Software Defined Networking Nodes Research Articles

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system’s accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.

Joint energy efficiency and load balancing optimization in hybrid IP/SDN networks

Software-defined networking (SDN) is a paradigm that provides flexibility and programmability to computer networks. By introducing SDN nodes in a legacy IP network topology, network operators can benefit on higher control over the infrastructure. However, this migration is not a fast or straightforward process. Furthermore, to provide an adequate quality of service in hybrid IP/SDN networks, the coordination of both IP and SDN paradigm is fundamental. In this paper, this coordination is used to solve two optimization problems that are typically solved separately: (i) traffic load balancing and (ii) power consumption minimization. Each of these problems has opposing objectives, and thus, their joint consideration implies striking a balance between them. Therefore, this paper proposes the Hybrid Spreading Load Algorithm (HSLA) heuristic that jointly faces the problems of balancing traffic by minimizing link utilization and network’s power consumption in a hybrid IP/SDN network. HSLA is evaluated over differently sized topologies using different methods to select which nodes are migrated from IP to SDN. These evaluations reveal that alternative approaches that only address one of the objectives are outperformed by HSLA.

QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing.

Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%.

A Novel Blockchain Based Secured and QoS Aware IoT Vehicular Network in Edge Cloud Computing

A software-defined vehicular network is made up of an IoT (Internet of Things) based vehicular ad-hoc network and a software-defined network. For better communication in IoT based vehicle networks, researchers are now working on the VANET (Vehicular Ad-hoc Network) to increase the overall system performance. To maximize the VANET ad-hoc network’s information application performance and reliability, edge computing has gained the attention of researchers. In current research, cloud computing is used for message related task execution, which increases the response time. We propose a Software-defined Fault Tolerance and QoS-Aware (Quality of Service) IoT-Based Vehicular Networks Using Edge Computing Secured by Blockchain to reduce overall communication delay, message failure fault tolerance, and secure service provisioning for VANET ad-hoc networks in this article. We proposed heuristic algorithms to solve the above mentioned problems of response delay, message failure, fault tolerance, and security provided by the Blockchain. The proposed model gets vehicle messages through SDN (Software defined network) nodes, which are placed on nearby edge servers, and the edge servers are validated by the blockchain to provide secure services to vehicles. The SDN controller, which exists on an edge server, which is placed on the road side to overcome communication delays, receives different messages from the vehicles and divides these messages in to two different categories. The message division is performed by the edge server by judging the time line, size, and emergency situation. SDN controller organized these messages and forwarded them to their destination. After the message is delivered to its destination, a fault tolerance mechanism checks their acknowledgements. If the message delivery fails, the fault tolerance algorithm will resend the failure message. The proposed model is implemented using a custom simulator and compared with the latest VANET based QoS and fault tolerance models. The result shows the performance of the proposed model, which decreased the overall message communication delay by 55% of the normal and emergency messages by using the edge server SDN controller. Furthermore, the proposed model reduces the execution time, security risk, and message failure ratio by using the edge server, cloud server and blockchain infrastructure.

Energy efficiency through joint routing and function placement in different modes of SDN/NFV networks

Network function virtualization (NFV) and software-defined networking (SDN) are two promising technologies to enable 5G and 6G services and achieve cost reduction, network scalability, and deployment flexibility. However, migration to full SDN/NFV networks in order to serve these services is a time-consuming process and costly for mobile operators. This paper focuses on energy efficiency during the transition of mobile core networks (MCN) to full SDN/NFV networks and explores how energy efficiency can be addressed during such migration. We propose a general system model containing a combination of legacy nodes and links, in addition to newly introduced NFV and SDN nodes. We refer to this system model as partial SDN and hybrid NFV MCN, which can cover different modes of SDN and NFV implementations. Based on this framework, we formulate energy efficiency by considering joint routing and function placement in the network. Since this problem belongs to the class of non-linear integer programming problems, to solve it efficiently, we present a modified Viterbi algorithm (MVA) based on multi-stage graph modeling and a modified Dijkstra’s algorithm. We simulate this algorithm for a number of network scenarios with different fractions of NFV and SDN nodes and evaluate how much energy can be saved through such transition. Simulation results confirm the expected performance of the algorithm, which saves up to 70% energy compared to a network where all nodes are always on. Interestingly, the amount of energy saved by the proposed algorithm in the case of hybrid NFV and partial SDN networks can reach up to 60%–90% of the saved energy in full NFV/SDN networks.

On the Optimal Lawful Intercept Access Points Placement Problem in Hybrid Software-Defined Networks.

For the law enforcement agencies, lawful interception is still one of the main means to intercept a suspect or address most illegal actions. Due to its centralized management, however, it is easy to implement in traditional networks, but the cost is high. In view of this restriction, this paper aims to exploit software-defined network (SDN) technology to contribute to the next generation of intelligent lawful interception technology, i.e., to optimize the deployment of intercept access points (IAPs) in hybrid software-defined networks where both SDN nodes and non-SDN nodes exist simultaneously. In order to deploy IAPs, this paper puts forward an improved equal-cost multi-path shortest path algorithm and accordingly proposes three SDN interception models: T interception model, ECMP-T interception model and Fermat-point interception model. Considering the location relevance of all intercepted targets and the operation and maintenance cost of operators from the global perspective, by the way, we further propose a restrictive minimum vertex cover algorithm (RMVCA) in hybrid SDN. Implementing different SDN interception algorithms based RMVCA in real-world topologies, we can reasonably deploy the best intercept access point and intercept the whole hybrid SDN with the least SDN nodes, as well as significantly optimize the deployment efficiency of IAPs and improve the intercept link coverage in hybrid SDN, contributing to the implementation of lawful interception.

Soft-Mesh: A Robust Routing Architecture for Hybrid SDN and Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are considered self-organizing, self-healing, and self-configuring networks. Despite these exciting features, WMNs face several routing challenges including scalability, reliability and link failures, mobility, flexibility, and other network management issues. To address these challenges, WMNs need to make programmable to allow modifications of standard techniques to be configured and implemented through software programs that can be resolved by integrating Software Defined Networking (SDN) architecture. SDN, being a cutting-edge technology promises the facilitation of network management as well as routing issues of wireless mesh networks. However, the evolution of the legacy IP-based network model in its entirety leads to technical, operational, and economic problems that can be mitigated by full interoperability between SDN and existing IP devices. This study introduces a Robust Routing Architecture for Hybrid Software-Defined and Wireless Mesh Networks (Soft-Mesh), by systematic and gradual transitioning of WMNs to SDNs in an efficient manner. The main objective of this paper is to suggest improvements to the architecture of the SDN node that allow the implementation of various network functions such as routing, load balancing, network control, and traffic engineering for the hybrid SDN and IP networks. Mininet-WiFi Simulator is used to perform various experiments to evaluate the performance of proposed architecture by creating a hybrid network topology with a varying number of nodes that is 50, 100, 150, 200, and 250 including SDN hybrid and legacy nodes with varying proportion of SDN hybrid and legacy nodes. Results are taken for the average UDP throughput, end-to-end delay, packet drop ratio, and routing overhead while comparing with traditional routing protocols including Optimized Link State Routing (OLSR) and Better Approach to Mobile Adhoc Networking (BATMAN) and with existing hybrid SDN/IP routing architectures including Hakiri and wmSDN. The analysis of simulation results shows that the proposed architecture Soft-Mesh outperforms in terms of the aforementioned performance metrics than the traditional and exiting hybrid routing protocols. Soft-Mesh gives 50% to 70% improved results concerning the incremental proportion of SDN hybrid nodes.

Improving dynamic service function chaining classification in NFV/SDN networks through the offloading concept

Service Function Chaining (SFC) paradigm improves network capabilities thanks to the support of application-driven-networking, which is realized through the invocation of an ordered set of Service Functions (SFs). The programmability and flexibility provided by emerging technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are perfect features for efficiently managing the lifecycle of SFCs. However, the limitation of Ternary Content Address Memories (TCAMs) of the SDN nodes in SDN-based SFC scenarios can lead to network performance degradation when the SFC classifier is not able to install new classification rules. To tackle the Dynamic Chain Request Classification Offloading (D-CRCO) problem presented in this paper, a hybrid eviction and split-and-distribute approach is proposed, where i) the dynamic behavior of SFC requests is exploited by removing the corresponding idle rules from the flow tables if necessary; and ii) SFC classification is not forced to be carried out by the ingress node, but by any transit node in the domain. An Integer Linear Programming (ILP) formulation and an heuristic are provided to solve D-CRCO, with the goal of maximizing the number of SFC requests that can be served, respecting TCAM size, link capacity, and SF availability constraints. Simulation and emulation results over two real topologies show that the proposed solution is able to significantly increase the number of served SFC requests with a negligible impact on the network performance.

MitM detection and defense mechanism CBNA-RF based on machine learning for large-scale SDN context

Software defined network (SDN) is a promising new network abstraction that aims to improve and facilitate network management. Due to its centralized architecture and the lack of intelligence on the data plane, SDN suffers from many security issues that slows down its deployment. Man in the Middle (MitM) attack is considered as one of the most devastating attacks in an SDN context. In fact, MitM attack allows the attackers to capture, duplicate and spoof flows by targeting southbound interfaces and SDN nodes. Furthermore, it’s very difficult to detect MitM attacks since it is performed passively at the SDN level. To reduce the impact of this attack, we generally set up security policies and authentication mechanisms. However, these techniques are not applicable for a large scale SDN architecture as they require complexes and static configurations and as they negatively influence on network performance. In this paper, we propose an intrusion detection and prevention framework by using machine learning techniques to detect and stop MitM attempts. To do so, we build a context-based node acceptance based on the random forest model (CBNA-RF), which helps to setting-up appropriate security policies and to automating defense operations on a large-scale SDN context. This mechanism can realize a quick and early detection of MitM attacks by automatically detecting malicious nodes without affecting performances. The evaluation of the proposed framework demonstrates that our model can correctly classify and detect malicious connections and nodes while keeping high accuracy and precision scores.

Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN

To empower advanced traffic engineering (TE) mechanism, while considering the infeasibility of one-step migration to software-defined networking (SDN), SDN nodes are incrementally deployed into legacy network, which gives rise to hybrid SDN. In hybrid SDN, redirecting flow of every source-destination pair through at least one SDN node, can enhance TE performance and obtain flow manageability, while on the other hand leading to increasing demands of TCAM resources in SDN nodes. In this paper, we make minimization of maximum link utilization as the TE objective, and comply with SDN waypoint enforcement and TCAM resource limitation. We first formulate the TE problem as an integer linear programming (ILP) model and solve it in a centralized manner, where SDN waypoint selection and splitting fractions for each flow are jointly determined. Then, based on a fact that the logically centralized control plane in hybrid SDN is composed of multiple physically decentralized controllers, each of which manages part of SDN nodes, as well as considering a real situation that a centralized solution is infeasible or too fragile for large-scale network, we develop a distributed algorithm deriving from Lagrangian decomposition theory to effectively solve the TE problem. The simulation results indicate that, when 30% of the SDN nodes are deployed, the proposed traffic engineering-aware distributed routing (TEDR) algorithm obtains maximum link utilization comparable to that of full SDN, and has a limited influence on the routing efficiency.

SOTE: Traffic engineering in hybrid software defined networks

Traffic engineering (TE) is an efficient method to balance flows in a network. Existing TE solutions mainly focus on routing traffic along shortest paths between source and destination nodes, but ignore the likely network congestion. With the emergence of Software Defined Networking (SDN), through the deployment of SDN switches, we can enable flexible routing by splitting flows arbitrarily among multi-paths. However, due to technical, organizational and economical challenges of full SDN deployment, deploying SDN incrementally may be a more practical solution. Therefore, in this paper, we focus on a hybrid SDN architecture, where both legacy routers and SDN switches coexist. To explore routing optimization of TE in a hybrid SDN, we formulate a Mixed Integer Non-Linear programming (MINLP) problem and propose a heuristic algorithm SOTE to jointly optimize the OSPF weight setting and traffic splitting ratio of SDN nodes. Through extensive simulation and implementation on our prototyped testbed, we demonstrate that SOTE outperforms other algorithms in minimizing the maximum link utilization (MLU) of a network. Moreover, we also demonstrate that 10% deployment of SDN nodes can reap the most of benefit.

Optimizing Gradual SDN Upgrades in ISP Networks

Nowadays, there is a fast-paced shift from legacy telecommunication systems to novel software-defined network (SDN) architectures that can support on-the-fly network reconfiguration, therefore, empowering advanced traffic engineering mechanisms. Despite this momentum, migration to SDN cannot be realized at once especially in high-end networks of Internet service providers (ISPs). It is expected that ISPs will gradually upgrade their networks to SDN over a period that spans several years. In this paper, we study the SDN upgrading problem in an ISP network: which nodes to upgrade and when we consider a general model that captures different migration costs and network topologies, and two plausible ISP objectives: 1) the maximization of the traffic that traverses at least one SDN node, and 2) the maximization of the number of dynamically selectable routing paths enabled by SDN nodes. We leverage the theory of submodular and supermodular functions to devise algorithms with provable approximation ratios for each objective. Using real-world network topologies and traffic matrices, we evaluate the performance of our algorithms and show up to 54% gains over state-of-the-art methods. Moreover, we describe the interplay between the two objectives; maximizing one may cause a factor of 2 loss to the other. We also study the dual upgrading problem, i.e., minimizing the upgrading cost for the ISP while ensuring specific performance goals. Our analysis shows that our proposed algorithm can achieve up to 2.5 times lower cost to ensure performance goals over state-of-the-art methods.

Reducing the reconfiguration cost of flow tables in energy-efficient Software-Defined Networks

Software-Defined Networking (SDN) is a new networking paradigm that is attracting the attention of the research community due to the flexibility provided by the separation between data and control planes. In particular, the SDN scenario introduces new aspects to be considered when formulating the energy-aware routing problem, such as the reconfiguration cost of flow tables. In this paper we introduce and investigate the problem of minimizing the power consumption of an SDN network while also reducing the number of rules that have to be modified in the flow tables of SDN nodes. An optimization problem formulation and a GA (Genetic Algorithm) based heuristic are presented to tackle this two-fold problem. The performance analysis, carried out over different realistic network topologies, highlights that GA is able to increase the power saving opportunities up to the 20% more than other energy-aware routing solutions proposed in the literature, while reducing the number of rules that have to be modified up to 100 times.

Exploiting the control power of SDN during the transition from IP to SDN networks

SummaryThe emerging software‐defined networking (SDN) paradigm introduces new opportunities to improve network performance due to the flexibility and programmability provided by a logically centralized element named controller. However, a rapid adoption of the full SDN architecture is difficult in the short term due to economic and technical reasons. This paper faces the SDN nodes replacement problem during the transition from traditional IP networks to fully deployed SDN networks. Six different replacement methods are proposed to select the most appropriate set of traditional IP nodes to be upgraded to SDN‐enabled switches at a particular transition stage. To show the effectiveness of the proposed methods, they have been applied on an optimization problem currently studied by the research community: the power consumption problem. An integer linear programming formulation is presented to solve it and a genetic algorithm is evaluated through simulations on realistic network topologies. Results highlight that energy‐efficiency in hybrid IP/SDN networks can be significantly improved by only replacing a reduced number of IP nodes.

Legacy IP-upgraded SDN nodes tradeoff in energy-efficient hybrid IP/SDN networks

Although most of the efforts to tackle the energy consumption problem have been made on traditional IP networks, energy-aware protocols normally require of several coordination tasks that are not easy to implement on current networking equipment. To this end, flexibility and programmability provided by the emerging Software-Defined Networking (SDN) paradigm allow researchers to propose new coordinated energy-aware solutions by exploiting the centralized operation of the SDN controller. However, a full SDN deployment is difficult in the short-term due to budget constraints and technical challenges. This paper investigates the problem of saving energy in hybrid IP/SDN networks, where both network architectures coexist at the same time. In particular, a study on the trade-off between the amount of energy that could be saved and the number and location of legacy IP routers to be replaced by (or upgraded to) SDN switches is performed. Results from the proposed Genetic Algorithm-based heuristic show that a reduced percentage such as 50% of SDN nodes would be enough to achieve similar power savings than the ones obtained by a full SDN network, with a performance gap less than 5.5%.

Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks.

Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller’s direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20–40% while ensuring feasible data delay.

The Budgeted Maximum Coverage Problem in Partially Deployed Software Defined Networks

Due to the large installed base of distributed legacy networks, software defined networking (SDN) nodes may be required to coexist with legacy nodes to form hybrid networks. It has been shown that such a hybrid network has better performance than a pure legacy network due to smarter transmission scheduling. Despite such advantages, limited budgets continue to hinder the rapid adaptation of SDNs. Only a part of the network can be upgraded at a time especially for large-scale networks. In this paper, we define the minimum percentage of SDN nodes in a path, and paths with at least one SDN node, as the hop coverage and path coverage, respectively. We intend to evaluate the relationship between cost and coverage in the partially deployed SDNs. We formulate SDN node selection as four optimization problems with hop/path coverage and cost as objectives and constraints, respectively, and vice-versa. We propose two heuristic solutions: 1) maximum number of uncovered path first (MUcPF) and 2) maximum number of minimum hop covered path first (MMHcPF), to these NP-hard problems. Through a MATLAB experiment, we show that MUcPF is significantly better in terms of economy and efficiency to establish a hybrid path between every pair of hosts in the network. In particular, it required 5%–15% less investment to achieve 100% path coverage compared to other algorithms. The results show the coverage consistency of MMHcPF on each individual path along with gains in terms of cost and efficiency. It takes 5%–20% less investment to achieve certain hop coverage target compared to other existing algorithms.

Saving Energy in Partially Deployed Software Defined Networks

As power consumption of the Internet has been growing quickly in recent years, saving energy has become an important problem of networking research, for which the most promising solution is to find the minimum-power network subsets and shut down other unnecessary network devices and links to satisfy changing traffic loads. However, in traditional networks, it is difficult to implement a coordinated strategy among the network devices due to their distributed network control. On the other hand, the new networking paradigm—software defined network (SDN) provides us an efficient way of having a centralized controller with a global network view to control the power states. As an emerging technology, SDNs usually coexist with traditional networks at present. Therefore, we need to investigate how to save energy in partially deployed SDNs. In this paper, we formulate the optimization problem of finding minimum-power network subsets in partially deployed SDNs. After proving the problem is NP-hard, we propose a heuristic solution to approach its exact solution. Through extensive simulations, we demonstrate that our heuristic algorithm has a good performance; that is, on average we can save about 50 percent of total power consumption in the full SDN, having a distance less than 5 percent of the exact solution's power consumption. Moreover, it also achieves good performance in the partially deployed SDN, on average saving about 40 percent of the total power consumption when there are about 60 percent SDN nodes in the network. Meanwhile, it runs significantly faster than a general linear solver of this problem, by reducing the computation time of the network containing hundreds of nodes by 100× at least.

Reliable and Fault-Tolerant Software-Defined Network Operations Scheme for Remote 3D Printing

The recent wide expansion of applicable three-dimensional (3D) printing and software-defined networking (SDN) technologies has led to a great deal of attention being focused on efficient remote control of manufacturing processes. SDN is a renowned paradigm for network softwarization, which has helped facilitate remote manufacturing in association with high network performance, since SDN is designed to control network paths and traffic flows, guaranteeing improved quality of services by obtaining network requests from end-applications on demand through the separated SDN controller or control plane. However, current SDN approaches are generally focused on the controls and automation of the networks, which indicates that there is a lack of management plane development designed for a reliable and fault-tolerant SDN environment. Therefore, in addition to the inherent advantage of SDN, this paper proposes a new software-defined network operations center (SD-NOC) architecture to strengthen the reliability and fault-tolerance of SDN in terms of network operations and management in particular. The cooperation and orchestration between SDN and SD-NOC are also introduced for the SDN failover processes based on four principal SDN breakdown scenarios derived from the failures of the controller, SDN nodes, and connected links. The abovementioned SDN troubles significantly reduce the network reachability to remote devices (e.g., 3D printers, super high-definition cameras, etc.) and the reliability of relevant control processes. Our performance consideration and analysis results show that the proposed scheme can shrink operations and management overheads of SDN, which leads to the enhancement of responsiveness and reliability of SDN for remote 3D printing and control processes.