a centralized controller. Controllers are distributed in a network to eliminate a single point of failure (SPOF) and improve reliability and balance load. In Software-Defined Internet of Things (SD-IoT), sensors exchange data with a controller on a regular basis. If the controllers are not appropriately located in SD-IoT, the E2E latency between the switches, to which the sensors are connected, and the controller increases. However, examining the placement of controllers in relation to the whole network is not an efficient technique since applying the objective function to the entire network is a difficult operation. As a result, segmenting the network into clusters improves the efficiency with which switches are assigned to the controller. As a result, in this research, we offer an effective clustering strategy for controller placement in SDN that leverages the Analytical Network Process (ANP), a multi-criteria decision-making (MCDM) scheme. The simulation results demonstrated on real Internet topologies suggest that our proposed method outperforms the standard k-means approach in terms of E2E delay, controller-to-controller (C2C) delay, the fair allocation of switches in the network, and the communication overhead.

Reliable Multipath Flow for Link Failure Recovery in 5G Networks Using SDN Paradigm

In modern networks and cloud evolution, as new nodes to internetwork growing rapidly and use of gaming and video streaming over the network require high availability with very small latency rate. 5G networks provides muchfaster services than 4G but link failure occurrence can affect the quality of service. In 5G networks environmentalfactors also affect the efficiency of wireless signals. To overcome such type of issues, base stations are placed indistributed manner around urban areaswhen decision is required for the placement. However, in some scenarioswe can have few similarities, as in general, highways are same. In existed systems signals distribution is performedhomogenously, so it will be generating issues like fractal and environmental. It will be cause of great economicloss. However, being an emerging network paradigm, Software defined Network (SDN) is easy to manage due tological separation of control plane and data plane. SDN supports numerous advantages, one of them is link robustness to avoid service unavailability. To cope with network link failures there are many mechanisms existedlike proactive and reactive mechanisms, but these mechanisms calculate multiple paths and stored in flow tableswithout considering reliability of link. Therefore, it will be cause of high latency rate due to calculation of manymultiple paths and increased traffic overhead too. To overcome these issues, we proposed a new approach in whichmultipath numbers depend on reliability of primary path. Number of alternative paths decreased as reliability oflink increased it leads to less time required to calculate alternative path. In addition, traffic overhead decreases ascompared to existing approaches. Secondly, we also integrate the shortest distance factor with reliability factor,and we get better results than existing approaches. Our proposed system will be helpful in increasing the availability of services in 5G network due to low latency rate and small traffic overhead required in link failure recovery

A Novel Adaptive East–West Interface for a Heterogeneous and Distributed SDN Network

In the years since its initiation, the software-defined network paradigm has evolved into a distinguished networking technology by causing a revolution in separating the control logic from physical devices and centralizing software-based controllers. Despite its indisputable benefits compared with the traditional network, the SDN raises the challenge of scalability with its physically centralized control. The only potential solution is to transform it into physically distributed SDN control. However, this solution requires the interoperability between SDN controllers, and the consistency of network state being distributed across these controllers. Although some east–west interfaces that help SDN controllers exchange network information have been released, they reveal several drawbacks. First, they cannot support a heterogeneous SDN system where SDN controllers are developed by different providers. Secondly, their consistency solution is simple in disregarding the tradeoff between the consistency level and the performance of SDN networks. This paper proposes an east–west interface, called SINA, to provide the interoperability of a heterogeneous and distributed SDN network. In addition, a novel reinforcement-learning-based consistency algorithm is introduced for an adaptive, quorum-based replication mechanism. The experimental results showed that SINA successfully connects heterogeneous and distributed SDN domains and balances the consistency and network performance.

BCSDN-IoT: Towards an IoT security architecture based on SDN and Blockchain

The Internet of Things (IoT) aims to create a digital world where any information system can expose, discover, understand and consume data and services for analysis, diagnosis, decision support and task automation in various domains such as healthcare, transportation, energy, industry, agriculture, etc. Faced with this diversity of applications and rapid evolution, infrastructures must be able to achieve high levels of security and confidentiality while being open, sustainable, and agile to adapt to the multiple requirements of applications. To meet these needs, new paradigms are emerging. These include the Software Defined Networks (SDN) paradigm, which offers the ability to dynamically program different applications and devices to provide end-to-end service chains. In parallel, the Blockchain paradigm is increasingly used in the Internet of Things, making distributed transactions between connected objects such as financial transactions or "smart contracts" possible. Although the combination of these two paradigms (Blockchain/SDN) is a major issue for the success of the Internet of Things, paving the way for new business models and management/control of communication networks, there is not yet a specified/formalized architecture allowing the use of the "Blockchain" in SDN. In this research, a new architecture for a system combining blockchain and SDN for IoT security is proposed

Mobility-Aware Controller Orchestration in Multi-Tier Service-Oriented Architecture for IoT

This work addresses the problem of controller orchestration in Internet of Things (IoT)-based Service-Oriented Architecture. It focuses on the fundamental issues of heterogeneity and dynamism of hybrid edge-cloud networks involving both static and mobile IoT devices for provisioning IoT-based services. Due to the limited capacity of IoT devices, IoT-cloud services require the support of edge networks, termed as ‘Service Edge,’ for meeting their ultra-low latency requirements. In such complex and dynamic networks, software-defined networking (SDN) paradigm is utilized for efficient resource utilization and service-oriented network management. A significant problem in SDN is the controller placement problem. Although researchers proposed several controller placement schemes for SDN, the problem of multi-tier controller placement in hybrid cloud-edge networks has not yet been addressed. In this work, this problem is formulated mathematically and shown to be a variant of the well-known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two-level capacitated facility location problem</i> , which is NP-hard. Thereafter, COMET, a scheme based on coalitional game and social choice theory, is proposed to solve the problem in polynomial time. COMET is designed while considering varying loads on the switches due to the presence of mobile IoT devices. Simulations depict that COMET reduces network delay by 49.02% with a significant increase in resiliency compared to the state-of-the-art.

Seamless Handover Scheme for MEC/SDN-Based Vehicular Networks

With the recent advances in the fifth-generation cellular system (5G), enabling vehicular communications has become a demand. The vehicular ad hoc network (VANET) is a promising paradigm that enables the communication and interaction between vehicles and other surrounding devices, e.g., vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications. However, enabling such networks faces many challenges due to the mobility of vehicles. One of these challenges is the design of handover schemes that manage the communications at the intersection of coverage regions. To this end, this work considers developing a novel seamless and efficient handover scheme for V2X-based networks. The developed scheme manages the handover process while vehicles move between two neighboring roadside units (RSU). The developed mechanism is introduced for multilane bidirectional roads. The developed scheme is implemented by multiple-access edge computing (MEC) units connected to the RSUs to improve the implementation time and make the handover process faster. The considered MEC platform deploys an MEC controller that implements a control scheme of the software-defined networking (SDN) controller that manages the network. The SDN paradigm is introduced to make the handover process seamless; however, implementing such a controlling scheme by the introduction of an MEC controller achieves the process faster than going through the core network. The developed handover scheme was evaluated over the reliable platform of NS-3, and the results validated the developed scheme. The results obtained are presented and discussed.

Optimized management of ultra-wideband photonics switching systems assisted by machine learning.

Recent years have seen an unprecedented growth of data traffic driven by a continuous increase of connected devices and new applications. This trend will tend to saturate transparent optical networks that are the backbone of the whole telecommunication infrastructure. To improve the capacity of already deployed network infrastructures and maximize operators CAPEX returns, band-division multiplexing (BDM) has emerged as a promising solution to expand the fiber bandwidth beyond the existing C-band. Along with this, the demand for flexible and dynamically reconfigurable functionalities in each network layer is increasing. In this regard, optical networking is fast evolving towards the applications of the software-defined networking (SDN) paradigm down to the physical layer. The implementation of optical SDN requires the full abstraction and virtualization of each network element in order to enable complete control by a centralized network controller. To pursue this objective, photonics transmission components and their transmission functionalities must be abstracted to allow the definition of the control states and a real-time quality-of-transmission (QoT) evaluation of transparent lightpaths (LP). In this work, we propose an SDN based model of a photonic switching fabric that allows determining the control state and evaluating QoT degradation. Our investigations present a wideband optical switch design based on photonic integrated circuits (PICs), where QoT degradation is abstracted using a structure-agnostic approach based on machine learning (ML). The ML engine training and testing datasets are generated synthetically by software simulation of the photonic switch architecture. Results show the potential of the proposed technique to predict QoT impairments with high accuracy, and we envision its application in a real-time control plane.

A Framework for Dynamic Configuration of TLS Connections Based on Standards

The Transport Layer Security (TLS) protocol is widely used for protecting end-to-end communications between network peers (applications or nodes). However, the administrators usually have to configure parameters (e.g., cryptography algorithms or authentication credentials) to establish TLS connections manually. However, this way of managing security connections becomes infeasible when the number of network peers is high. This paper proposes a TLS management framework that configures and manages TLS connections in a dynamic and autonomous manner. The solution is based on well-known standardized protocols and models that allow providing the necessary configuration parameters to establish a TLS connection between two network nodes. Nowadays, this is required in several application scenarios such as virtual private networks, virtualized network functions, or service function chains. Our framework is based on standard elements of the Software Defined Networking paradigm, widely adopted to provide flexibility to network management, such as for the scenarios aforementioned. The proposed framework has been implemented in a proof of concept to validate the suitability of the proposed solution to manage the dynamic configuration of TLS connections. The experimental results confirm that the implementation of this framework enables an operable and flexible procedure to manage TLS connections between network nodes in different scenarios.

Performance evaluation of data-driven techniques for the softwarized and agnostic management of an N×N photonic switch

The emerging Software Defined Networking (SDN) paradigm paves the way for flexible and automatized management at each layer. The SDN-enabled optical network requires each network element’s software abstraction to enable complete control by the centralized network controller. Nowadays, silicon photonics due to its low energy consumption, low latency, and small footprint is a promising technology for implementing photonic switching topologies, enabling transparent lightpath routing in re-configurable add-drop multiplexers. To this aim, a model for the complete management of photonic switching systems’ control states is fundamental for network control. Typically, photonics-based switches are structured by exploiting the modern technology of Photonic Integrated Circuit (PIC) that enables complex elementary cell structures to be driven individually. Thus PIC switches’ control states are combinations of a large set of elementary controls, and their definition is a challenging task. In this scenario, we propose the use of several data-driven techniques based on Machine Learning (ML) to model the control states of a PIC N×N photonic switch in a completely blind manner. The proposed ML-based techniques are trained and tested in a completely topological and technological agnostic way, and we envision their application in a real-time control plane. The proposed techniques’ scalability and accuracy are validated by considering three different switching topologies: the Honey-Comb Rearrangeable Optical Switch (HCROS), Spanke-Beneš, and the Beneš network. Excellent results in terms of predicting the control states are achieved for all of the considered topologies.

Mobile Control Plane Design for Quantum Satellite Backbones

The interconnection of quantum computers through the so-called Quantum Internet is a very promising approach. The most critical issues concern the physical layer, considering that the creation of entanglement over long distances is still problematic. Given the difficulty that usually arises from fiber optics due to exponential losses, the introduction of intermediate quantum repeaters (QRs) allows mitigating the problem. A quantum satellite network based on QRs on satellites deployed over low Earth orbit could make it possible to overcome the constraints of terrestrial optical networks. Hence, the recent technological developments in terms of quantum satellite communications motivated our investigation on an ad hoc quantum satellite backbone design based on the software defined networking paradigm with a control plane directly integrated into the constellation itself. Our aim is to outline some guidelines by comparing several options. Specifically, the focus is to analyze different architectural solutions making some considerations on their feasibility, possible benefits, and costs. Finally, we performed some simulations on the architectures we considered the most promising, concluding that the integration of the control plane in the constellation itself is the most appropriate solution.

Reduction of the Delays Within an Intrusion Detection System (IDS) Based on Software Defined Networking (SDN)

Software Defined Networking (SDN) is a very useful tool not only to manage networks but also to increase network security, in particular by implementing Intrusion Detection Systems (IDS) directly into the SDN architecture. The implementation of IDS within the SDN paradigm can simplify the implementation, speed up incident responses, and, in general, allow to promptly react to cyber attacks through proper countermeasures. Nevertheless, embedding IDS within SDN also introduces delays that cannot be tolerated in specific network environments, like industrial control systems. This paper focuses on the implementation of an IDS based on Machine Learning (ML) algorithms into an SDN architecture and proposes a very practical approach to reduce the delay by using the sequential implementation of prototypes of increasing software and hardware complexity so allowing quick tests to highlight the main problems, solve them and pass to the next operative step. A fully validated performance evaluation is then shown by exploiting all the presented solutions and by using further improved hardware features. The overall performance is very good and compliant with most, even if not yet all, industrial control systems constraints. Results show how the proposed solutions provide a significant improvement of the latency so opening the door to a real implementation in the field.

Survey on PMIPv6-based Mobility Management Architectures for Software-Defined Networking

Software-Defined Networking (SDN) has changed the network landscape. Meanwhile, IP-based mobility management still evolves, and SDN affects it dramatically. Integrating Proxy Mobile IPv6 (PMIPv6) – a network-based mobility management protocol – with the SDN paradigm has created several promising approaches. This paper will present an extensive survey on the joint research area of PMIPv6 and SDN mobility management by detailing the available SDN-integrated network-based techniques and architectures that intend to accelerate handover and mitigate service disruption of mobility events in softwareized telecommunication networks. The article also provides an overview of where PMIPv6 can be used and how SDN may help reach those ways.

A Comparative and Analytical Study for Choosing the Best Suited SDN Network Operating System for Cloud Data Center

The growing deployment of Software Defined Network (SDN) paradigm in the academic and commercial sectors resulted in many different Network Operating Systems (NOS). As a result, adopting the right NOS requires an analytical study of the available alternatives according to the target use case. This study aims to determine the best NOS according to the requirements of Cloud Data Center (CDC). This paper evaluates the specifications of the most common open-source NOSs. The studied features have been classified into two groups, i.e., non-functional features such as availability, scalability, ease of use, maturity, security and interoperability, and functional features, such as virtualization, fault verification and troubleshooting, packet forwarding techniques and traffic protection solutions. A Decision support system, Analytical Hierarchy Process (AHP) has been applied for assessing specifications of the inspected NOSs, namely, ONOS, Opendaylight (ODL), Floodlight, Ryu, POX and Tungsten. Our investigation revealed that ODL is the most suitable NOS for CDC compared to the rest studied NOSs. However, ODL and ONOS have almost similar scores compared to the rest NOSs.

Metropolitan Quantum-Drone Networking and Computing: A Software-Defined Perspective

Swarms of drones are utilized in a wide range of applications, considering that they can be deployed on-demand and are economically affordable. Furthermore, they can also have a significant role in the creation of future Quantum Networks (QNs). As a matter of fact, the use of drones allows deploying a non terrestrial Quantum Metropolitan Area Network (QMAN), overcoming Optical Fibers’ (OFs) limits, due to the large percentage of photons that scatters before reaching the receiver. However, random fluctuations of drones’ positions and atmospheric turbulence can affect the quality of the Free Space Optic (FSO) link with a significant impact on performance. Considering that Quantum Drone Networks (QDNs) require significant control, Software-Defined Networking (SDN) paradigm can play a key role in their provisioning. Specifically, an SDN Controller is responsible for managing the global strategies for the distribution of end-to-end (E2E) entangled pairs. Therefore, this paper provides the design of an SDN-based architecture for supporting high-performance Metropolitan Quantum Drone Networks (MQDNs) with a specific protocol for creating entanglement between two Ground Stations (GSs) through the swarm of drones. The proposed architecture can be employed for distributed quantum computing applications and entanglement-based Quantum Key Distribution (QKD) services. Moreover, a suited objective function to optimize the planning and operation of the swarm mission has been proposed. Finally, the paper provides a performance evaluation considering the most relevant metrics, such as fidelity, entanglement rate, and the overhead of the proposed protocol, pointing out that even higher performance than OFs is achievable.

Modeling and Control of Priority Queueing in Software Defined Networks via Machine Learning

Software Defined Networking (SDN) is a new architectural paradigm that enables programmable control of a network to make it more flexible and easier to manage. SDN architectures decouple control and forwarding functionalities, and enable switches and routers to be remotely configurable/ programmable in run-time by a controller. Modeling and optimization of such modern heterogeneous network infrastructures are key factors to achieve better performance, e.g. in terms of traffic flow improvement while reducing bandwidth allocation. Identifying an accurate model of a network device in SDNs (e.g., a switch or a router) is crucial in order to apply advanced techniques such as Model Predictive Control (MPC). However, such a problem is very challenging due to non-linearities and unavailability of internal variables measurements in real devices. To this aim, a promising direction is given by an appropriate integration of System Identification and Machine Learning techniques to obtain predictive models using historical data collected from the network thanks to the SDN paradigm. In this paper we apply a novel data-driven methodology to learn accurate models of the dynamical input-output behavior of a network&#x2019;s switch device by appropriately combining AutoRegressive eXogenous (ARX) model identification with Regression Trees (RTs) and Random Forests (RFs). The advantage of such model is that it can be directly used to apply MPC (which just requires Quadratic Programming to be solved) to optimally control the queues&#x2019; bandwidth of the switch ports within the SDN paradigm.We validate our approach on an experimental emulation setup using the Mininet network emulator environment and a real dataset obtained from measurements of an Italian Internet Service Provider (Sonicatel S.r.l.). To this aim, we first develop a model of a real network switch, then implement MPC using the RYU controller, and finally demonstrate the benefits of the proposed dynamic queueing control methodology in terms of packet losses reduction and bandwidth saving, i.e. in terms of improvement of the Quality of Service.

A software-defined multi-modal wireless sensor network for ocean monitoring

The software-defined networking paradigm enables wireless sensor networks as a programmable and reconfigurable network to improve network management and efficiency. However, several challenges arise when implementing the concept of software-defined networking in maritime wireless sensor networks, as the networks operate in harsh ocean environments, and the dominant underwater acoustic systems are with limited bandwidth and high latency, which render the implementation of software-defined networking central-control difficult. To cope with the problems and meet demand for high-speed data transmission, we propose a radio frequency–acoustic software-defined networking-based multi-modal wireless sensor network which leverages benefits of both radio frequency and acoustic communication systems for ocean monitoring. We first present the software-defined networking-based multi-modal network architecture, and then explore two examples of applications with this architecture: network deployment and coverage for intrusion detection with both grid-based and random deployment scenarios, and a novel underwater testbed design by incorporating radio frequency–acoustic multi-modal techniques to facilitate marine sensor network experiments. Finally, we evaluate the performance of deployment and coverage of software-defined networking-based multi-modal wireless sensor network through simulations with several scenarios to verify the effectiveness of the network.

PHeavy: Predicting Heavy Flows in the Programmable Data Plane

Since heavy flows account for a significant fraction of network traffic, being able to predict heavy flows has benefited many network management applications for mitigating link congestion, scheduling of network capacity, exposing network attacks and so on. Existing machine learning based predictors are largely implemented on the control plane of Software Defined Networking (SDN) paradigm. As a result, frequent communication between the control and data planes can cause unnecessary overhead and additional delay in decision making. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> , a machine learning based scheme for predicting heavy flows directly on the programmable data plane, thus eliminating network overhead and latency to SDN controller. Considering the scarce memory and limited computation capability in the programmable data plane, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> includes a packet processing pipeline which deploys pre-trained decision tree models for in-network prediction. We have implemented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> in both bmv2 software switch and P4 hardware switch (i.e., Barefoot Tofino). Evaluation results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> has achieved 85% and 98% accuracy after receiving the first 5 and 20 packets of a flow respectively, while being able to reduce the size of decision tree by 5.4x on average. More importantly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> can predict heavy flows at line rate on the P4 hardware switch.

A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT

Nowadays, in the industrial Internet of things, address resolution protocol attacks are still rampant. Recently, the idea of applying the software-defined networking paradigm to industrial Internet of things is proposed by many scholars since this paradigm has the advantages of flexible deployment of intelligent algorithms and global coordination capabilities. These advantages prompt us to propose a multi-factor integration-based semi-supervised learning address resolution protocol detection method deployed in software-defined networking, called MIS, to specially solve the problems of limited labeled training data and incomplete features extraction in the traditional address resolution protocol detection methods. In MIS method, we design a multi-factor integration-based feature extraction method and propose a semi-supervised learning framework with differential priority sampling. MIS considers the address resolution protocol attack features from different aspects to help the model make correct judgment. Meanwhile, the differential priority sampling enables the base learner in self-training to learn efficiently from the unlabeled samples with differences. We conduct experiments based on a real data set collected from a deepwater port and a simulated data set. The experiments show that MIS can achieve good performance in detecting address resolution protocol attacks with F1-measure, accuracy, and area under the curve of 97.28%, 99.41%, and 98.36% on average. Meanwhile, compared with fully supervised learning and other popular address resolution protocol detection methods, MIS also shows the best performance.

Optical networks management and control: A review and recent challenges

In the last twenty years, optical networks have witnessed recurrent changes in their management and control architecture. In this paper, we present a historical timeline and a future perspective of the evolution of optical network management and control deployed for Wavelength Switched Optical Networks (WSON), Elastic Optical Networks (EON) and (multilayer) Data Center Networks.Early implementations of WSON envisaged a static and centralized provisioning approach supported by the Management Plane only. Gradually, the requirement of accommodating more network dynamicity in WSON, and later in EON, pushed the adoption of a distributed control, mostly supported by vendor-dependent implementations of the Generalized MultiProtocol Label Switching (GMPLS) protocol suite. The drawbacks of the fully distributed GMPLS-based control, such as resource contention, suboptimal resource usage, and complex computations (e.g., to account for physical layer constraints) showed the necessity to bring back some of the routing/provisioning functions to a centralized Path Computation Element (PCE) capable of accounting for e.g. physical impairments and interworking with GMPLS.The centralized control then gained its momentum and brought a radical change in network control, through the separation of data and control plane introduced by the paradigm of Software Defined Networking (SDN). Such an approach has been gradually extended to optical network control.The paper, eventually, presents the most advanced control techniques, namely the intent-based networking, the observe/decide/act state-based approach providing for autonomic optical network and the (closed-loop) zero-touch service management approach. Advanced traffic conditioning techniques are also detailed, namely the in-band telemetry and the exploitation of Programming Protocol-Independent Packet Processors (P4) language capabilities as well as solutions tailored for data center networks: all of them are still in a research stage and to be integrated within future optical network architectures.

Performance Evaluation of Data-DrivenTechniques for Softwarized and AgnosticManagement of N×N Photonic Switch

The emerging Software Defined Networking (SDN) paradigm paves the way for flexible and automatized management at each layer. The SDN-enabled optical network requires each network element&#x2019;s software abstraction to enable complete control by the centralized network controller. Nowadays, silicon photonics due to its low energy consumption, low latency, and small footprint is a promising technology for implementing photonic switching topologies, enabling transparent lightpath routing in re-configurable add-drop multiplexers. To this aim, a model for the complete management of photonic switching systems&#x2019; control states is fundamental for network control. Typically, photonics-based switches are structured by exploiting the modern technology of Photonic Integrated Circuit (PIC) that enables complex elementary cell structures to be driven individually. Thus PIC switches&#x2019; control states are combinations of a large set of elementary controls, and their definition is a challenging task. In this scenario, we propose the use of several data-driven techniques based on Machine Learning (ML) to model the control states of a PIC N&#x00D7;N photonic switch in a completely blind manner. The proposed ML-based techniques are trained and tested in a completely topological and technological agnostic way, and we envision their application in a real-time control plane. The proposed techniques&#x2019; scalability and accuracy are validated by considering three different switching topologies: the Honey-Comb Rearrangeable Optical Switch (HCROS), Spanke-Bene&#x0161;, and the Bene&#x0161; network. Excellent results in terms of predicting the control states are achieved for all of the considered topologies.