Optimal Qos-aware network reconfiguration in software defined cloud data centers

Software defined networks (SDN) has gained a lot of attention from both academia and industry recently. SDN is a new networking paradigm that decouples the control plane from the data plane. In this paradigm, control functions are centralized in an SDN controller. The migration from traditional networks to SDN is highly challenging for network operators. Sudden migration may not be possible due to the cost and operational issues. Therefore, incremental deployment of SDN is considered as a viable solution, resulting in the formation of hybrid SDN networks. An important task in hybrid SDN networks is bandwidth allocation and routing considering the coexistence of both conventional and SDN-enabled routers. In this paper, we first formulate this problem as a multi-objective integer program with delay and capacity constraints. Then, a heuristic approach is proposed to combat the computational complexity of the model. Our proposed method is extensively analyzed through simulations, and the evaluation results confirm the effectiveness of our approach.

In today's ever-evolving networking landscape, Software Defined Networking (SDN) has emerged as a paradigm-shifting technology that promises greater flexibility, agility, and control over network infrastructures. However, how easy are the configuration, extensibility, and programmability of these SDN controllers, considering the practical implications for network administrators and developers? Despite the growing adoption of SDN, there is limited research on the comparative performance of controllers across multiple simulation environments. This study aims to explore the practical implementation and comparative evaluation of different SDN Controllers within diverse simulated network environments. Prominent controllers such as POX and Faucet are meticulously configured and deployed in simulated network environments created using GNS3 (Graphical Network Simulator-3), OPNET (Optimized Network Engineering Tools), NS3 (Network Simulator-3), OMNET++ (Objective Modular Network Testbed in C++) and MININET platforms. Furthermore. the study employs a range of performance metrics like controller latency, network throughput, packet loss, CPU (Central Processing Unit) and memory utilization to assess the efficacy and efficiency of each SDN Controller. The Results indicated that Mininet provided the lowest latency, whereas OPNET demonstrated better scalability for large-scale networks. NS3, though useful for SDN network design and visualization, exhibited higher CPU and memory utilization that might limit its scalability for large-scale SDN controller simulations. While GNS3 offered a balanced performance and resource utilization, making it a suitable choice for SDN controller simulation that prioritizes realistic network modelling, OMNET++, on the other hand, exhibited moderate performance metrics with efficient resource utilization, making it suitable for SDN controller simulations requiring a balance between performance...

Considering the problem that the SDN controller can not fully resolve the virtual network function during the Software Defined Network (SDN) and Network Function Virtualization (NVF) collaborative deployment in the satellite network, a SDN/NFV coordination based on the three-layer satellite network is designed. The framework extends the SDN controller function and proposes the SDN controller function extension method (SDN-FE) based on LightGBM algorithm. SDN-FE has extended the functions of the original SDN controller in three aspects. One is based on LightGBM to establish a classification model for intelligent forwarding of SDN controller data packets. The second is to learn and predict VNF modification of data packets through logistic regression. The third is to design an idle state monitoring mechanism. The above method effectively enhances the control capability of the SDN controller to the network. In terms of SDN packet forwarding classification, SDN-FE is compared with existing well-known machine learning classification algorithms such as SVM, random forest, LSTM. Simulation experiments show that the SDN packet forwarding classification problem is predictable. The average accuracy of the LightGBM model in multi-classification problems is over 69%, which is better than machine learning models such as SVM and random forest. The next step is to conduct an in-depth study of the packet rule learning algorithm in VNF.

Software Defined Networks (SDN) is a networking paradigm that separates the control plane from the data plan of a network, and controls data flow with a SDN controller. This networking model would be useful for ISP (Internet Service Provider) networks in order to control their own networks via the controller. Unfortunately, it is difficult to fully implement SDN in ISP networking due to financial and operational constraints. In this article, we will identify the various migration challenges for hybrid SDN in ISP networks, and investigate on the network planning tools currently available in the market that with regards to facilitate the migration process. Lastly, we will discuss on the future direction of research and development in the field of hybrid SDN migration.

Software-Defined Networking (SDN) is now a well-established approach in 5G, Internet of Things (IoT) and Cloud Computing. The primary idea behind its immense popularity is the separation of its underlying intelligence from the data-carrying components like routers and switches. The intelligence of the SDN-based networks lies in the central point, popularly known as the SDN controller. It is the central control hub of the SDN-based network, which has full privileges and a global view over the entire network. Providing security to SDN controllers is one such important task. Whenever one wishes to implement SDN into their data center or network, they are required to provide the website to SDN controllers. Several attacks are becoming a hurdle in the exponential growth of SDN, and among all one such attack is a Distributed Denial of Service (DDoS) attack. In a couple of years, several new SDN controllers will be available. Among many, Open Networking Operating System (ONOS) and OpenDayLight (ODL) are two popular SDN controllers laying the foundation for many other controllers. These SDN controllers are now being used by numerous businesses, including Cisco, Juniper, IBM, Google, etc. In this paper, vulnerability analysis is carried out against DDoS attacks on the latest released versions of both ODL and ONOS SDN controllers in real-time cloud data centers. For this, we have considered distributed SDN controllers (located at different locations) on two different clouds (AWS and Azure). These controllers are connected through the Internet and work on different networks. DDoS attacks are bombarded on the distributed SDN controllers, and vulnerability is analyzed. It was observed with experimentation that, under five different scenarios (malicious traffic generated), ODL-3 node cluster controller had performed better than ONOS. In these five different scenarios, the amount of malicious traffic was incregradually increased. It also observed that, in terms of disk utilization, memory utilization, and CPU utilization, the ODL 3-node cluster was way ahead of the SDN controller.

Software Defined Network (SDN) Technology made tremendous changes in networking field by controlling the hardware with remote software by decoupling data plane and control plane. The focus here is on the controller which is part of control plane. A controller is a central part of SDN; it is an intermediary between Data plane and Forwarding plane. It takes prime place in SDN architecture like Operating System in Computers. SDN Controller is a software which directs the packets using flow rules and also contains statistical information of network. The controller orchestrates the networking devices such as switches and routers by channelizing the packets. SDN controller has different flavor based on the commercial requirement achieved by including new pluggable module. This paper discusses some of the SDN Controller with their feature; the controllers are POX, NOX, Floodlight, ODL, Open Contrail. This paper also compares the characteristic of centralized and distributed controller by focusing on performance, bandwidth and round-trip time of Floodlight and Open Daylight (ODL) controller. The simulation is conducted using Mininet.

Software-Defined Network (SDN) is a new networking paradigm that adopts centralized control logic and provides more control to the network operators over the network infrastructure to meet future network requirements. SDN controller known as operation system, which is responsible for running network applications and maintaining the different network services and functionalities. Despite all its great capabilities, SDN is facing different security threats due to its various architectural entities and centralized nature. Distributed Denial of Service (DDoS) is a promptly growing attack and becomes a major threat for the SDN. To date, most of the studies focus on detecting high-rate DDoS attacks at the control layer of SDN and low-rate DDoS attacks are high concealed because they are difficult to detect. Furthermore, the existing methods are useful for the detection of high-rate DDoS, so need to focus on low-rate DDoS attacks separately. Hence, the use of machine learning algorithms is growing for the detection of low-rate DDoS attacks in the SDN, but they achieved low accuracy against this attack. To improve the detection accuracy, this paper first describes the attack's mechanism and then proposes a Recurrent Neural Network (RNN) based method. The extracted features from the flow rules are used by the RNN for the detection of low-rate attacks. The experimental results show that the proposed method intelligently detects the attack, and its detection accuracy reaches 98.59%. The proposed method achieves good detection accuracy as compared to existing studies.

Network virtualization based on software-defined networking (SDN) has become a necessary technology to provide various services in cloud datacenters. Although many network hypervisors have been proposed to support SDN-based network virtualization, their forwarding techniques excessively consume the limited ternary contents addressable memory of OpenFlow-enabled switches. Moreover, they do not consider network reconfiguration after virtual machine migration. In this paper, we propose LiteVisor, that resolves the two problems mentioned above. It develops the Locator, Identifier, and Tenant sEparating (LITE) scheme. The LITE-based forwarding enables flow aggregation that reduces switch memory consumption. In addition, LiteVisor provides seamless network reconfiguration that does not require tenant controllers to be aware of virtual machine migration based on LITE. We evaluate LiteVisor in terms of the number of flow table entries and network reconfiguration time and compare it with OpenVirteX that is an open-source network hypervisor. The results show that the number of flow rules decreases by up to eight times compared with OpenVirteX in a fat-tree topology. We also demonstrate the seamless network reconfiguration of LiteVisor in the experiments and present the results of the reconfiguration time by the topology size and packet sending interval of hosts.

Software Defined Networking (SDN) has emerged as a new networking paradigm that is based on the decoupling between data plane and control plane providing several benefits that include flexible, manageable, and centrally controlled networks. From a security point of view, SDNs suffer from several vulnerabilities that are associated with the nature of communication between control plane and data plane. In this context, software defined networks are vulnerable to distributed denial of service attacks. In particular, the centralization of the SDN controller makes it an attractive target for these attacks because overloading the controller with huge packet volume would result in bringing the whole network down or degrade its performance. Moreover, DDoS attacks may have the objective of flooding a network segment with huge traffic volume targeting single or multiple end systems. In this paper, we propose an entropy-based mechanism for Distributed Denial of Service (DDoS) attack detection and mitigation in SDN networks. The proposed mechanism is based on the entropy values of source and destination IP addresses of flows observed by the SDN controller which are compared to a preset entropy threshold values that change in adaptive manner based on network dynamics. The proposed mechanism has been evaluated through extensive simulation experiments.

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.

Software Defined Networking (SDN) is a new paradigm proposed to meet the requirements of new networking concepts such as cloud computing and data center networking. SDN decouples control and data planes and uses a logically centralized controller to manage the underlying network resources. In this paper, a joint server and route selection mechanism is proposed for SDN networks, where current and past statistics of network resource utilization are taken into account to allocate a server and a corresponding routing path for each incoming flow. The proposed mechanism periodically and automatically updates the link cost metrics of link state routing protocol to achieve the load balancing in the network. The proof of concept has been implemented using Mininet to emulate OpenFlow enabled switches and OpenDaylight as an SDN controller. Using the real IP traffic, we successfully demonstrated that the proposed system evenly distributes traffic flows among network links and servers.

The Internet of Things (IoT) is expanding rapidly with billions of connected devices worldwide, necessitating robust security solutions to protect these systems. This paper proposes a comprehensive and adaptive security framework called Enhanced Secure Channel Authentication using random forests and software-defined networking (SCAFFOLD), tailored for IoT environments. The framework establishes secure communication channels between IoT nodes using software-defined networking (SDN) and machine learning techniques. The key components include encrypted channels using session keys, continuous traffic monitoring by the SDN controller, ensemble machine-learning for attack detection, precision mitigation via SDN reconfiguration, and periodic reauthentication for freshness. A mathematical model formally defines the protocol. Performance evaluations via extensive simulations demonstrate Enhanced SCAFFOLD’s ability to reliably detect and rapidly mitigate various attacks with minimal latency and energy consumption overheads across diverse IoT network scenarios and traffic patterns. The multidimensional approach combining encryption, intelligent threat detection, surgical response, and incremental hardening provides defense-in-depth to safeguard availability, integrity, and privacy within modern IoT systems while preserving quality of service.

Interest in OpenFlow and software-defined network (SDNs) has resulted in a boom in SDN hardware and controller offerings, with varying degrees of maturity, popularity and support. However, few studies have been conducted to investigate the interaction between SDN hardware and software, as well as its impact on controller design and implementation. In this paper, we chronicle our experience with deploying two commodity SDN controllers and a new system, IronStack, of our own design in a production enterprise network at Cornell University, and describe the lessons learnt. We also report on several practical limitations of SDN and controller technology, and detail important future challenges for SDN adopters and developers.

Software Defined Networking (SDN) is a new networking paradigm, with a great potential to increase network efficiency, ease the complexity of network control and management, and accelerate the rate of technology innovation. One of the core concepts of SDN is the separation of the network's control and data plane. The intelligence and the control of the network operation and management, such as routing, is removed from the forwarding elements (switches) and is concentrated in a logically centralised component, i.e. the SDN controller. In order for the controller to configure and manage the network, it needs to have up-to-date information about the state of the network, in particular its topology. Consequently, topology discovery is a critical component of any Software Defined Network architecture. In this paper, we evaluate the cost and overhead of the de facto standard approach to topology discovery currently implemented by the major SDN controller frameworks, and propose simple and practical modifications which achieve a significantly improved efficiency and reduced control overhead. We have implemented our new topology discovery approach on the widely used POX controller platform, and have evaluated it for a range of network topologies via experiments using the Mininet network emulator. Our results show that our proposed modifications achieve an up to 45% reduction in controller load compared to the current state-of-the-art approach, while delivering identical discovery functionality.

Optical networks are evolving toward self-adaptation exploiting self-observation of network conditions (e.g., through monitoring at the physical layer). Thus, according to monitoring information, reconfiguration of network devices including switches and transponders (e.g., transmission parameters) is acted. Typically, the network is thought as centralized, where a central software defined networking (SDN) controller holds the overall view of network topology and resources and, upon connection request, it decides for routing and spectrum assignment, and, then, it configures accordingly the data plane. Similarly, the central controller receives notifications when failures occur and (re)acts by reconfiguring the data plane. However, centralized solutions can suffer from scalability and responsiveness issues, translating in delays during provisioning and recovery. In this paper, we report the demonstration in a field trial of an automatic and self-reconfigurable network. We leverage a novel control paradigm based on programmable finite state machine where each state is associated to a specific network condition and a state transition implies network reconfiguration. Thanks to this approach the central controller can instruct (pre-program) network devices making them aware of the proper reconfiguration settings associated to the network conditions. Then, the reconfiguration is triggered locally, thus increasing scalability and response speed. Two successful experiments will be shown: first, local transmission parameter adaption according to physical layer conditions; second, connection central frequency optimization mitigating filtering effects and distortions.