SDN Controller Research Articles

A novel Distributed Denial of Service attack defense scheme for Software-Defined Networking using Packet-In message and frequency domain analysis

Software-Defined Networking (SDN) enhances network management by improving adaptability, flexibility, and scalability. However, its centralized controller is vulnerable to Distributed Denial of Service (DDoS) attacks that can disrupt network availability. This study introduces a novel real-time DDoS detection scheme integrated into the SDN controller. The scheme uses a two-step process to analyze Packet-In messages in both time and frequency domains. A time-series is generated by sampling the number of Packet-In messages at specific time intervals, which is compared against a predefined threshold. If exceeded, frequency domain analysis is applied to extract features, which are then used by Machine Learning (ML) algorithms to identify DDoS attacks. The scheme achieves 99.85% accuracy in distinguishing normal traffic from attack traffic, demonstrating its effectiveness in safeguarding SDN environments from DDoS threats.

DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests.

Distributed Denial of Service Attack Detection and Prevention Using Pipit Fox-Attentional Deep Learning in Software-Defined Networking

The Software-Defined Network (SDN) remains the innovative model that assists in satisfying the new application requirements of future networks. However, destructive attacks, particularly Distributed Denial of Service (DDoS) attacks, are aimed primarily at the SDN control panel, presenting a significant risk to network security. The traditional approaches for DDoS attack detection exhibit several limitations including interpretability challenges, overfitting problems, and false predictions. To mitigate these drawbacks, the research proposed a Pipit Flying Fox Optimized Deep Neural Network (PPF-DNN) and Intelligent Pipit Forage Optimized-Attentional Convolutional Neural Network (IPFO-ACNN) model. The channel attention is employed to enable the network to focus selectively on relevant information, improving the model’s sensitivity to subtle anomalies associated with DDoS attacks. By dynamically adjusting the attention weights across channels, the mechanism enhances the capability of the model to discriminate the normal network traffic and malicious patterns, which results in improved precision and reduced false positives. This approach harnesses the power of CNN to automatically extract hierarchical features from network data, enhancing the efficacy of the ACNN model in attack detection. IPFO and PPF algorithms integrate the unique strengths of bio-inspired algorithms, creating a synergistic approach for efficient model parameter tuning. In the case of Training Percentage (TP) 80, the accuracy, sensitivity, and specificity of the IPFO-ACNN model are evaluated as 98.87%, 99.24% and 97.91%, respectively. Similarly, the PPF-DNN model’s performance is assessed as 98.49%, 92.50% and 98.55%, which presents a promising avenue for bolstering network security infrastructure, ensuring more robust DDoS detection and mitigation capabilities in an increasingly complex cyber threat landscape.

Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing

Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion.

Data Mining Techniques used for Evaluation an Efficient DDoS Attack Detection System: A Deep-Learning Model

Developed recently to solve the shortcomings of traditional networks, software-defined networking is a new paradigm (SDN). Decoupling the control plane from the data plane, which is the SDN's core property, makes network management simpler and promotes effective programmability. The new design, on the other hand, is vulnerable to a number of attacks that could exhaust the system's resources and prohibit the SDN controller from offering services to authorized users. One of these threats, the Distributed Denial of Service (DDoS) assault is one that is gaining popularity. A DDoS assault has a severe negative effect on emphasize servers have no ability to access their network facilities as a result. accommodate legitimate users. We introduce DDoS Net, a DDoS attacks system for intrusion detection for SDN systems. Our approach is based on data mining software (Rapdiminer) and neural networks working along with auto encoders. As a result, we have a lot of faith in protecting these networks thanks to our methods. We assess the CICIDS 2019 dataset, which includes network behavior associated with DDoS threat and benign network activity, is publicly available, and satisfies certain requirements. It evaluates a variety of data mining algorithms' efficacy as well as internet usage characteristics to determine the best attributes for spotting the most typical assault kinds.

A Blockchain-Based Security Framework for East-West Interface of SDN

Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based security framework that integrates Ethereum technology with customized blockchain algorithms for authentication, encryption, and access control. The framework introduces decentralized mechanisms to protect against diverse attacks, including false data injection, man-in-the-middle (MitM), and unauthorized access. Experimental results demonstrate the effectiveness of this framework in securing distributed controllers while maintaining high network performance and low latency, paving the way for more resilient and trustworthy SDN infrastructures.

Traffic Classification in Software-Defined Networking Using Genetic Programming Tools

The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features.

Efficient load distribution in heterogeneous vehicular networks using hierarchical controllers

Vehicle movement poses significant challenges in vehicular networks, often resulting in uneven traffic distribution. Fog computing (FC) addresses this by operating at the network edge, handling specific tasks locally instead of relying solely on cloud computing (CC) facilities. There are instances where FC may need additional resources and must delegate tasks to CC, leading to increased delay and response time. This work conducts a thorough examination of previous load balancing (LB) strategies, with a specific focus on software-defined networking (SDN) and machine learning (ML) based LB within the internet of vehicles (IoV). The insights derived from this research expedite the development of SDN controller-based LB solutions in the IoV network. The authors proposes the integration of a local SDN controller (LSDNC) within the FC tier to enable localized LB, addressing delay concerns. However, the information will be available to the main SDN controller (MSDNC) too. The authors explore the concept mathematically and simulates the formulated model and subjecting it to a comprehensive performance analysis. The simulation results demonstrate a significant reduction in delay, with a 125 ms difference when 200 onboard units (OBUs) are used, compared to conventional software-defined vehicular networks (SDVN). This improvement continues to increase as the number of OBUs grows. Our model achieves the same maximum throughput as the previous model but delivers faster response times, as decisions are made locally without the need to wait for the main controller.

5G Network with Hexagonal SDN Control for Highly Secure Multimedia Communication

The 5G communication network is a promising technology that offers user-centric connectivity to customers, enabling quick, high-capacity, and latency-free access to many applications. Software Defined Networking Controller (SDNC) is an emerging technology that is used for increasing the security and maintaining the dynamic nature of mobile devices in latency and energy-assured 5G wireless communication. Different SDN topology open flow controllers are used in the literature for increasing the throughput and security of the network but they possess some drawbacks such as frequent location update (LU) overhead, high latency, and high energy consumption. This paper proposes a hexagonal software defined network (H-SDN) controller to overcome these challenges. The proposed H-SDN topology is used to preserve Intermediate Message Layer (IML) location during multimedia data transmission. A Core Switch (CS) updates an IML's location within clusters of cells, as long as it remains within the cluster. However, in the proposed method, there will be a location update on the server if the IML departs the cluster. Experiment results show that the proposed method can increase security, which in turn increases throughput, while reduces the location update costs, energy consumption, and transmission latency.

Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity

<p>The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.</p>

Softwarized and containerized microservices-based network management analysis with MSN

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

Adaptive quorum based scheduling and interference-free routing for edge enabled UAV assisted software-define WSN using AI

Wireless Sensor Networks (WSN) faces numerous problems. The deployment of the sensor nodes in faraway places makes battery replacement a difficult process. As a result, there are energy constraints and security issues. So, the fusion of SDN with WSN is SDWSN, which flexibly brings network management. The main issue here is controller placement, which has an impact on communication dependability, latency, and throughput. The major drawbacks in existing work include high energy consumption, inefficient data collection, increased response time, and poor packet delivery ratio. This paper presents a unique Artificial Intelligence-based method for adaptive quorum-based scheduling and interference-free routing in edge-enabled UAV-assisted Software-Defined Wireless Sensor Networks (SDWSNs). It combines energy-efficient clustering with E-DBSCAN, multiple attribute-based software node authentication, and interference-free routing with the IDEE algorithm. The NS-3 tool was utilized to conduct simulations using a setup consisting of 4 edge-assisted UAVs, 4 SDN controllers, and 100 wireless sensor nodes. The findings show considerable improvements in different performance measures when compared to previous techniques. In particular, this suggested approach reduces latency by 64.77 % when compared to DGRL and 66.30 % when compared to ESRA. Additionally, it uses less energy than DGRL and ESRA by 45.74 % and 51.89 %, respectively. In comparison to DGRL and SRA, the packet delivery ratio is enhanced by 27.27 % and 32.43 %, respectively. Furthermore, the Node Network Lifetime is increased by 24.29 % in comparison to ESRA and 12.99 % in comparison to DGRL. Additionally, our method increases throughput by 22.08 % compared to DGRL and 30.56 % compared to SRA, while reducing the Controller Response Time by 2.39 % compared to ESRA and 6.46 % compared to DGRL. These findings demonstrate how well our method improves the effectiveness and efficiency of SDWSNs, making it a viable option for practical use.

DIS-Guard: Enhancing SDN resilience to topology and RCO attacks

SDN controllers employ discovery protocols with LLDP packets to discover the network topology; however, this approach is susceptible to topology attacks due to the static reuse of LLDP packets without proper origin verification or integrity checks. This paper proposes a novel form of topology attack called the Round-trip time Confusion Onslaught (RCO) attack. Our findings showcase the capability of the RCO attack to mislead the discovery service of SDN controllers and bypass the Real-time Link Verification (RLV) defence mechanism, which depends on timing features in Machine Learning (ML) models. The RCO attack significantly reduces the effectiveness of the RLV mechanism in terms of True Positive Rate (TPR), Precision, F1-score, and Cohen’s Kappa and increases the False Positive Rate (FPR). These results underscore the difficulties in achieving dependable ML accuracy in topology attack detection. Accordingly, we propose the DIS-Guard framework, designed to protect and improve the efficiency of discovery protocols within SDN controllers. This framework utilizes the mutual Transport Layer Security (mTLS) protocol to discover hosts proactively. Moreover, it employs a novel combination of a chaos-tent map and the proposed statistical method for link discovery. Through extensive evaluation, we illustrate DIS-Guard’s superior performance and security over the established Ryu (switches.py) controller and the RLV defence mechanism. Notably, DIS-Guard reduces host-to-host access delays, decreases LLDP packet transmission and reception significantly, and improves control channel efficiency. Collectively, these quantifiable benefits manifest the efficacy of DIS-Guard as a significantly optimized solution for SDN security and performance.

Classification of DDoS attack traffic on SDN network environment using deep learning

Distributed Denial of Service (DDoS) attack is a major threat to the Internet of Things (IoT), Software Defined Networks (SDN), and Cloud Computing Networks. Due to the tremendous applications of IoT networks, the number of DDoS attacks is increasing significantly, and most sophisticated DDoS attacks are generated through IoT botnets. An IoT botnet-based DDoS attack can disrupt the network quickly with a surge of malicious traffic. Especially in an SDN network, it is important to detect the DDoS attack before it occurs to the SDN controller. DDoS attacks on the centralized controller of the SDN can disrupt the whole network. So, identifying DDoS attacks at the earliest is a critical security measure for network experts and practitioners. In this paper, we analyze the DDoS attack on an SDN environment and develop a method to identify the DDoS attack using Deep Learning (DL). The proposed method converts the captured raw network traffic to image data and classifies the malicious data from normal data. The method is evaluated on our test-bed simulated dataset and two other benchmark datasets. The experimental comparison shows that the proposed method performs better on all three datasets, giving more than 99% classification accuracy.

Enhanced CPU Design for SDN Controller.

Software-Defined Networking (SDN) revolutionizes network management by decoupling control plane functionality from data plane devices, enabling the centralized control and programmability of network behavior. This paper uses the ternary system to improve the Central Processing Unit (CPU) inside the SDN controller to enhance network management. The Multiple-Valued Logic (MVL) circuit shows remarkable improvement compared to the binary circuit regarding the chip area, propagation delay, and energy consumption. Moreover, the Carbon Nanotube Field-Effect Transistor (CNTFET) shows improvement compared to other transistor technologies regarding energy efficiency and circuit speed. To the best of our knowledge, this is the first time that a ternary design has been applied inside the CPU of an SDN controller. Earlier studies focused on Ternary Content-Addressable Memory (TCAM) in SDN. This paper proposes a new 1-trit Ternary Full Adder (TFA) to decrease the propagation delay and the Power-Delay Product (PDP). The proposed design is compared to the latest 17 designs, including 15 designs that are 1-trit TFA CNTFET-based, 2-bit binary FA FinFET-based, and 2-bit binary FA CMOS-based, using the HSPICE simulator, to optimize the CPU utilization in SDN environments, thereby enhancing programmability. The results show the success of the proposed design in reducing the propagation delays by over 99% compared to the 2-bit binary FA CMOS-based design, over 78% compared to the 2-bit binary FA FinFET-based design, over 91% compared to the worst-case TFA, and over 49% compared to the best-case TFAs.

QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

ProDetect: A Proactive Detection Approach of the TCP SYN Flooding Attack in the SDN Controller

The TCP SYN flooding attack or assault is one of the most prominent methods for the controller layer and the target server exhaustion in software-defined networking (SDN). The switches in the data layer or plane are forced to transfer an enormous amount of illicit SYN requests that the adversary creates to the controller because there are no forwarding rules. This extensive forwarding strains the computing capacity of both the data layer and the control layer and clogs the transmission route connecting them. We suggest a dynamic threshold-based countermeasure in this research, named ProDetect, to proactively cater to the SYN flooding attack in the SDN environment. We have fully implemented ProDetect as a security extension on the SDN controller for verifying inbound TCP connections. For SYN requests those that pass through ProDetect's validation test, the controller installs data forwarding policies for them and other malicious hosts sending forged requests are blocked. Therefore, these hosts are barred from sending any new TCP requests to the switches present inside the data layer. We have also employed a strategy to cope with forged MAC addresses in this work. When compared with the existing approaches, the evaluation of performance conducted by the simulation data reveals that ProDetect demonstrates low side effects for legitimate TCP requests and increases the total response time through an onset of SYN flooding operation.

Security Assessment in Software Defined Networks (SDN): Vulnerabilities, Challenges and Research Prospects

Conventional internet architecture facilitating users for different services and applications but facing number of challenges like network management, QoS management for network virtualization, IP multicasting, deployment of IPV6, crucial security measures, end to end connectivity, inter and inter domain routing. To meet the demand of these services due to rapid growth of technologies and traffic, an emerging network architecture termed as Software Defined Network (SDN) with programmable technology has brought unprecedented management to control networks. Due to separation of data, control and application planes software defined network provides cost effective, openness, centralized automation, programmable features as per users own demands and high resilience to network administrators. OpenFlow evolved as a first standard protocol for software defined network control and data planes communication to meet changing business requirements. Although, Software Defined Network brings enormous advancements in networks to support business applications but it is severely affected with cyber-attacks at data, control and application planes. Middle boxes plays a significant role to manage network effectiveness and provide adequate security control from external and internal security threats but require proper management and configuration otherwise it leads to devastating effect. The main contribution of this paper are: (1) It explores various potential security attacks at SDN layers and inconsistent policies, (2) It provides various security concerns in SDN planes and preventive measures against these prevailing attacks, (3) It discusses security threats challenges and research opportunities in software defined network keeping in view critical security controls like spam detector, IDS/IPS, Firewall and policy management. This article assists researchers to comprehend security concerns and state of the art development and challenges explored by scientific community in SDN.

Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.

Performance Evaluation of OpenStack Cloud Integrated with SDN Controller

The Internet is undergoing a revolution thanks to the development of a very popular recent technology trend - “Cloud Computing”. Large, small, and medium-sized businesses organizations, as well as individuals increasingly prefer to use cloud computing services due to their many advantages. Furthermore, the widespread implementation of cloud computing in large enterprises is an indicator of the technological advancement of information technology companies. It helps in the efficient allocation and utilization of infrastructure, power, and resources. In the meantime, centralized control provided by software-defined networking (SDN) allows for flexibility in operations and management. For that reason, there has been an adequate amount of research and application of combining these two objects over the years. In this study, we will evaluate network performance between virtual machines in terms of bandwidth, latency, and system performance interms of CPU, RAM, etc. in OpenStack with and without integration of SDN Controller - Tungsten Fabric (TF).