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The internet’s future architecture, known as Named Data Networking (NDN), is a creative way to offer content-based services. NDN is more appropriate for content distribution because of its special characteristics, such as naming conventions for packets and methods for in-network caching. Mobility is one of the main study areas for this innovative internet architecture. The software-defined networking (SDN) method, which is employed to provide mobility management in NDN, is one of the feasible strategies. Decoupling the network control plane from the data plane creates an improved programmable platform and makes it possible for outside applications to specify how a network behaves. The SDN is a straightforward and scalable network due to its key characteristics, including programmability, flexibility, and decentralized control. To address the problem of consumer mobility, we proposed an efficient SDPCACM (software-defined proactive caching architecture for consumer mobility) in NDN that extends the SDN model to allow mobility control for the NDN architecture (NDNA), through which the MC (mobile consumer) receives the data proactively after handover while the MC is moving. When an MC is watching a real-time video in a state of mobility and changing their position from one attachment point to another, the controllers in the SDN preserve the network layout and topology as well as link metrics to transfer updated routes with the occurrence of the handoff or handover scenario, and through the proactive caching mechanism, the previous access router proactively sends the desired packets to the new connected routers. Furthermore, the intra-domain and inter-domain handover processing situations in the SDPCACM for NDNA are described here in detail. Moreover, we conduct a simulation of the proposed SDPCACM for NDN that offers an illustrative methodology and parameter configuration for virtual machines (VMs), OpenFlow switches, and an ODL controller. The simulation result demonstrates that the proposed scheme has significant improvements in terms of CPU usage, reduced delay time, jitter, throughput, and packet loss ratio.

The Software Defined Networking (SDN) paradigm decouples the network control functions from the data plane and offers a set of software components for flexible and controlled management of networks. SDN has promised to provide numerous benefits in terms of on-demand provisioning, automated load balancing, streamlining physical infrastructure, and flexibility in scaling network resources. In order to realize these network service offerings, there is an important need for developing an efficient, robust, and secure execution platform. As a primary contribution, we present a novel virtual execution platform for the OpenFlow controller using Network Function Virtualization (NFV). Theoretically, NFV can apply to any network function, which can simplify the managing of the heterogeneous data plane. The characteristics of our proposed architecture include pipe-lined processing of network traffic, virtualized and replicated execution of network functions, isolation between task nodes, and random mapping of traffic to task nodes. The proposed architecture has two major components: a Network Packet Schedulers (NPS) and a Task Engine (TE). The TE consists of Task Nodes (TNs) which are responsible for executing different network functions on various traffic flows and each TN is realized as a virtual machine. Upon receiving traffic from the data plane, NPS analyses the functional requirements of the traffic and different controller performance parameters. Then it allocates the traffic to appropriate TNs for executing necessary network functions. In this respect, it provides performance benefits, robustness, fine-grained modularity, and strong isolation security in the processing of traffic flows on the SDN platform. Efficacy of our proposed architecture has been demonstrated with a case study.

The availability of Software Defined Network’s (SDNs) flow rule entry in the flow table is considered a key factor in the timely delivery of a certain flow. The controller is approached for instructions on how to deal with the flow when the rule for such flow is missing. The controller then updates the flow table accordingly at the switch so that flow could be dealt with locally. It becomes problematic when no rule is defined yet at the controller by the application plane. In most of these cases, such a situation is handled by programming the controller with wildcard rules. However, handling many flows at once under wildcard rules severely hampers the network performance. Flow rules formation by the application plane is sometimes critical and time-consuming which increases the latency ratio by creating a bottleneck at the switch level. To avoid the bottlenecks due to rule absence, in this paper, rather than waiting for the application plane’s response and putting the pending traffic flows in the buffer, which may be dropped, the controller is programmed in a way that has the built-in mechanism of self-flow rule formation. This atomized mechanism is based on the previously available traces of the same flows when they were forwarded on the network using the wildcard rules. To assess the performance of the proposed work, it is emulated and benchmarked with the latest research. The results show considerable performance achievement.

Cloud Computing provides an auto-scaling feature for dynamic resource utilization to cope with their customers’ requirements and charge as ‘pay-per-use’. Attackers get the benefit of this auto-scaling feature by flooding DDoS attacks on the VMs or instances in the cloud environments. Such DDoS attacks give financial damages to the customers because of massive amount of resource utilization, known as the Economic Denial of Sustainability (EDOS) attack. Transmission Control Protocol (TCP) SYN flooding attack is known to be the most challenging attack resulting EDOS attack. Software Defined Network (SDN) being a cost-efficient solution for cloud service providers, uses the OpenFlow switches and flow tables to make rules for each incoming flow. In SDN, it is feasible to classify an incoming flow as an attack and block forwarding of this flow to the targeted VM. This research work proposes an SDN based fast and computationally cost-efficient statistical anomaly detection model, named EDOS-TCP SYN mitigation model (EDOS-TSM) to mitigate TCP SYN flooding attack from a single user and spoofed IPs. EDOS-TSM uses binomial probability, TTL field value of IP packet header and multi-TCP SYN requests to detect source-based and spoofing based attacks. The proposed model is implemented, and the performance is evaluated on an OpenStack production-based cloud with real-time traffic and attack generation. The attack mitigation results are compared with existing models in the literature. The results show a large number of false negatives in existing models and efficiency of EDOS-TSM is proved in both source based and spoofing attacks.

Low-rate Denial of Service (LDoS) attacks cause severe destructiveness to network security. Consequently, the implementation of detection and defense against them is a concern among the research communities. But it is formidable to deploy extension modules to detect and mitigate attacks online in traditional networks, because devices are deficient of flexibility and scalability. To address the problem, we design and implement an online attack detection and mitigation system (ADMS) framework via the scalable and programmable Software Defined Networking (SDN). ADMS is installed on SDN controllers and conforms to the OpenFlow policy without extra devices. ADMS consists of two modules: the two-phase detection module and the mitigation module. The two-phase detection module combines the new port traffic feature and the Lightgbm classifier based on flow table statistics traffic to precisely detect LDoS attacks. The mitigation module utilizes the novel Sequence Matching based Dynamic Series Analysing (SMDSA) algorithm to locate the attacker, and efficiently mitigates attack traffic by packet filter. The SMDSA algorithm distinguishes the victim port from benign ports by calculating the anomaly score of each port. Our evaluation on a prototype implementation of ADMS shows that the framework is able to precisely identify and efficiently mitigate LDoS attacks in real-time.

The quality of service (QoS) in networking is the process of managing network resources to reduce packet loss and to lower network jitter and latency. QoS has been widely used in traditional network and can also be implemented in the 5G standard based on a software-defined network (SDN). A traditional network carries several challenges, such as vendor dependency, the complexity of managing a large network, dynamically changing forwarding policies, and more. Software-defined networking is a new networking strategy designed to address the challenges of a traditional IP network, such as high levels of complexity and inability to adapt to the new quality of service requirements in a timely manner. The fundamental idea behind SDNs compared to the conventional networking paradigm is the creation of horizontally integrated systems through the separation of the control and the data plane while providing an increasingly sophisticated set of abstractions. Recently, various SDN-enabled QoS frameworks have emerged that offer many possibilities for network reconfiguration and high-level definition of policies. QoS requirements for 5G networks have been defined on the basis of three main categories of use cases: extreme mobile broadband (xMBB), massive machine type communications (mMTC) IoT/M2M devices, and highly reliable М2М-communication (ultra-reliable machine-type communications – uMTC). This paper analyzes and surveys the QoS based on the openflow protocol method and QoS based on open-source SDN controllers method in 5G network. In addition, we discuss various architectural issues of open-source SDN controllers network and examine their impact on the QoS. Furthermore, we outline the characteristics of the QoS parameters such as latency, availability, reliability, jitter, and bandwidth in the 5G network. Finally, the article discusses and compares parameters of the QoS in 5G determined by world’s leaders in 5G technology.

Software Defined Networking (SDN) is a new networking paradigm where forwarding hardware is decoupled from control decisions. It promises to dramatically simplify network management and enable innovation and evolution. In SDN, network intelligence is logically centralized in software-based controllers (the control plane), while network devices (OpenFlow Switches) become simple packet-forwarding devices (the data plane) that can be programmed via an open interface (OpenFlow protocol). Such decoupling of the control plane from the data plane introduces various challenges that include security, reliability, load balancing, and traffic engineering. Dreadful security challenges in SDNs are denial of service (DoS) and distributed denial of service (DDoS) attacks. For instance, in SDNs, DoS/DDoS attacks could flood the control plane, the data plane, or the communication channel. Attacking the control plane could result in failure of the entire network, while attacking the data plane or the communication channel results in packet drop and network unavailability. In this paper we deliver several contributions that shed light on the field of DoS/DDoS attacks in SDNs, providing a complete background about the area, including attacks and analysis of the existing solutions. In particular, our contributions can be summarized as follow: we review and systematize the state-of-the-art solutions that address both DoS and DDoS attacks in SDNs through the lenses of intrinsic and extrinsic approaches. Moreover, the discussed countermeasures are organized accordingly to their focus, be it on detection, mitigation, prevention, or graceful degradation. Further, we survey the different approaches and tools adopted to implement the revised solutions. Finally, we also highlight possible future research directions to address DoS/DDoS attacks in SDNs.

In recent years, the huge expansion of Datacenters (DC) to execute billions of end-user applications in real-time leads to a large amount of energy consumption across the globe. So, the traditional TCP/IP-based networks which are being used for DC inter-connections are facing challenges of managing stringent Quality-of-Service (QoS) requirements of different applications of the end-users and service providers. Moreover, the existing solutions rely on distributed architecture and do not scale for large scale data centers. The issue of high power consumption at DC arises with the increase in the number of nodes and links in the network. Also, it becomes problematic on the DC whenever the underlying network resources (switches and routers) are not efficiently utilized at the time of peak data traffic resulting in high operational cost of energy utilization. However, Software-Defined Networking (SDN) emerges as one of the leading technologies to address the aforementioned issues using the programmable switches and controllers. Inspired from these facts, in this paper, we have formulated the Energy-Aware Routing (EAR) problem of DCs as a Mixed Integer Non-Linear Programming (MINLP) for which an Energy-Efficient Fast Flow Forwarding (EnFlow) scheme is designed. The EnFlow scheme uses the power-saving mode of the network to solve the EAR problem. It has three modules namely– priority scheduling , routing , and re-routing . The first module works according to the First-in-First-Out Push Out Priority (FIFO-POP) scheduling using the multiple OpenFlow switches. The FIFO-POP is designed to save the energy usage of multiple switches by reducing the average waiting time of incoming packets in the queue buffers. The second module is based upon an efficient flow re-routing for a new node and link adaptation to provide the maximum bandwidth to the wired links. The third module is based upon the meta-heuristic Ant Colony Routing (ACR) to execute the stochastic decision policy on the network controller for computation of the shortest path of the forwarding nodes. The proposed EnFlow scheme is simulated using the data traces of 34 cities of NorthAmerica zone with Omnet++ 5.1 using various performance evaluation metrics. The results obtained demonstrated that the proposed EnFlow scheme is 24.55% and 71.15% more energy-efficient in comparison to the RE-FPR and ILP-EAR schemes. Also, it consumes 9.72% and 40.83% lower energy in comparison to the FFHA and EXR schemes respectively.

Software Defined Networking (SDN) has developed tremendously in the recent years. This paper addresses different flow installation mechanisms. The idea of a programmable network which is the foundation of SDN ensures dynamic network management by separating the control plane from data and management planes. In SDN the control plane resides in SDN Controller, which is responsible for the installation of flows on the data plane. Whenever a flow enters into an SDN switch, flow tables are generated for those flows and installed at data plane from the control plane. These flows have their own properties because of their relevant protocols which are attached to them. A flow consists of a matching pattern, actions, and counter. Based on the respective flow, data communication takes place. This paper investigates three kinds of flow installation mechanisms, reactive, proactive and hybrid. Based on these three flow installation mechanisms, we have analysed the performance of SDN by using different performance metrics. We have used the Mininet and POX controller to evaluate the performance analysis in Ubuntu and Windows-based environments. Finally, we have drawn conclusions

We expose the state of the art in the topic of one-way delay measurement in both traditional and software-defined networks. A representative range of standard mechanisms and recent research works, including OpenFlow and Programming Protocol-independent Packet Processors (P4)-based schemes, are covered. We classify them, discuss their advantages and drawbacks, and compare them according to their application environment, accuracy, cost, and robustness. The discussion extends to the reuse of traditional schemes in software-defined networks and the benefits and limitations of the latter with respect to reducing the overhead of network wide measurements. We conclude with a summary of learned lessons and open challenges for future work.

The Internet of Things (IoT) is a key developing innovation aimed at linking objects via the Internet. While, Software Defined Networking (SDN) is another modern network- ing domain intelligence innovation that increases network effi- ciency and enhances security, reliability, and protection through dynamic software programs. In this paper, we proposed a distributed secure SDoT-NFV architecture for smart cities with Network Function Virtualization (NFV) implementation. We integrated highly protected SDN that delivers better network ef- ficiency, protection, and privacy results. It also secures metadata within each layer as well as payload. In addition, this architecture attempted to implement a more efficient method for constructing a cluster via SDN. Moreover, SDN-IoT with the NFV ideas brings benefits in terms of energy conservation and load balancing to the relevant fields. In addition, several distributed controllers have suggested enhancing accessibility, integrity, anonymity, con- fidentiality, and so on. We also implemented an energy-efficient Cluster Head Selection (CHS) algorithm to make use of our proposed architecture. The network offers greater protection of each network layer as opposed to the traditional network in the proposed architecture. Lastly, we analyze the efficiency of the proposed architecture with different network parameters (throughput, RTT, and Time sequence) for smart cities. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 27-35

2In Software Defined Network based Internet of Things (SDN-IoT), the path selected for forwarding the packets as per rules, should be Quality of Service (QoS) effective. Since SDN based IoT involves dynamic heterogeneous flows, handling caching in terms of rules becomes challenging. Hence in this paper, we propose QoS aware Optimum Path Selection and Rule Caching Policy (QOPS-RCP) for SDN based IoT. In this technique, the optimum path is determined that satisfies the QoS constraints: Rule Caching (RC) cost, packet loss probability and delay. A RC policy is designed that matches the requirements of SDN based IoT architecture such that the cost of RC is minimized. The proposed research methodology is implemented in OpenFlow module and the performance is evaluated in terms of throughput and end-to-end delay.

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

Проблематика. Принципи побудови і функціонування мереж SDN суттєво відрізняються від традиційних телекомунікаційних мереж. Для визначення чисельних значень показників функціонування такого типу мереж та перевірки їх відповідності вимогам, консорціумом OpenNetworkingFoundation розроблений мережевий емулятор Mininet. Елементи мережевого емулятора є у відкритому доступі. Однак, побудова імітаційної моделі і порядок визначення показників функціонування мережі SDN з використанням Mininet має ряд особливостей,які розглянуті в роботі. Мета дослідження. Створення імітаційної моделі на базі мережевого емулятору Mininet і визначення показників функціонування мереж SDN різної структури. Методика реалізації. Визначення показників функціонування мережі SDN у режимі перевантаження напрямків зв'язку. Результати дослідження. Проведено моделювання мереж SDN складної структури і визначені наступні показники функціонування: RTT (RoundTripTime) для кожного напрямку зв'язку, пропускна здатність гілок і напрямків зв'язку, величина затримки на мережевих елементах, завантаження портів OpenFlowSwitch, елементи мережі з найбільшою затримкою, число обслугованих і втрачених пакетів. Висновки. Використання мережевого емулятора Mininet є досить зручним інструментом для визначення показників функціонування мереж SDN. Однак, є і проблеми. По-перше, налаштування працездатності системи вимагає написання програм для забезпечення взаємодії стандартних елементів з бібліотеками Mininet. По-друге, при виникненні перевантажень в напрямках зв'язку, FloodlightController не забезпечує автоматичного балансування трафіку по обхідним шляхам. Цю задачу доводиться вирішувати в ручному режимі шляхом внесення змін до таблиці маршрутизаціїOpenFlowSwitch. Background. The implementation of the new modern services on existing networks requires replacement or modernization of the old equipment. This negatively affects the efficiency of providing users with new types of services and their cost. Therefore, the issues/tasks of implementing SDN technology in the construction of networks are in the center of attention of representatives of science-research organizations and telecom operators. SDN networks have the advantages of flexible scaling of the system without replacing existing server and network equipment, eliminating operator dependence on specific vendor solutions, and rapid implementation of the new network technologies and services. To determine the numerical values of indicators of the functioning of SDN networks and check their compliance with the requirements, it is proposed to use in working process simulation modelling. For these goals, the Open Networking Foundation consortium developed the Mininet network emulator, the elements of that are published publicly. The formation of the simulation model and the procedure for determining the performance indicators of the SDN network that uses Mininet has a number of features that are considered in the work. Objective. The purpose of the article is to build a simulation model based on the Mininet network emulator and to determine the performance indicators of SDN networks of various structures/topologies. Methods. Creation of a SDN network segment for testing process of its functioning in the overload mode of various communication routes. Results. The Mininet network emulator allows simulating SDN networks of a rather complex structure, change the performance of network branches and the amount of load in communication routes, to create a network-wide overload mode. During the simulation, it is possible to determine a number of indicators of network performance, such as the RTT (Round Trip Time) parameter for each route of communication, the bandwidth of branches and routes of communication, the amount of delay on network elements, loading of OpenFlow Switch ports/interfaces, network elements with the highest delay, the number of served and lost packets. Conclusions. Use of the Mininet network emulator is a fairly convenient tool for determining the performance indicators of SDN networks. However, there are also some problems. First of all, setting up the system's operability requires writing programs to ensure the interaction among standard elements from the Mininet library. Secondone, is when congestion occurs in the communication routes, the Floodlight Controller does not automatically balance traffic along the work-around route. This task has to be solved manually by making changes to the OpenFlow Switch routing tables.

Software defined networks (SDN) creates an environment for designing customized networks based on consumer needs. SDN can effectively apply the decoupling methods on the forwarding plane and the control plane. The SDN controller will forward the traffic information to northbound API and controller obtain the traffic information from openflow virtual switch. SDN routes data and control packets to their destinations based on flow labels, but it lacks protection features to prevent malicious traffic. The SDN environment faces denial of service (DoS) attacks and distribute denial of service attacks, it leads to service unavailability. This paper is mainly focused on dynamically configuring the firewall to identify DoS attacks and decrease malevolent traffic. Virtual networks are simulated using mininet with dynamic access control list rules, and the result show that the suggested solution has improved attack alleviation time bins.

In order to support a variety of Internet of Things (IoT) and smart city applications, it is necessary to provide computing and networking resources at cloud, fog and edge levels. Fortunately, evolution of Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies are greatly supporting operators to deploy their data centers at reduced expenditures by integrating cloud, fog and edge (Cloud-Fog-Edge) platforms. Although Cloud-Fog-Edge environments provide economic platforms, due to their multi-tenant sharing platforms customer applications could face a variety of security issues in terms of networking and computing resources. For instance, in cloud environments Intrusion Detection Systems (IDS) and authentication mechanisms are useful for enforcing security policies and improving operational security, but internal malicious users can do Address Resolution Protocol (ARP) spoofing attacks by exploiting shared networking environments. Mainly, ARP spoofing attacks could lead to VLAN-ID spoofing, Denial of Service (DoS) and distributed DoS (DDoS), Man in the Middle (MITM) and session hijack attacks in the network. In this work we are proposing a Denial of ARP Spoofing (D-ARPSpoof) approach to prevent ARP spoofing in SDN and NFV enabled Cloud-Fog-Edge platforms. Unlike existing IDS and anomaly detection systems, D-ARPSpoof prevents ARP spoofing attacks by reducing overhead towards the centralized controllers and OpenFlow switches. In this work, D-ARPSpoof performance is compared with recent ARP spoofing mitigation approaches and anomaly detection systems using important metrics like number of successful connections, controller processing messages overhead and number of flow rules installed in OpenFlow switches. In results, we found that in comparison with existing approaches D-ARPSpoof successfully prevents all malicious connections at reduced overhead towards controllers and switches.

OpenFlow discovery protocol (OFDP) is the standard protocol for discovering network topologies for OpenFlow. Each controller generates and receives OFDP messages for each switch to yield a network topology. However, for a network with a large number of switches, the controller may suffer from high overhead to perform OFDP even though the network topology remains unchanged. In this article, we improve the efficiency of network topology discovery by implementing a delegated function in OpenFlow switches. Our mechanism can reduce controller’s overhead for topology discovery to shorten the time of discovering a network topology. It also provides with the interoperability with switches which only perform OFDP. The experimental results demonstrate that the proposed scheme can improve the time for discovering a new network topology by decreasing the number of messages. Our scheme also eliminates unnecessary Packet-Out and Packet-In messages when the network topology remains unchanged. As a result, the controller overhead, including CPU usage and traffic load, is greatly reduced to shorten the response latency of controllers. Hence, the proposed scheme improves the feasibility of achieving scalable OpenFlow networking.

Network traffic monitoring is vital for enhancing the overall network performance and for optimizing the traffic flows. However, an emerging growth of use in cloud services, internet-of-things, block-chain and data analytics, demand the hardware-based-network-controller to provide more features for expanding network architecture. Therefore, Software Defined Networking (SDN) offers a new solution in terms of scalability, usability and programmable software-based-network-controller for the legacy network infrastructure. In fact, SDN provides a dynamic platform for the network traffic monitoring using international standard. In this study, SDN setup and installation method uses a Mininet emulator containing a controller Ryu with switching hub component, OpenFlow switches, and nodes. The number of nodes is adding until reaches to 16 nodes and evaluated through different network scenarios (single, linear and tree topology). Findings show that the single topology gives a winning criterion compared to other topologies. SDN implementation is measured with performance parameters such as Throughput, Jitter, Bandwidth and Round-Trip Time between scenarios using the Ryu controller. Future research explores on the performance of SDN in larger network and investigates the efficiency and effectiveness of SDN implementation in mesh topology.

Software Defined Networks (SDN) concept reduces the complexity of the traditional network. The decentralization of controlling issues of the traditional networks in SDN increases the popularity of ...

Software-Defined Networking (SDN) continues to be deployed spanning from enterprise data centers to cloud computing with the proliferation of various SDN-enabled hardware switches and dynamic control plane applications. However, state-of-the-art SDN-enabled hardware switches have rather limited downlink message processing capability, especially for Flow-Mod and Statistic Query , which may not suffice the huge need of dynamic control plane applications. In this paper, we systematically study the interactions between the control plane applications and the data plane switches, and present two new attacks, namely Control Plane Reflection Attacks, to exploit the limited processing capability of SDN-enabled hardware switches. The reflection attacks adopt direct and indirect data plane events to force the control plane to issue massive expensive downlink messages towards SDN switches. Moreover, we propose a two-phase probing-triggering attack strategy, which makes the reflection attacks much more efficient and powerful. Experiments on a testbed with 3 different physical OpenFlow switches demonstrate that the attacks can lead to catastrophic results such as hurting the establishment of new flows and even disruption of connection between SDN controller and switches. To mitigate such attacks, we present several countermeasures from different perspectives. In particular, we propose a novel, systematical defense framework, SwitchGuard, to detect anomalies of downlink messages and prioritize these messages based on a novel monitoring granularity, i.e., host-application pair (HAP). Implementations and evaluations demonstrate that SwitchGuard can effectively reduce the latency for legitimate hosts and applications under the control plane reflection attacks with only minor overheads.

It is widely recognized that connected vehicles have the potential to further improve the road safety, transportation intelligence and enhance the in-vehicle entertainment. By leveraging the 5G enabled Vehicular Ad hoc NETworks (VANET) technology, which is referred to as 5G-VANET, a flexible software-defined communication can be achieved with ultra-high reliability, low latency, and high capacity. Many enabling applications in 5G-VANET rely on sharing mobile data among vehicles, which is still a challenging issue due to the extremely large data volume and the prohibitive cost of transmitting such data using 5G cellular networks. This article focuses on efficient cooperative data sharing in edge computing assisted 5G-VANET. First, to enable efficient cooperation between cellular communication and Dedicated Short-Range Communication (DSRC), we first propose a software-defined cooperative data sharing architecture in 5G-VANET. The cellular link allows the communications between OpenFlow enabled vehicles and the Controller to collect contextual information, while the DSRC serves as the data plane, enabling cooperative data sharing among adjacent vehicles. Second, we propose a graph theory based algorithm to efficiently solve the data sharing problem, which is formulated as a maximum weighted independent set problem on the constructed conflict graph. Specifically, considering the continuous data sharing, we propose a balanced greedy algorithm, which can make the content distribution more balanced. Furthermore, due to the fixed amount of computing resources allocated to this software-defined cooperative data sharing service, we propose an integer linear programming based decomposition algorithm to make full use of the computing resources. Extensive simulations in NS3 and SUMO demonstrate the superiority and scalability of the proposed software-defined architecture and cooperative data sharing algorithms.

Software Defined Networking (SDN) is a promising technology that provides flexibility, programmability, and network automation by shifting the network intelligence to the logically centralized controller. In SDN architecture, the controller maintains the global view of network topology; therefore the network management is efficient as compared to the traditional networks. Moreover, the cost of SDN devices is less due to the use of open source network operating system instead of proprietary and vendor-specific software. In spite of the flexibility offered by the SDN, OpenFlow enabled switches have a fixed behavior as specified in the datasheet of switch ASIC. They recognize fixed set of header fields according to the support of OpenFlow version. In addition, SDN is suffering from scalability and performance issues because SDN switches heavily dependent on the control plane to forward the packets which increases the data-control communication overhead. To resolve these issues, P4-Programmable data plane switches can be used. The analysis of review articles about SDN suggests insufficient focus on the data plane programmability. Therefore, this paper provides the comprehensive overview of domain-specific language (P4) for the programmability of the data plane. We have discussed the problems in the traditional SDN architecture and then, how these problems can be managed by the data plane programmability. Further, this study critically analyze the research articles based on the P4 programming language for network traffic monitoring, DDoS attack detection, load balancing, and packet aggregation and disaggregation. Finally, we have identified the research gaps and highlighted the open research challenges in the field of data plane programmability for the future directions.

Software-Defined Networking (SDN) is a growing network technology that has brought significant benefits to a wide range of disciplines, from science to technology to various fields. This structure can be used in network-based environments, data centers and various research sites. OpenFlow is one of the most widely used protocols for interaction between a controller and a switch in a Software-Defined Networking. Understanding the performance and limitations of the network defined by open source software, including bottlenecks and security vulnerabilities due to the centralized network structure, are important prerequisites for the efficient deployment of these systems. These points of view led researchers to examine various related mathematical models to address these issues. Queueing theory provides the most important and accurate model for evaluating the performance of the SDN networks affected by these restrictions, which has attracted the attention of researchers recently. Regarding to extensive mathematical modeling, this theory has also been used to improve network efficiency. Researchers have used this theory to investigate operational power and improve time consumption and control of data planes due to the nature of classification and storage of packets in SDN buffers. These methods overcome controller performance bottlenecks and increase SDN control capacity, especially for large distributed networks. In this paper, we examine the queueing models for different applications in different layers of SDN, in which researchers use these methods to monitor network loads, evaluate and predict performance changes due to diversity in network traffic. We introduce and review a collection of articles that explore, different applications of Queueing theory in SDN networks. In addition, in order to increase the efficiency of this research, detailed comparisons are performed in terms of structure, mathematical models and the final simulation results.

Network resource management is one of the key technologies needed to ensure that multiple applications in edge networks provide reliable and stable performance. Although throughput has previously been seen as the primary network performance metric, recent applications do not focus on throughput alone. Instead, Quality of Experience (QoE) is attracting significant attention as an indicator of network resource management performance because it allows a wide variety of applications to be compared within a single metric. In this study, we tackle QoE measurements for a video streaming service as a way to evaluate QoE-based network management. However, there are several problems related to measuring QoE. For example, in-network components are difficult to measure because QoE is normally measured at end-points, and several properties that are deeply related to application settings are required for those calculations. Additionally, the measurements set forth in the International Telecommunication Union’s ITU-T G.1071 standard require a certain duration, which is too long for network resource management evaluations. Therefore, this paper proposes a two-staged in-network QoE estimation method for video flows that can resolve these issues. In the first stage, we focus on producing a fast and rough QoE estimate to start forwarding the arriving flow onto an appropriate route as soon as possible. Next, the second stage is designed to produce precise QoE estimations based on careful long-duration measurements. In both stages, the proposed method uses a parameter estimation process that converts in-network information to end-point information for QoE calculations by following ITU-T G. 1071 and corrects measurement errors reducing QoE calculation errors to the greatest extent possible. Through experimental evaluations, we then demonstrate that the QoEs of all flows can be maximized by selecting appropriate routes based on the predicted QoE at the first stage, and that the accuracy of the QoE estimation at the second stage can be improved in real-time even when packet losses occur.

SDN enables a new networking paradigm probable to improve system efficiency where complex networks are easily managed and controlled. SDN allows network virtualization and advance programmability for customizing the behaviour of networking devices with user defined features even at run time. SDN separates network control and data planes. Intelligently controlled network management and operation, such that routing is eliminated from forwarding elements (switches) while shifting the routing logic in a centralized module named SDN Controller. Mininet is Linux based network emulator which is cost effective for implementing SDN having in built support of OpenFlow switches. This paper presents practical implementation of Mininet with ns-3 using Wi-Fi. Previous results reported in literature were limited upto 512 nodes in Mininet. Tests are conducted in Mininet by varying number of nodes in two distinct scenarios based on scalability and resource capabilities of the host system. We presented a low cost and reliable method allowing scalability with authenticity of results in real time environment. Simulation results show a marked improvement in time required for creating a topology designed for 3 nodes with powerful resources i.e. only 0.077 sec and 4.512 sec with limited resources, however with 2047 nodes required time is 1623.547 sec for powerful resources and 4615.115 sec with less capable resources respectively.

© Copyright 2021, A. O. Nyanteh et al. Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end–to–end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end- to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.

Scalable Video Coding (SVC) is a promising coding technique that enables flexible video transmission to support heterogeneous devices. However, the current best-effort delivery model characterized by the Internet cannot fully exploit the scalability feature of SVC because the network nodes are transparent for multimedia streaming applications. Software-defined networking (SDN) has emerged as an innovative network paradigm that decouples the control and forwarding planes while routing. This architecture with the OpenFlow protocol enables network operators to obtain per-flow QoS control in a more scalable, flexible, and finely granular manner compared to the conventional network architecture. Inspired by these facts, this paper designs a comprehensive QoS-aware multicast scheme for scalable video streaming over SDN networks. First, we present the designed multimedia streaming multicast system architecture and formulate the QoS-aware multicast routing problem as a non-linear programming model. We then propose a fundamental tree construction algorithm that decomposes the video streaming requests into subrequests and serves them in a bottom-up manner. Furthermore, we design a layer switching strategy based on the video distortion model to mitigate network-induced video quality distortion. Finally, the experimental results are presented to validate the performance of our methods in terms of scalability, network utility, video playout quality, and playback interruption ratio.

Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91–99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.

Smart city is a transformative and progressive vision that aims to revolutionize infrastructure systems and public services in an urban area with modern information technologies. Its ultimate goal is to greatly improve the livability Quality of Service (QoS) of its citizens and to optimize the utilization of its assets and natural resources sustainably. One of the key technical attributes in smart cities is to deploy a large number of sensors to collect data to enable real-time and intelligent decisions for various city functions and citizen needs. Many of the data have strict security requirements as they are either private to citizens or sensitive to critical infrastructures. As a result, how to securely and efficiently deliver and process the dramatically increasing volume of data becomes one of the grand challenges in materializing the smart city vision. In recent years, Software-Defined Networking (SDN) has emerged as a leading communication infrastructure candidate for smart cities. While many efforts have existed to research, prototype, and even deploy SDN on a small scale for some smart city applications, there is still a lack of cohesive understanding about SDN’s impact on the secure communication need of smart cities. In this paper, we conduct a comprehensive survey of the core functionality of SDN from the perspective of secure communication infrastructure at different scales. A specific focus is put on the security threats and challenges in accordance with SDN plane-based architectures for various smart city-enabled applications. We further systematically categorize the state-of-art solutions and proposals to apply SDN to support typical smart city applications, such as transportation, health, and energy applications. Lastly, we cast a holistic view of future research trends.

Nowadays, the increase in the number of mobile users and cellular traffic leads to new challenges in the fifth-generation (5G) of cellular networks. The increase in the demand for high data rates brings challenges like scalability and flexibility in the 5G network. Software-defined networking (SDN) is a network paradigm that separates the control plane and data plane in the network and ease the management of the network. In this work, an SDN based 5G core architecture is proposed, in order to introduce flexibility and ease of management in the network. Another benefit of using SDN is to make the network vendor-independent. Furthermore, the explanation of initial attachment and handover procedures in the proposed architecture is provided. A network simulator is built to evaluate the performance of proposed architecture, in terms of end-to-end delay, throughput and resource utilization of controller, under different network factors. A performance comparison, in terms of end-to-end delay, between proposed SDN based 5G architecture and traditional 5G architecture is provided. Results show that the proposed architecture provides 18% to 62% less end-to-end delay, under different factors for different procedures, compared to the traditional 5G architecture. A comparison with previous works is also provided, which indicates similar trends in delay between our work and previous studies.

Core wavelength division multiplexed (WDM) networks are widely used to provide fixed physical connectivity and bandwidth to the logically connected upper electronic layer devices using optical signals. However, growing demands for bandwidth-intensive applications and cloud-based services push optical networks carriers’ to provide scalable and flexible services dynamically. Software defined networking (SDN) has the potential to program electronic layers by dynamically controlling and managing network resources using SDN controller applications. SDN’s on-demand characteristics combined with the optical circuit-switching can enable optical network service providers to customize their service provisioning dynamically to the user’s requirements. They enable fast provision of new services, and minimize under-utilization of resources. In this paper, a model is proposed to bring the dynamic allocation of resources which is a layer 2+ functionality, to the WDM layer using SDN. A middle-ware application based on SDN and OpenFlow for dynamic switching and provisioning of optical service is presented. The application abstracts the optical layer’s connectivity, also accounting for the switching constraints. Details of the model’s implementation are discussed considering classically used equipment and its performance in terms of CPU and memory utilization, topology emulation time, and latency is evaluated. Finally, the application is tested with a Cisco layer one switch. Performance results show that the latency doubles when increasing the number of fibers of an optical cross connect from 5 to 7 and keeping wavelengths equal to 8, with Clos fabric topology.

The SDN controller uses the OpenFlow Discovery Protocol (OFDP) to collect network topology status. OFDP detects the link between OpenFlow switches by generating Link Layer Discovery Protocol (LLDP) packets. However, OFDP is not a completely secure protocol and can be used by attackers to perform topology discovery injection attacks, topology discovery man-in-the-middle attacks, and topology discovery flood attacks, thereby confusing the network topology. This paper proposes a Correlation-based Topology Anomaly Detection (CTAD) mechanism to run in a software-defined network controller. Spearman’s rank correlation is used to analyze the correlation between network traffic between links and measure the time difference between the round trip time of each LLDP frame to determine whether the topology man-in-the-middle attack exists in the network. This paper also adds a dynamic authentication key and counting mechanism in the LLDP frame to prevent attackers from using the topology discovery injection attack to generate fake links and topology discovery flooding attacks, causing network routing or switching abnormalities.

Software-defined networking (SDN) is an emerging technology of efficiently controlling and managing computer networks, such as in data centres, wide-area networks, as well as in ubiquitous communication. In this study, the authors explore the idea of embedding the SDN components, represented by SDN controller and virtual switch, in end-hosts to improve network performance. In particular, the authors consider load balancing across multiple network interfaces on end-hosts with different link capacity scenarios. The authors have explored and implemented different SDN-based load-balancing approaches based on OpenFlow software switches, and have demonstrated the feasibility and the potential of this approach. The proposed system has been evaluated with MultiPath transmission control protocol (MPTCP). The proposed results demonstrated the potential of applying the SDN concepts on multi-homed devices resulting in an increase in achieved throughput of 55% compared to the legacy single network approach and 10% compared to the MPTCP.

Recent developments in networking include the interest in software‐defined networking, in which OpenFlow switches are in charge of forwarding traffic by matching incoming packets with forwarding rules in their flow tables. Flow tables are commonly implemented in ternary content addressable memories (TCAMs). Typically, this type of memory tends to be extremely expensive and to be power‐hungry devices. The price and power dissipation of TCAM depend on the capacity of the memory. A blind design of memory size can result in dramatic degradation in performance and power efficiency of the entire network. In this study, the authors clarify this impact and show how the optimum value of TCAM capacity is attainable. They also propose a power consumption model for TCAM‐based OpenFlow switches. Simulation results show that the proposed solution reduces the average power consumption by 24% compared to the minimum link on method and by 11.5% compared to the minimum node on (MNO) method and thus power efficiency is better achieved when each memory is supplied by appropriate TCAM size.

To ensure the quality of service of an end-to-end connection, current network solutions are mostly dependable on the differentiation between different classes of traffic. The Software-defined networking (SDN) architecture has emerged to offer network programmability, giving to network operators a programmatic control over their network. In SDN, network devices are programmed in many ways, having a standard, open, and vendor-agnostic interface, e.g., OpenFlow, enabling the control plane to instruct the forwarding behavior of network devices from different vendors. In this paper, we introduce the Programmable Labels (ProgLab) approach to support traffic differentiation with QoS guarantees as a low-cost alternative built over an SDN architecture. The idea relies on the simplification of a packet-forwarding operation which relies on the remainder of a division, instead of classical table lookup method. ProgLab computes programmable label at the control plane by solving a congruence system from Residue Number System and the co-prime numbers assigned to the switches in the path of an end-to-end connection. Such label has a meaning within this network that expresses the entire route, addressing the respective traffic class at each switch’s logical queue along the path. ProgLab approach has been implemented through the P4 language and evaluated through an emulation-based evaluation. The experiments demonstrated the feasibility of ProgLab and showed its ability in providing QoS differentiation on demand.

OpenFlow is considered as the most known protocol for Software Defined Networking (SDN). The main drawback of OpenFlow is the lack of support of new header definitions, which is required by network operators to apply new packet encapsulations. While SDN’s logically centralized control plane could enhance network security by providing global visibility of the network state, it still has many side effects. The intelligent controllers that orchestrate the dumb switches are overloaded and become prone to failure. Delegating some level of control logic to the edge or, to be precise, the switches can offload the controllers from local state based decisions that do not require global network wide knowledge. Thus, this paper, to the best of our knowledge, is the first to propose the delegation of typical security functions from specialized middleboxes to the data plane. We leverage the opportunities offered by programming protocol-independent packet processors (P4) language to present two authentication techniques to assure that only legitimate nodes are able to access the network. The first technique is the port knocking and the second technique is the One-Time Password. Our experimental results indicate that our proposed techniques improve the network overall availability by offloading the controller as well as reducing the traffic in the network without noticeable negative impact on switches’ performance.

Software-defined networking (SDN) is an emerging technology, which provides network architecture that decouples the control plane from the data plane. Due to the centralized control, the network becomes more dynamic, and the network resources are managed in a more efficient and cost-effective manner. The centralization of the control plane requires robust and real-time security techniques. The security Techniques will protect it from any sign of vulnerabilities associated with the network such as a distributed denial of service (DDoS) attacks. The problem of the data-plane is that the attack is hard to be tracked by the SDN controlling plane. This makes the switches to be more susceptible against these types of attacks and hence it is very important to have quick provisional methods in place to prevent the switches from breaking down as soon as first signs of an attack are detected. To resolve this problem, the research developed a mechanism that detects and mitigates flood attacks in IPv6 enabled software to define networks. An experimental testbed was developed using sFlow technique, floodlight controller, and OpenFlow version 1.3. A mitigation algorithm was also developed and was tested with a simulation tool Mininet. The real network traffic was tested on the testbed to investigate the effective mitigation of a DDoS attack. The mitigation time performance for IPv6 was 46.6% while IPv4 was 66.6%. Also, The result gathered from the experiment showed that both the response and detection times were 4 secs while the mitigation time was 7secs respectively. The overall control time being 11 secs. The experimental Testbed result shows that the developed testbed outperformed the previous methods with the ability to detect threats on the network faster. The result from the IPv6 testbed is a probable solution to mitigate the threats posed by DDoS attacks on the IPv6 enabled SDN network resources.

In traditional networks, the pre-routed packets are dropped during the link failure leading to a huge data loss. Survivability techniques such as protection and restoration are available to provide the solution before and after the link failure. But, the new flow entries that are to be added in the flow table increase the initial network demand leading to an increase in memory demand per switch. The saved data not only reduces the network speed but also demands of repeated processing of entries. In this paper, we propose a new scheme of Group Table based Rerouting (GTR) technique to find the response against single link failure through Fast Fail-over (FF) group table feature provided by OpenFlow. This scheme provides equal roles for both controllers and forwarding OpenFlow enabled switches. Here, the controller maintains a look-up table which is updated periodically according to the change in network structure. Also, it has to update the FF group table simultaneously corresponding to every active port of the switches. The controller relabels the packets and updates the flow entries on respective switches.

Software defined networking (SDN) and its most popular southbound implementation OpenFlow (OF) are already greatly exploited in the existing mobile cellular networks as part of data centers and mobile core networks. Due to user’s mobility, it is of upmost importance for the operators to provide the shortest possible interruption when the mobile users are performing the procedure of handover. In this work, we proposed a novel analytical approach to model the OF-related handover messages exchanged between the OF-switches and the SDN controller. We modeled two different OF-switch implementations and we compared the results: (1) single shared buffer used for the control and data plane; (2) two priority buffers, where the data plane packets are served only when there are no packets to be processed in the control plane. We numerically evaluated the two systems and we validated the model by using simulations. The obtained results clearly point that although the priority buffering increased the complexity, it effectively provided the shortest handover delay. Therefore, the priority buffering should be the preferred mechanism for mobile networks.

Software defined networking (SDN) has emerged over the past few years as a novel networking technology that enables fast and easy network management. Separating the control plane and the data plane in SDNs allows for dynamic network management, implementation of new applications, and implementing network specific functions in software. This paper addresses the problem of SYN flood attacks in SDNs which are considered among the most challenging threats because their effect exceeds the targeted end system to the controller and TCAM of OpenFlow switches. These attacks exploit the three-way handshaking connection establishment mechanism in TCP, where attackers overwhelm the victim machine with flood of spoofed SYN packets resulting in a large number of half-open connections that would never complete. Therefore, degrading the performance of the controller and populating OpenFlow switches’ TCAMs with spoofed entries. In this paper, we propose ISDSDN, a mechanism for SYN flood attack mitigation in software defined networks. The proposed mechanism adopts the idea of intentional dropping to distinguish between legitimate and attack SYN packets in the context of software defined networks. ISDSDN is implemented as an extension module of POX controller and is evaluated under different attack scenarios. Performance evaluation shows that the proposed mechanism is very effective in defending against SYN flood attacks.

The introduction of software-defined networking (SDN) has emerged as a new network paradigm for network innovations. By decoupling the control plane from the data plane in traditional networks, SDN provides high programmability to control and manage networks. However, the communication between the two planes can be a bottleneck of the whole network. SDN-aimed DoS attacks can cause long packet delay and high packet loss rate by using massive table-miss packets to jam links between the two planes. To detect and mitigate SDN-aimed DoS attacks, this paper presents FloodDefender, an efficient and protocol-independent defense framework for SDN/OpenFlow networks. FloodDefender stands between the controller platform and other controller apps, and conforms to the OpenFlow policy without additional devices. The detection module in FloodDefender utilizes new frequency features to precisely identify SDN-aimed DoS attacks. The mitigation module uses three new techniques to efficiently mitigate attack traffic: table-miss engineering to prevent the communication bandwidth from being exhausted; packet filter to filter out attack traffic and save computational resources of the control plane; and flow rule management to eliminate most of useless flow entries in the switch flow table. Our evaluation on a prototype implementation of FloodDefender shows that the defense framework can precisely identify and efficiently mitigate the SDN-aimed DoS attacks with very little overhead.

Software-defined networking introduces opportunities to optimize the Internet Service Provider’s network and to improve client experience for the Video-on-Demand applications. Recent studies on SDN frameworks show that traffic engineering methods allow a fair share of bandwidth between adaptive video streaming clients. Additionally, ISPs can make better estimations of bandwidth and contribute to the bitrate selection for the clients. This study focuses on another aspect of network assistance in video delivery: CDN server selection. In a typical framework where the ISP contributes to the CDN selection, the video provider and the network provider interfaces are merged together. Clients connect to the ISP to get the best CDN server candidate for a given video. This exposes client requests to the ISP. However, video providers have been investing large resources for encrypted video provisioning to preserve their client’s information from third parties, especially network providers. The typical approach is not practical due to privacy concerns. In this study, we present a framework called PPNet to allow CDN-ISP collaboration while preventing the ISP’s access to the video request and availability information. Our framework introduces an isolation between the video provider’s and the ISP’s web interfaces. Clients connect to both of the interfaces and deliver information on a need-to-know basis. As a second contribution, PPNet introduces a practical optimization method for CDN selection. Real-time data collection capabilities of a typical OpenFlow network is used as the input for optimization. Congestion-awareness has been the priority. To adapt for changing network conditions, capability of utilizing multiple servers simultaneously for a single video is introduced. Instead of directing each video client into a CDN node, the proposed system performs request routing per video segment. Finally, we present a system prototype of PPNet and show that our multiple-host adaptive streaming method introduces a significant improvement in quality of experience when compared to the state of the art.

Due to the increase in video streaming traffic over the Internet, more innovative methods are in demand for improving both Quality of Experience (QoE) of users and Quality of Service (QoS) of providers. In recent years, HTTP Adaptive Streaming (HAS) has received significant attention from both industry and academia based on its impacts on the enhancement of media streaming services. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the Network Operators’ infrastructure and evolving network conditions. Along with the development of future Internet infrastructure, Software-Defined Networking (SDN) has been researched and newly implemented as a promising solution in improving services of different Internet layers. In this paper, we present a novel architecture incorporating bitrate adaptation and dynamic route allocation. At the client side, adaptation logic of VBR videos streaming is built based on the MPEG-DASH standard. On the network side, an SDN controller is implemented with several routing strategies on top of the OpenFlow protocol. Our experimental results show that the proposed solution enhances at least 38% up to 185% in term of average bitrate in comparison with some existing solutions as well as achieves better viewing experience than the traditional Internet.

The Virtual Private Network (VPN) service offered by the Multi-Protocol Label Switching Protocol (MPLS) is more and more solicited by clients thanks to its various advantages that it can offer especially concerning the security, the quality of service, and the flexibility. MPLS VPN relies on multiple protocols to function correctly and can be represented according to different architectures, each of which requires its own configuration. Besides, this technology is often used to interconnect a high number of sites which makes it challenging to deploy, manage, and supervise. Software Defined Network (SDN) is a paradigm, which, thanks to a centralized point (controller) can manage the MPLS VPN tunnels in an automatic, dynamic, and on-demand way between all the clients’ equipment. To manage the equipment by an SDN controller, they must be of the new generation supporting the OpenFlow protocol. The hybrid SDN paradigm allows Legacy equipment to be included in the orchestration as well, reducing OPEX and CAPEX costs. By the present work, we propose a new approach (SH-SDN) SDN Hybrid for the management of different architectures MPLS VPN based on a new graphical interface. The proposed approach allows not only to manage the MPLS VPN tunnels but also to ensure a reasonable quality of service level by performing an adaptive and an optimal load balancing across the multiple paths. The approach has been implemented and tested in a virtual test network consisting of different manufacturers (Cisco, Juniper, and HP). The results show that the SH-SDN approach offers a remarkable reduction in the MPLS VPN tunnel establishment time in a large network and a reasonable quality of service level of the transported applications through the multipath routing improved process.

The design of existing machine-learning-based DoS detection systems in software-defined networking (SDN) suffers from two major problems. First, the proper time window for conducting network traffic analysis is unknown and has proven challenging to determine. Second, it is unable to detect unknown types of DoS saturation attacks. An unknown saturation attack is an attack that is not represented in the training data. In this paper, we evaluate three supervised classifiers for detecting a family of DDoS flooding attacks (UDP, TCP-SYN, IP-Spoofing, TCP-SARFU, and ICMP) and their combinations using different time windows. This work represents an extension of the runner-up best-paper award entitled ‘Detecting Saturation Attacks in SDN via Machine Learning’ published in the 2019 4th International Conference on Computing, Communications and Security (ICCCS). The results in this paper show that the trained supervised models fail in detecting unknown saturation attacks, and their overall detection performance decreases when the time window of the network traffic increases. Moreover, we investigate the performance of four semi-supervised classifiers in detecting unknown flooding attacks. The results indicate that semi-supervised classifiers outperform the supervised classifiers in the detection of unknown flooding attacks. Furthermore, to further increase the possibility of detecting the known and unknown flooding attacks, we propose an enhanced hybrid approach that combines two supervised and semi-supervised classifiers. The results demonstrate that the hybrid approach has outperformed individually supervised or semi-supervised classifiers in detecting the known and unknown flooding DoS attacks in SDN.

Software-Defined Networking (SDN) is used widely in Data Center Networks (DCNs) to facilitate the automated configuration of network devices required to provide cloud services and a multi-tenant environment. The resulting rate of change presents a challenge to a DCN operator who needs to be able to answer questions about the past state of the network. We describe our work in addressing this need, and how an ontological approach was taken to build a topological and temporal model of a DCN, which could then be populated using control-plane data captured in a message log. Sophisticated queries applied against the populated model allow the DCN operator to gain insight into the effects of historical automated configuration changes. We have tested our model for accuracy against a network from which a message log was captured, and we have demonstrated how queries have been formulated to retrieve useful information for the DCN operator.

Being a state-of-the-art network, Software Defined Networking (SDN) decouples control and management planes from data plane of the forwarding devices by implementing both the control and management planes at logically centralized entity, called controller. This helps to make simple and easy both the network control and management. Failure of links occurs frequently in a computer network. To deal with the link failures, the existing approaches computes and installs multiple paths for a flow at the switches in SDN without considering the reliability value of the primary path. This incurs extra computation to compute multiple paths, and both increased computation time and traffic to install extra flow rules in the network. In this research work we propose a new approach that calculates the link reliability and then installs the number of multiple paths based on the reliability value of the primary path. More specifically, if a primary path has higher reliability then a smaller number of alternative paths should be installed. This shall decrease the path computational time and flow rule installation load at controller. Resultantly there shall be less flow rule entries in switch flow table which in turn will avoid the overflow of the flow table. Through simulation results, our proposed approach performs better as compared to the existing approach in term of computational overhead at controller, end-to-end delay for packet deliver and the traffic overhead for flow rule installation.

The Internet of things (IoT), 5G networks, edge/fog computing, vehicular networks, and cloud computing are current examples of systems with challenging requirements for modeling, orchestrating, and allocating resources in networks. In this context, there is a need for dynamic network infrastructures that adapt to the demands and requirements of users. This adaptive capacity of the network commonly uses machine learning algorithms to deal with the inherent complexity of the problem and support the operational dynamism of the network. These new network structures are called intelligent networks, as a reference to their operational intelligence, or self-driving networks, as a reference to their ability to adapt to the operation. The virtualization of network resources is another aspect of research with significant impact and relevance. In this case, the network resources are sliced and grouped to create virtual networks using shared physical resources with high cost, efficiency, and scale gains for companies and institutions. The NSRAlloc-ML (Network Slicing Resource Allocation ML-Enhanced) project proposes the modeling and deployment of network slicing strategies to allocate communication resources capable of supporting the dynamism and elasticity of end-to-end communication users. NSRAlloc-ML uses new resource allocation models with bandwidth allocation models (BAM) aided by Q-Learning and SARSA algorithms for resource slicing and bandwidth allocation in networks. The DyRA framework (Framework for Dynamic Resource Allocation) integrates the components of NSRAlloc-ML to provide a virtual, dynamic, and intelligent resource allocation over a physical network infrastructure. Intelligent bandwidth allocation in NSRAlloc-ML uses, in addition to machine learning algorithms, two other innovative elements: the SDN/OpenFlow paradigm for programming the network infrastructure and the Publish/Subscribe (Pub/Sub) strategy for a distributed data access scenario. A dynamic, intelligent, and self-regulating resource allocation for network slicing is the target of the NSRAlloc-ML and is an innovative solution to the NP-hard class of network slicing problems with communication resource allocation for Edge/Fog/Cloud. NSRAlloc-ML addresses complex problems subject to multiple requirements and objectives that are difficult to solve via heuristics and other approaches to obtain an optimal solution., {"references": ["S. Pattar, R. Buyya, K. R. Venugopal, S. S. Iyengar, and L. M. Patnaik. Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions. 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As a new networking paradigm, Software-Defined Networking (SDN) separates data and control planes to facilitate programmable functions and improve the efficiency of packet delivery. Recent studies have shown that there exist various security threats in SDN. For example, a saturation attack may disturb the normal delivery of packets and even make the SDN system out of service by flooding the data plane, the control plane, or both. The existing research has focused on saturation attacks caused by SYN flooding. This paper presents an anomaly detection method, called SA-Detector, for dealing with a family of saturation attacks through IP spoofing, ICMP flooding, UDP flooding, and other types of TCP flooding, in addition to SYN flooding. SA-Detector builds upon the study of self-similarity characteristics of OpenFlow traffic between the control and data planes. Our work has shown that the normal and abnormal traffic flows through the OpenFlow communication channel have different statistical properties. Specifically, normal OpenFlow traffic has a low self-similarity degree whereas the occurrences of saturation attacks typically imply a higher degree of self-similarity. Therefore, SA-Detector exploits statistical results and self-similarity degrees of OpenFlow traffic, measured by Hurst exponents, for anomaly detection. We have evaluated our approach in both physical and simulation SDN environments with various time intervals, network topologies and applications, Internet protocols, and traffic generation tools. For the physical SDN environment, the average accuracy of detection is 97.68% and the average precision is 94.67%. For the simulation environment, the average accuracy is 96.54% and the average precision is 92.06%. In addition, we have compared SA-Detector with the existing saturation attack detection methods in terms of the aforementioned performance metrics and controller’s CPU utilization. The experiment results indicate that SA-Detector is effective for the detection of saturation attacks in SDN.

In this paper, we address the problem of defining probabilistic bounds of packet flow through an OpenFlow switch in software-defined networks (SDNs). The problem is challenging, as OpenFlow is one of the popular southbound application programming interfaces, which enables controller-switch interaction. The related existing literature addresses the different aspects of OpenFlow and SDN-controller interactions. However, there is a need to analyze the performance of the OpenFlow switch, in order to determine the bounds of the performance measures. In this paper, we propose Markov chain-based analytical model, named AMOPE, for analyzing packet flow through an OpenFlow switch, while defining the probabilistic bounds on performance analysis. Additionally, in AMOPE, we propose a state diagram based on the OpenFlow specification version 1.5.0, and calculate the theoretical probabilities of a packet to be in different states of the OpenFlow switch. Furthermore, AMOPE defines the theoretical bounds of OpenFlow performance measures such as the output action, packet drop, and send to the controller probabilities. Simulation-based analysis exhibits that approximately ${\text{60}}\%$ of the processed packets are sent to output action, ${\text{31}}\%$ of the processed packets are sent to the controller, and the remaining processed packets are dropped in an OpenFlow switch.