Software Defined Networking Architecture Research Articles

A Multi-Controller Placement Strategy for Hierarchical Management of Software-Defined Networking

Software-Defined Networking (SDN) is a new architecture with symmetric/asymmetric network structures that separates the control plane of network devices from the data plane, and a Controller Placement Problem (CPP) is a critical management problem in SDN. The main research content of the CPP is to determine the number and location of controllers placed in a network topology, as well as the connection relationship between controllers and switches. However, traditional CPP solutions based on symmetric/asymmetric structures may not be efficient to meet the increasing requirements of SDN applications. In order to improve the CPP solutions from the viewpoint of hierarchical management, this paper considers the CPP solutions as a multi-objective optimization problem based on symmetric/asymmetric structures in the SDN architecture. Thus, this paper then proposes a multi-controller placement strategy based on an improved Harris Hawks Optimization algorithm. Firstly, the local controller load is limited, and a Sin chaotic map is introduced to initialize the CPP scheme. The total latency of the network, the reliability of the node, the total failure rate of the link and the total placement cost are seriously considered when placing the controllers. Secondly, a Cos nonlinear function is added to the global search. A dynamic adaptive weight factor is used to smooth the switching approach between the global search and the local search, so as to enhance the global search ability. Then, a Cauchy variation perturbation is added to the obtained CPP scheme to strengthen the diversity of CPP schemes, and the CPP scheme with the Pareto front is finally solved. The topology simulation of three real large-scale SDN networks shows that the proposed strategy, based on an improved Harris Hawks Optimization algorithm, has more robust advantages in comparison to other algorithms.

A Transmission-Reliable Topology Control Framework Based on Deep Reinforcement Learning for UWSNs

This paper focuses on the topology optimization problem to decrease transmission delay and prolong network lifetime on the premise of reliable transmission in Underwater Wireless Sensor Networks (UWSNs). This is extremely challenging owing to dynamic ocean current movement, harsh underwater communication channel and increasingly demanding application requirements. With the development of software defined network architectures, the centralized topology control (TC) strategy with a global perspective in UWSNs is expected to become a more effective way to tackle the above challenges compared with the existing distributed and heuristic TC strategies involving local network state information. Therefore, we first transform the topology optimization problem of UWSNs into an integer nonlinear programming (INLP) model and design a centralized TC framework to solve the INLP model. In this framework a TC center is built to periodically generate the network topology for UWSNs according to the current network state information. Further, an efficient topology generation algorithm based on deep reinforcement learning (TGA-DRL) is proposed in the TC center. In TGA-DRL, to reduce computing overhead and improve operational efficiency, we formulate an action-space narrowed Markov decision process suitable for network topology generation and solve it with the aid of the rainbow algorithm which is a deep reinforcement learning model. Finally, the performance of our centralized TC framework is verified in terms of the node out-degree, algorithm convergence and optimization effect.

Defending SDN against packet injection attacks using deep learning

The (logically) centralized architecture of software-defined networks makes them an easy target for packet injection attacks. In these attacks, the attacker injects malicious packets into the SDN network to affect the services and performance of the SDN controller and overflows the capacity of the SDN switches. Such attacks have been shown to ultimately stop the network functioning in real-time, leading to network breakdowns. There have been significant works on detecting and defending against similar DoS attacks in non-SDN networks, but detection and protection techniques for SDN against packet injection attacks are still in their infancy. Furthermore, many of the proposed solutions have been shown to be easily bypassed by simple modifications to the attacking packets or by altering the attacking profile. In this paper, we develop novel Graph Convolutional Neural Network models and algorithms for grouping network nodes/users into security classes by learning from network data. We start with two simple classes — nodes that engage in suspicious packet injection attacks and nodes that are not. From these classes, we then partition the network into separate segments with different security policies using distributed Ryu controllers in an SDN network. We show in experiments on an emulated SDN that our detection solution outperforms alternative approaches with above 99% detection accuracy for various types (both old and new) of injection attacks. More importantly, our mitigation solution maintains continuous functions of non-compromised nodes while isolating compromised/suspicious nodes in real-time. All code and data are publicly available for the reproducibility of our results.

ENHANCING SECURITY IN SOFTWARE-DEFINED NETWORKING THROUGH ROUTING TECHNIQUES EXPLORATION

In today's world, network security is a key issue of information security. Virtual Networks have become an integral part of modern IT infrastructure, which presents us with challenges in the field of security. One solution to this problem is the use of software-defined networking (SDN), which provides a means to control and manage network traffic. However, as with any technology, SDN has its vulnerabilities that must be considered when deploying it. One of the tools that helps to take into account the vulnerabilities of network infrastructure is the Common Vulnerability Scoring System (CVSS) standard. It allows you to quantify the level of vulnerability of the infrastructure, which enables effective network protection. Analysis of the CVSS standard is an important stage in the development of a network security strategy. This paper analyzes the standards for building software-configured networks. It is noted that SDN is a modern approach to the design, construction, and operation of information communication networks. Using SDN makes it possible to directly program and dynamically manage the network, as well as to abstract the functionality of the infrastructure layer. However, the growing interest in SDN has revealed the shortcomings of their application in the fight against cybersecurity threats. The SDN architecture itself, external malicious attacks, and insufficient access control and encryption tools were found to be the main security challenges. The use of secure routing tools based on vulnerability metrics is proposed to increase the level of SDN data plane network security. According to the conducted analysis of SDN data plane vulnerabilities and the functionality of routing tools, the authors recommend using the CVSS standard to quantify the level of infrastructure vulnerability during the development and research of promising approaches to secure routing in the data plane of software-configured networks.

SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm.

The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved Sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%.

Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems

A decentralized power distribution network consisting of smart microgrids introduces opportunities to trade with energy called transactive energy. However, research studies in the existing literature suggest that several standardized information models for TE do not meet the network architecture’s reliability, flexibility, and security requirements. This limitation is mainly due to the static nature of traditional IP infrastructure. To achieve these requirements in the network architecture, this study investigates the optimized application of software-defined network architecture for transactive energy in smart microgrid systems. Through literature research, unique design approaches in an SDN architecture are identified that improve the reliability, flexibility, and security of the SDN architecture. These design approaches include a decentralized controller network layout, redundant link configuration, a mesh network topology, and data encryption. The proposed solution uniquely combines these design approaches into a single optimized SDN solution for TESMS. To validate the improvements of the findings from the literature research, each design approach is simulated in this study using Mininet SDN emulator and AnyLogic system simulation software. The proposed solution is then applied to a use-case scenario that shows the improvements required for TESMS. The use-case scenario shows significant improvement in the data path uptime. An improvement of 0.27% is achieved, which equates to a 2 h per month increase in the data path uptime. The results of the simulation show that the proposed SDN architecture improves the reliability and flexibility of a traditional SDN network. Furthermore, enabling encryption between the nodes improves the security of the SDN architecture.

A Novel Dynamic Software-Defined Networking Approach to Neutralize Traffic Burst

Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation.

Deep-learning-based Intrusion Detection for Software-defined Networking Space Systems

This paper briefly reviews the application of the Software-defined Networking (SDN) architecture to satellite networks. It highlights the prominent cyber threats that SDN-based satellite networks are vulnerable to and proposes relevant defence mechanisms. SDN transforms traditional networking architectures by separating the control plane from the forwarding (data) plane. This separation enhances scalability and centralises management. In comparison, in traditional networks, the control plane and the data plane are usually combined, resulting in complex network management and reduced scalability. Satellite networks can take advantage of these benefits offered by SDN and this supports them as key enablers of critical services, including weather prediction, global broadband Internet coverage, and Internet of Things (IoT) services. Ease of configuration and flexibility are essential for satellites providing critical services to instantly adapt to network changes. These desirable attributes can be realised by applying SDN to satellite networks. Although SDN offers significant benefits to satellite networks, it is vulnerable to cyber-attacks and particularly due to its centralised architecture. A common attack on SDN is the Distributed Denial of Service (DDoS) attack which could render the entire SDN unavailable. To mitigate such threats, an efficient Intrusion Detection System (IDS) is required to monitor the network and detect any suspicious traffic. However, traditional IDSs produce too many false positives and often fail to detect advanced attacks. For their ability to learn feature hierarchies in network traffic data automatically, whether, for network traffic classification or anomaly detection, deep learning (DL) plays an increasingly important role in IDSs. In this paper, we present a brief review of recent developments in cyber security for SDN-based space systems, and we identify vulnerabilities and threats to an SDN-based satellite network. We further discuss the potential of a DL-based IDS for the detection of cyber threats. Finally, we identify further research gaps in the recent literature and propose future research directions.

Detection of DDOS Attack using Decision Tree Classifier in SDN Environment

Software Defined Networking (SDN) is a dynamic architecture that employs a variety of applications for making networks more adaptable and centrally controlled. It is easy to attack the entire network in SDN because the control plane and data plane are separated. DDoS attack is major danger to SDN service providers because it can shut down the entire network and stop services to all customers at any time. One of the key flaws of most SDN architectures is lack of susceptibility to DDoS attacks with its types like TCP flooding, UDP flooding, SYN flooding, ICMP flooding and DHCP flooding for detecting those kinds of attacks. The machine learning algorithms are widely used in recent years to identify DDoS attacks. This research utilizes Decision Tree Classifier for detection and classification of DDoS attacks on SDN. The Forward Feature Selection technique is also used in the research to select the best features from the dataset and from that dataset the data are employed to train and test the model by Decision Tree Classifier Algorithm. The decision Tree Classifier technique is a supervised method used to forecast desired values of observations using rudimentary machine learning decision rules derived from training data. Based on the accuracy of decision tree techniques, in future, a hybrid learning model will be designed for detecting the Distributed Denial of Services in an SDN environment with high accuracy and a low false negative rate.

SDN-Controlled Resource-Tailored Analytics for Healthcare IoT System

In this work, we propose phSDN, a programmable healthcare network architecture for resource-tailored management and on-demand allocation of analytic modules in Internet of Things (IoT) healthcare system. The identification, dynamic management of services, and fast analysis of healthcare data are a few challenges in existing remote healthcare monitoring devices. phSDN programs and reconfigures the resources and services, while updating the associated flow-rules in the data-plane of software-defined network (SDN) architecture. It is especially useful in scenarios with intermittent or constrained network connectivity and/or device availability. The newly identified flows associated with different resources are explicitly considered, while forwarding services over the SDN network. The on-the-fly programmable services enable healthcare devices to provide resource-specific analytics. The programmability over services also allows unified control over the end-devices by reconfiguring on-the-fly vendor-specific heterogeneity of resource types. Our prototype of phSDN supports three different types of resources: 1) body temperature, 2) pulse rate, and 3) blood pressure. Experiments using our implementation show that the proposed architecture minimizes the network response time by at least 43.87% and 78.4%, compared to the fog- and cloud-based approaches, respectively. The implemented system's scalability is further evaluated using Mininet by considering latency, packet delivery ratio, and throughput, as the performance metrics.

Intelligent Spectrum Anti-Jamming With Cognitive Software-Defined Architecture

Malicious jamming in congested, contested, and constrained wireless environments has stimulated the demand of jamming-resistant communications. Nevertheless, classical anti-jamming techniques suffer from not only an extremely low data rate but also the risk of being interfered by the recently developed high-power jammers. As a promising solution, dynamic spectrum access (DSA) with software-defined platforms and cognitive functionality has been conceptualized, while systematic design remains an open issue. To this end, in this article, we propose a cognitive software-defined network (CSDN) architecture. Based on this, spectrum semantic extraction is developed to remove irrelevant spectrum features, such that learning a frequency preconfiguration policy (for DSA) can directly utilize the spectrum occupancy information. In order to learn the policy online, we propose a novel kernel-based distributed actor-critic algorithm, where the kernel method and online feature admission are exploited to make policy learning more adaptive. Numerical results show that semantics-driven reinforcement learning (RL) makes policy learning more efficient and accurate. Moreover, the proposed RL outperforms various baseline algorithms in terms of the strength in adapting to dynamic jamming patterns as well as the convergence speed of policy learning. Finally, a testbed is prototyped to validate the CSDN-based intelligent spectrum anti-jamming communication system.

Placement of SDN controllers based on network setup cost and latency of control packets

Software-defined networking (SDN) was developed to simplify the management of computer networks. In the SDN architecture, the control layer is completely decoupled from the data layer. Moreover, network software configuration is also possible. To setup an SDN, you need a device called controller, which is not available in traditional networks. In the control layer, there are several controllers that control the data layer switches. The type, number and place of these controllers directly affect the cost and average latency of control packets. Since achieving the lowest setup cost and the minimum latency of the control packets (between switches and controllers, and among controllers) is of great importance at the same time, in this paper, a binary linear programming model is proposed to achieve the trade-off between them in wide area networks(WAN). The model determines the type, place and minimum number of required controllers by receiving the place of the switches, the amount of processing capacity of the controllers and the setup cost of each controller. Accordingly, it reduces the setup cost, average latency between switches and controllers as well as average latency among controllers in comparison with previous methods. The results of the study show that the model has the lowest setup cost and the minimum average latency of control packets (switch-to-controller and controller-to-controller) in controllers placement compared to the previous methods.

Detection of anomalies of a non-deterministic software-defined networking control

Software Defined Networking (SDN) is a network architecture within the control is centralized through a software-based controller. Being a single point of attack makes the controller the preferred target in the SDN architecture. Multi-controller architecture has been introduced to reinforce the control plane. However it requires a communication interface between the controllers which is a security threat. In this objective, a dual controller architecture is introduced and it consists of one nominal controller in charge of the data plane computation plus a second one in charge of the detection of anomalies in the decisions taken by the main controller. In the case of non-determinist algorithm, the detection logic aims at determining a likelihood score of the decisions taken by the controller. A multi-criterion detection approach is proposed by considering both the performance of the decisions and the structure of the decisions taken by the controller. Such computations are probabilistic and attention has been paid to machine learning algorithms to determine this likelihood. More precisely, three formalisms are compared: Probabilistic Finite Automaton, Hidden Markov Model and Recurrent Neural Network. The impact of the control variance in the detection accuracy depending on the formalism used is discussed on a case study.

Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks

The Software-Defined Networking (SDN) paradigm is one that is utilized frequently in data centers. Software-Defined Wireless Networking, often known as SDWN, refers to an environment in which concepts from SDN are implemented in wireless networks. The SDWN is struggling with challenges of scalability and performance as a result of the growing number of wireless networks in its coverage area. It is thought that SDN techniques, such as Mininet-Wi-Fi and Ryu Controller for wireless networks, can overcome the problems with scalability and performance. Existing Wi-Fi systems do not provide SDN execution to end clients, which is one reason why the capability of Wi-Fi is restricted on SDN architecture. Within the scope of this study, we analyzed Wi-Fi networks operating on SDN using the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). By utilizing a testbed consisting of Ryu Controller and Mininet-Wi-Fi, we were able to test Wi-Fi over SDN and evaluate its performance in addition to its scalability. When evaluating the performance of a network, we take into account a number of different metrics, such as bandwidth, round-trip time, and jitter. In order to assess the level of performance, the SDN-based Wi-Fi controller Ryu is linked to an increasing number of access points (1, 2, 3, and 4) as well as stations (10, 30, 50, and 100). The experimental findings obtained using Mininet-Wi-Fi indicate the scalability and dependability of the network performance provided by the SDN Wi-Fi network controller Ryu in an SDN environment. In addition, the round-trip time for TCP packets grows proportionally with the number of hops involved. A single access point is capable of simultaneously supporting up to fifty people at once.

Jointly optimized resource allocation for SDN control and forwarding planes in edge-cloud SDN-based networks

Today’s resource allocation strategies for edge-cloud network based on Software-Defined Network (SDN) and Network Function Virtualization (NFV) architectures often make a decision of placing resource based on network locations, i.e. edge or core cloud. Unfortunately, these strategies are not optimally tailored for a growing class of Internet-of-Things (IoT) services whose traffic is designated to go through a Virtual Network Functions (VNF) service chain. Without considering the communication between SDN control plane and forwarding plane, inappropriate SDN controllers could be selected for switches, which may result in a significant setup delay of the whole service chain. In addition, the fluctuate demand of IoT services requires the entire system, that is composed of controllers, forwarding elements, i.e. switches, and physical machines, being stable during service provisioning. In this paper, we jointly address two issues, i.e. SDN controller-switch assignment and VNF placement, both are high-complexity problems. We formulate the joint optimization problem of dynamically provisioning resource for SDN controllers and VNFs in a long run. We then apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problems and employ exponentiated gradient ascent (EGA) method to find a near-optimal solution. Extensive simulation and experiments show that our approach helps to optimize the system cost up to 11 ∼ 40% depending on service demand and network size.

Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach

In this study, we present and implement the SAnDet (SDN anomaly detector) architecture, an anomaly-based intrusion detection system designed to take advantage of the capabilities offered by software-defined networking (SDN) architecture, as a controller application. The SAnDet system is composed of three modules: statistics collection, anomaly detection, and anomaly prevention. In particular, we utilize replicator neural networks (RNN), which is a specialized variant of the autoencoder, and the LSTM-based encoder–decoder (EncDecAD) method, which is a special type of long short-term memory (LSTM) network that has demonstrated a strong performance on data series particularly, to identify unknown attacks using flow features collected from OpenFlow switches. In our experiments, we utilize flow-based features extracted from network traffic data containing various types of attacks as input to our models in the form of time series. We evaluate the performance of our methods using the accuracy and area under the receiver operating characteristic curve (AUC) metrics. Our experimental results demonstrate that EncDecAD outperforms RNN and that our approach offers several benefits over previously conducted research.

A Secure Multilayer Architecture for Software-Defined Space Information Networks

Both space information networks (SINs) and software-defined networking (SDN) have gained considerable attention from industry and academia in recent years. Due to the unique characteristics of SDN and SINs, a hybrid version of them, e.g., software-defined SINs, can handle many complicated tasks. Some technological advances based on SDN are increasingly being deployed to satellite networks. In this context, the multilayer architecture makes it difficult to control the different physical devices with maximum network performance for vast volumes of traffic transmission. The multilayer architecture is considered to be a versatile framework to include different applications and facilities efficiently, while it enjoys the support of SDN as well. In this context, this article proposes a secure multilayer SDN architecture that separates the paradigm into terrestrial, aerial, and ground domains and facilitates security solutions. We explore the specifics of this architecture's development and implementation, hence finding out some problems and unanswered questions. In addition, our descriptive results demonstrate that the proposed architecture will significantly improve the multilayer efficiency gains of configuration upgrading and decision-making.

Collaborative prediction and detection of DDoS attacks in edge computing: A deep learning-based approach with distributed SDN

Edge computing (EC) has greatly facilitated the deployment of networked services with fast responses and low bandwidth, by deploying computing and storage at the network edge which is closer to the data sources. However, it is challenging to have the EC servers gain security protections like those in centralized data centers, making them more vulnerable to security attacks, especially distributed denial-of-service (DDoS) attacks. Existing detection approaches relying on the feedback from EC servers under the attacks can incur high bandwidth costs and service performance degradation. In this paper, we propose a new framework CoWatch for collaborative prediction and detection of DDoS Attacks in EC scenarios. Based on the distributed software-defined networking (SDN) architecture, CoWatch can collaboratively predict the DDoS attacks towards the EC servers and detect the attack flows in time. To efficiently filter the suspicious flows in distributed SDN, we devise an optimal threshold model by balancing the trade-off between collaboration efficiency and prediction effectiveness. We also explore and build on the LSTM model to design an algorithm for collaborative prediction and detection of DDoS Attacks. Experiment results on a number of datasets demonstrate the promising performance of CoWatch in effectiveness and efficiency.

Low Rate DDoS Detection Using Weighted Federated Learning in SDN Control Plane in IoT Network

The Internet of things (IoT) has opened new dimensions of novel services and computing power for modern living standards by introducing innovative and smart solutions. Due to the extensive usage of these services, IoT has spanned numerous devices and communication entities, which makes the management of the network a complex challenge. Hence it is urgently needed to redefine the management of the IoT network. Software-defined networking (SDN) intrinsic programmability and centralization features simplify network management, facilitate network abstraction, ease network evolution, has the potential to manage the IoT network. SDN’s centralized control plane promotes efficient network resource management by separating the control and data plane and providing a global picture of the underlying network topology. Apart from the inherent benefits, the centralized SDN architecture also brings serious security threats such as spoofing, sniffing, brute force, API exploitation, and denial of service, and requires significant attention to guarantee a secured network. Among these security threats, Distributed Denial of Service (DDoS) and its variant Low-Rate DDoS (LR-DDoS), is one of the most challenging as the fraudulent user generates malicious traffic at a low rate which is extremely difficult to detect and defend. Machine Learning (ML), especially Federated Learning (FL), has shown remarkable success in detecting and defending against such attacks. In this paper, we adopted Weighted Federated Learning (WFL) to detect Low-Rate DDoS (LR-DDoS) attacks. The extensive MATLAB experimentation and evaluation revealed that the proposed work ignites the LR-DDoS detection accuracy compared with the individual Neural Networks (ANN) training algorithms, existing packet analysis-based, and machine learning approaches.

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.