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Example of OpenFlow Figure 3. Packet Flow through an OF switch 3.1.2 Flow tables entries are classified by match fields and priorities, i.e. match fields and priorities are bound together to define a specific flow entry in a particular flow table. Flow tables include: • Switching: It includes the MAC destination. If there is a match with the MAC destination field then the packet is forwarded to eth2. It behaves like a normal L2 mechanism. • Firewall: If the user wants to execute the firewall, first look into the TCP destination port. If the TCP destination port is 22 then just drop the packet. • Forwarding: If the user wants to execute the forwarding of the packets, firstly look into the IP destination. If IP belongs to 202.2.*.* then forward the packet to eth2. • Flow Switching: With the help of flow tables users can make the convergence between the packet and circuit switching network. Therefore, the idea of packet switching is to choose a specific path for a specific flow.
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With assistance from Software Defined Networking (SDN), networks have become more creative to build and maintain over the last few years. The inflexibility of modern network architecture is presenting researchers with a tough achievement. SDN replaces existing inescapable and complicated networks with a creative way of separating the control plane...
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... client i.e. software layer and hardware layer. From the hardware layer, source MAC is started i.e. it is a wildcard character that means the client can accept any source MAC field or any destination MAC field, as explained in Figure 2 source IP or destination IP has to be 128.9.1.10, and the source and destination port can be anything. ...
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Software-defined networking (SDN) is a new approach that overcomes the obstacles which are faced by conventional networking architecture. The core idea of SDN is to separate the control plane from the data plane. This idea improves the network in many ways, such as efficient utilization of resources, better management of the network, reduced cost,...
Citations
... On the other hand, P.V. Tijare and D. Vasudevan in Ref. [14] worked on Northbound APIs and Programming languages. A. Martinez et al. in Ref. [15] worked on Network Management challenges and trends in Multi-layer and Multi-vendor settings for carrier-Grade networks. They discussed the interoperability of Multi-vendor devices as one of the research challenges. ...
Software Defined Networking (SDN) is an emerging networking paradigm with the potential to foster innovation through programmable networks. SDNs are characterized by the separation of control and data planes where, in a logically centralized controller, it's possible to make routing decisions on behalf of forwarding elements. For this, there are different protocols proposed and used like Open Flow, Forward and Control Element Separation (FORCES), Path Computation Element Protocol (PCEP), Network Configuration Protocol (NETCONF), and Interface to Routing System (IRS), among others. Border Gateway Protocol (BGP) is one such protocol that enables the network programmability promised by SDNs. The controllers used in BGP as control plane protocols leverage RESTCONF (RFC 8040) as management plane protocol to interact with physical routers, switches, and network service firewalls. Therefore, the aim of this work is to improve the performance of SDN controllers using BGP enabling a multi-vendor environment without requiring infrastructure upgrade showcasing how interoperability can be achieved. The proposed system is implemented using OpenDaylight controllers. The performance of the prototype is evaluated using Latency, Throughput, CPU consumption, and TTL (Time to Live). Based on the simulation results it is observed that the SDN controllers that use BGP show average throughput of 49.6 Gbps, maximum latency of 7 μs, and average CPU consumption 89% and maximum TTL value of 600 msec. The overall performance of the proposed system shows better results when compared with previous works.
... Finally, when we talk about the Inability to scale; As application workloads change and demand for network bandwidth increases, the IT department either needs to be satisfied with an oversubscribed static network or needs to grow with the demands of the organization. Unfortunately, the majority of traditional networks are statically provisioned in such a way that increases the number of endpoints, services, or bandwidth requires substantial planning and redesign of the network [13].In Figure 2 we describe the architecture of the traditional network. ...
Intelligent information systems, which have recently undergone development and complexity, are now indispensable to the entire world. The networking strategy has unquestionably altered based on machine learning principles to be programable and dynamically configurable with the greatest flexibility and simplicity of use. The term "software-defined network" (SDN) refers to networks that are managed using software applications and SDN controllers as opposed to the more traditional network management consoles and commands, which require a lot of administrative overhead. To centralize network control and administration, SDN changed the topology of network devices to be more flexible and programable. The software-defined network's uses protocols for interacting with and managing switches is called OpenFlow (OF). With this protocol, the switches learn the routing information from the controller and then pass data packets based on this information. One of the most important components of the SDN is the controller, which is the smartest component of the network such as the Ryu controller. Including the importance of the Ryu controller in SDN. This article discussed how to enhance data traffic transmission and classification in the SDN environment. This research shows how we can track all data packets and traffics and automatically identify all data types and classify them correctly, so we can apply a security policy, bandwidth, and quota for each type. The most different thing we used is using a real SDN network environment and also connected a real physical lambda server that makes daily continuous training for all data traffic and synchs this at the same time with the SDN controller that applies this instantly on the real live traffic. Using Machine Learning (ML) and Artificial Intelligence (AI) to enhance the SDN environments and identify data traffic types automatically. The controller (using ML and AI) takes the needed action automatically according to the data types. Enhance security, Data Transmission, and Data Availability in the software-defined networking and Intelligent Systems environment.
... Today's internet structure cannot provide good services, so we need solutions to reach the desired quality of services [11]. Adaptive QoS-based routing and resource reservation (VQoSRR) for video streaming enables SDN networks to handle video demands and improve user experience over best-effort networks introduced in reference [25]. ...
The quality of service is not the same in all parts of the network. Some areas experience a low level and others a higher level of fixed quality services. The shortcomings in legacy networks encouraged researchers to find a new paradigm of the network to obviate legacy networks' deficiencies. The effort to create network services is called Quality of Service (QoS). Software-Defined Networking (SDN) focuses on separating the control layer from the data layer, and their communication is done through a central controller named SDN controller. After separation, the data layer moves the packets through the network according to the commands it receives from the controller. The controller obtains applications (QoS requests), translates them to low-level instructions, and implements them in the data layer. In this paper, we create an infrastructure for Quality of Service (QoS) in tree topology using a meter table per flow in Software Defined Networking Floodlight open-source controller. Meters are introduced into the OpenFlow protocol version 1.3, which calculates the packet rates allocated to them and allows control of those packet rates. Meters are directly connected to flow entry. Any flow entry can determine a meter in its command collection, which calculates and supervises the sum of all flow entries to which it is connected. When we get statistics from the meter table in each switch, we manage the network and affect the routing algorithms.
... The most important component is the controller, which directly controls the configuration of the network device itself [4]. Basically a controller centralizes network intelligence, while the network maintains the data plane distributed by the OpenFlow switch [5]. Routing is the process of selecting paths for data traffic on one or several networks so that the transfer process of routing routes can only be carried out by network administrators, this will cause greater network downtime if there is a link failure [6]. ...
Software defined network (SDN) is a new approach concept for planning, developing, and operating computer networks. Routing is the process of selecting paths on a network system to send or forward packets to the destination network. This study aims to obtain the results of the calculation analysis using the k-nearest neighbor (k-NN) method as the implementation of the recommendations. The results of the analysis use a mesh topology design approach with predicted values of throughput, jitter, delay, and packet loss. This value is used as a recommendation to the network manager to determine the best path. The best path selection from the analysis results is i) path-1 (very good) which includes switch 1, switch 2, and switch 4; ii) path-2 (good) to switch 1, switch 3 and switch 4 switch 4; iii) channel-3 (moderate) through switch 1, switch 2, switch 3 and switch 4; and iv) channel-4 (bad) through switch 1, switch 3, switch 2, and switch 4. While the calculation using the confusion matrix is accuracy=72.31%, precision=96.08% and recall=87.84%.
... The most important component is the controller, which directly controls the configuration of the network device itself [4]. Basically a controller centralizes network intelligence, while the network maintains the data plane distributed by the OpenFlow switch [5]. Routing is the process of selecting paths for data traffic on one or several networks so that the transfer process of routing routes can only be carried out by network administrators, this will cause greater network downtime if there is a link failure [6]. ...
Software defined network (SDN) is a new approach concept for planning, developing, and operating computer networks. Routing is the process of selecting paths on a network system to send or forward packets to the destination network. This study aims to obtain the results of the calculation analysis using the k-nearest neighbor (k-NN) method as the implementation of the recommendations. The results of the analysis use a mesh topology design approach with predicted values of throughput, jitter, delay, and packet loss. This value is used as a recommendation to the network manager to determine the best path. The best path selection from the analysis results is i) path-1 (very good) which includes switch 1, switch 2, and switch 4; ii) path-2 (good) to switch 1, switch 3 and switch 4 switch 4; iii) channel-3 (moderate) through switch 1, switch 2, switch 3 and switch 4; and iv) channel-4 (bad) through switch 1, switch 3, switch 2, and switch 4. While the calculation using the confusion matrix is accuracy=72.31%, precision=96.08% and recall=87.84%.
... The application plane mainly contains SDNbased applications, and users can deploy new applications through the northbound interface without understanding the underlying details, which provides great convenience for the installation of intrusion prevention systems. SDN controllers have developed at a high rate in recent years and at this stage are divided into two main factions, one is the camp of network service providers such as operators, of which the main representatives are ONOS controllers [14] and Ryu controllers [15], and the other is the camp of network equipment vendors, of which the main representative is the OpenDayLight controller [16]. Since OpenDayLight controllers and ONOS controllers are mainly used for industrial applications and their architectures are more complex, scientific experiments usually use lightweight Ryu controllers. ...
Honeypot is an active defense mechanism, which attracts attackers to interact with virtual resources in the honeypot mainly by simulating real working scenarios and deploying decoy targets, so as to prevent real resources from being damaged and collect attackers’ attack processes and analyze potential system vulnerabilities to proactively respond to similar attacks. Because of the existing honeypot system has defects such as the inability to deploy specific honeypots to induce attacks based on complex attacks, the inability to select the best honeypot for dynamic response based on honeypot deployment and maintenance costs during attack interactions, and insufficient ability to identify variants of known attack methods. Although hybrid honeypots can solve some of these problems by deploying low-interaction honeypots and high-interaction honeypots, they cannot really be applied to real production scenarios because of their slow TCP connection switching speed and inability to efficiently identify encrypted malicious traffic. In this paper, we propose a new dynamic security defense system based on the combination of TCP_REPAIR-based dynamic honeypot selection architecture and a deep learning-based intelligent firewall. The system accurately distributes encrypted or non-encrypted attack traffic and its variants through the intelligent firewall. The normal traffic is sent to the actual system, and the marked malicious traffic dynamically selects honeypots to respond according to the attack process.The experimental result indicated that the system can select honeypots for targeted responses according to the actual network situation quickly and dynamically and covertly, effectively improving the utilization rate of honeypot clusters as well as the ability to decoy.
... On the other hand, Babbar and Rani [21] evaluated the performance of Ryu in tree topology of two depth and three fanout (i.e. 9 hosts and 4 switches). They summarized the performance of Ryu using different criteria such as delay, bandwidth, jitter, and packet loss. ...
Traditional networking solutions are unable to meet modern computing needs due to the expanding popularity of the internet, which requires increased agility and flexibility. To meet these objectives, software-defined networking (SDN) arises. A controller is a major element that will determine if SDN succeeds or fails. Various current SDN controllers in many sectors must be evaluated and compared. The performance of two well-known SDN controllers, POX and Ryu, is evaluated in this research. We used the Mininet-WiFi emulator to implement our work and the distributed internet traffic generator (D-ITG) to assess the aforementioned controllers using delay, jitter, packet loss, and throughput metrics. What is new in our research is the study of network performance in two different types of transmission media: wired and wireless. The speed of the wired medium was chosen to be fast ethernet, which was not previously studied. In addition, the size of the packet was varied among 128, 256, 512, and 1,024 bytes. The comparison was performed on three topologies (single, linear, and tree). The experimental results showed that Ryu offers significantly lower latency, jitter, and packet loss than POX in most scenarios. Also, the Ryu controller has higher throughput than POX, especially on wireless networks. Keywords: Bitrate Delay Jitter Mininet-WiFi POX Ryu SDN This is an open access article under the CC BY-SA license.
... However, because proprietary controllers cannot serve the wider public, open source controllers came into being. The ODL (OpenDaylight) architecture represents a major progress in this field and also provides an important basis for the completion of the test in this paper [3] . ...
... The proposed system instantly determines the integrated load of paths when acquiring routing path from the control plane for the method of finding the least loaded realistic path. Therefore, the controller [7] frequently imparts the load information for each route to the load balancer in this built system; then it delivers the optimal route to the controller. We have designed and implemented the load balancing approach with new genetic algorithm to optimize the routing of multimedia data in an efficient way. ...
With the increasing growth in the network and latest technologies by which people communicates via voice or data and modifies the radio devices easily and cost effectively. Software defined radio brings the flexibility, power and efficiency including cloud and big data, control and management of the traditional networks has raised the challenges for the development of multimedia applications. Multimedia applications require to handle the large amount of data at the servers which has increased the load on them. To resolve this issue, Software Defined Networking (SDN) came into existence which makes the management of the network more conformable. To satisfy the constraints of Quality of Service (QoS) and Quality of Experience (QoE) with the limited network availability, one of the keynotes that have been taken into consideration is the load balancing. Therefore, many servers can be used with the load balancers which behave as the front end. The present paper aims to reflect impact on the efficiency of the usage of software-defined networks service in various multimedia applications. A genetic load balancing algorithm (GLBA) is proposed and is implemented on POX controller with mininet emulator in python language to compute its effectiveness and efficiency. Validation of GLBA for 100 to 600 users over server load, weighted round robin, round robin, dynamic server and LBBSRT algorithms with parameters, throughput, response time, memory and CPU utilization has proved the significance of proposed algorithm.
The Border Gateway Protocol (BGP) is commonly used for TCP and UDP services, but it poses challenges in terms of Quality of Service (QoS) analysis. Parameters like throughput, packet loss, delay, and jitter are crucial for assessing network service quality. This study aims to analyze the performance and influence of the BGP routing protocol on TCP and UDP services using QoS parameters. The research used a GNS3 network simulation to conduct multiple packet transmission tests for TCP and UDP protocols, lasting 15, 30, and 60 seconds; and monitored using Wireshark. For TCP services, the average QoS index value is 3.75, categorizing the quality as "Good". The tested network topology and routing configuration exhibit reliable performance, providing good throughput, low packet loss rates, minimal delays, and stable jitter. Similarly, UDP services demonstrate “Good” performance with an average QoS index of 3.75. The BGP routing protocol in the tested network topology ensures high-quality service with good delivery speed, low packet loss rate, minimal delay, and stable jitter. Overall, the study concludes that the BGP routing protocol effectively provides satisfactory QoS for TCP and UDP services. This research contributes to understanding network performance and optimizing routing protocols for improved telecommunications services. The findings highlight the significance of routing protocols in facilitating efficient data transmission on the Internet, reinforcing the importance of QoS analysis for enhancing service quality.
