OpenFlow Network Research Articles

Preserving flow table integrity in OpenFlow networks through smart contract

Preserving flow table integrity in OpenFlow networks through smart contract

QMon: A method to monitor queueing delay in OpenFlow networks

In software-defined networking (SDN), the decoupled architecture provides opportunities for efficiently measuring critical quality of service (QoS) parameters, such as delay. Existing approaches, to dynamically obtain delay, are based around calculating the transit time of a probe packet that travels through the data links. These approaches are not efficient as the probe packet injected into the data plane incurs considerable overhead. Additionally, a separate probe packet is required to measure the delay of each queue if more than one queue is present on the egress port of a switch. Thus, these approaches are not scalable. In this paper, we propose an efficient passive delay estimation method, queueing delay monitoring (qMon), to monitor queueing delay in SDN networks. qMon leverages the OpenFlow protocol to obtain queue statistics from switches at regular intervals, which are further employed to estimate the mean queueing delay for each interval. Thus, the proposed approach differs from the existing approaches as no packet is injected into the data plane to measure delay. The results show that for Poisson traffic and for bursty traffic with large ON intervals, round trip time (RTT) values estimated using qMon and ping utility demonstrate high correlation when the measured RTT value is considered as time-series data.

Reducing Packet-In Messages in OpenFlow Networks

In OpenFlow networks, several packet-in traffic messages are sent to the controller to request routes from any switch on the network. Executing these packet-in messages and replying to control messages may interfere with the controller performance. Therefore, a mechanism for lightening the load on the controller by reducing packet-in traffic between the controller and the switch is proposed in this research. The proposed system operates by responding to the two flow entries for each request and removing packet-in messages from some unused multicast traffic. The proposed system can thus not only avoid a third packet-in message but also some multicast packet-in traffic, thereby reducing the load and traffic in the network as well as packet loss. According to the evaluation results, the proposed system can improve network performance by significantly reducing packet-in overhead.

Penetrating into Openflow Networks: Novel Ddos Attacks in Sdn and Countermeasures

Penetrating into Openflow Networks: Novel Ddos Attacks in Sdn and Countermeasures

Optimized load balancing using software-defined networking (SDN)

In a cloud environment, handling user service requests and providing the requested resources fairly is critical. Load balancing is important to distribute service requests fairly to an unloaded server and to dynamically maintain load balancing across server farms. In conventional internet protocol (IP) networks, maintaining load balancing is a stubborn and non-adaptable task due to the forwarders' lack of global topology representation. Software-Defined Networking (SDN) provides centralized decision-making for any topological changes to manage changes dynamically. To solve the above problem, we propose a new server-side load balancing strategy that provides an efficient and effective server management scheme for SDN Open Flow networks. Experiments were done on the Ryu controller and the Mininet emulator showed that the performance was better than what was already available.

An SDN-Coordinated Steering Framework for Multipath Big Data Transfer Application

Data Transmission is a primary mechanism that can affect the performance of distributed storage systems. The traditional single-path transmission protocols are not efficient to serve several requirements of big data applications. In this paper, we propose an SDN-coordinated steering framework for multipath big data transfer applications. Multipath TCP protocol (MPTCP) coupled with SDN are mainly used for big data transfer in our framework. This framework is useful and cost-effective for OpenFlow networks and overlay networks. To provide a practical multipath transmission scheme for big data transfer applications using MPTCP, we propose a novel OpenFlow-Stats routing algorithm. In our algorithm, a new topology-pruning technique is applied, and the transmission paths are selected based on switch-port statistics. Our proposed framework is implemented and evaluated using the Mininet emulator and ONOS controller. The results show that our routing scheme can reduce the completion time of big data transfer up to 90% compared with the traditional routing with disjoint paths and up to 35% compared with the previous work. Moreover, our proposed routing is more scalable than other previous works in that it can provide lower complexity and system overhead. The results show that our routing scheme produces 57% less overhead compared with the previous work.

Enabling seamless communication over several IoT messaging protocols in OpenFlow network

The most prominent protocols for data transfer in internet of things (IoT) are message queuing telemetry transport (MQTT) and constrained application protocol (CoAP). The existing clients from both sides are unable to communicate directly because of the packet’s header structure difference in application and transport layer. In response, this paper aims to develop a bidirectional conversion server used to translate the specified messaging protocol interchangeably in the OpenFlow network and transmit the converted packet from both sides. The conversion server integrated the MQTT subscriber and CoAP POST object for converting the MQTT message into CoAP data. Similarly, the CoAP-MQTT translation was processed by CoAP GET and MQTT publisher object. The research was evaluated by analysing the round trip time (RTT) value, conversion delay, and power consumption. The RTT value for MQTT-CoAP required 0.5 s while the CoAP-MQTT was accumulated in 0.1 s for single-packet transmission. In addition, the SDN controller and the conversion server only consumed less than 1% central processing unit (CPU) usage during the experiment. The result indicated that the proposed conversion server could handle the translation even though there was an overwhelming request from the clients.

Static Equivalence Checking for OpenFlow Networks

Software-defined networking (SDN) provides many advantages over traditional networking by separating the control and data planes. One of the advantages is to provide programmability, which allows administrators to control the behavior of the network. The network configuration may need to be changed for some reason. Whenever such changes are made, it can be required to verify that the forwarding behavior is preserved from the existing configuration, that is, whether the ruleset is properly reflected. In this paper, we propose the forwarding behavior based equivalence checking of OpenFlow networks. We present the formal definition of the network model and the forwarding behavior of the packet flow. Based on the definition, We present a method for checking the equivalence of OpenFlow network forwarding behaviors. Next, we present the implementation of the proposed method, using the constraint satisfaction method, which will be the basis for further extension.

A proposal of port scan detection method based on Packet‐In Messages in OpenFlow networks and its evaluation

SummaryBy quickly detecting a port scan and blocking the culprit host from the network, it is possible to minimize the spread of the damage by infected hosts and malicious users. In the past, various Software‐Defined Networking (SDN)‐based methods have been proposed, whose main advantage is the lower overhead compared to traditional methods that collect and analyze all captured traffic. On the other hand, due to the polling process used in these methods, it is necessary to set a short interval (e.g., few seconds) to keep the attacks' detection as short as possible. However, when the attack frequency is very low compared to normal traffic, there is an unnecessary overhead. In this paper, we propose a port scan detection method that considers the characteristics of Packet‐In messages sent from the OpenFlow (OF) switch to the controller. This allows a prompt detection and with less overhead than conventional polling methods. The evaluation was conducted using both simulated and real traffic data. Results confirm that the proposed method can detect port scans with lower overhead than existing methods.

GlobeSnap: An Efficient Globally Consistent Statistics Collection for Software-Defined Networks

Software defined networking (SDN) controller requires crucial statistics like flow-wise statistics from the switches to make decisions related to routing, load balancing, and QoS provisioning. These statistics, when viewed across the switches are likely to be inconsistent if a specific order is not enforced while collecting statistics. Collecting consistent statistics requires coordination among all the participating switches. A few approaches in the literature collect globally consistent statistics of a network in the SDN domain. However, these approaches are not time-efficient, robust, and synchronous for OpenFlow based networks. We propose, GlobeSnap, a time-efficient, robust, and synchronous method to collect globally consistent statistics for OpenFlow networks. GlobeSnap collects consistent statistics for all flows in a single round and is therefore, time-efficient. Moreover, GlobeSnap is robust since it resumes the statistics collection process from where it left in case of interruption. GlobeSnap also provides a near-synchronous snapshot of statistics of the switches traversed by a given flow. We also propose a mechanism to persistently store states in OpenFlow based networks using registers, multiple flow tables, and multiple pipelines. We find that GlobeSnap outperforms the state-of-the-art approaches in consistency evaluation. Further we present two use-cases which are sensitive to inconsistent flow statistics, that is, computing packet loss and identifying bottleneck links, to show the time-efficiency, robustness, and synchronicity of GlobeSnap. GlobeSnap provides 100% consistency in OpenFlow based SDN networks. Whereas the existing methods achieve a maximum of 59.89% consistency.

Generation and collection of data for normal and conflicting flows in software defined network flow table

<a name="_Hlk31039004"></a><span lang="EN-US">In terms of network simplification and regulation, Software Defined Networking (SDN) is a new form of infrastructure that offers greater adaptability and flexibility. SDN, however, is an invention that is logically centralized. </span><span>In addition, the optimization of the control plane and data plane in SDN has become an area deserving of more attention. The flow in OpenFlow has been one of the essential parameters in the SDN standards, in which every individual flow includes packet matching fields, flow priority, separate counters, instructions for packet forwarding, flow timeouts and a cookie. This research work is conducted in order to produce and collect flows from the OpenFlow switch in two scenarios; in normal flows and when conflict policy rules are enforced in the network. In this article, the throughput is required to review and evaluate the conflict impact on two protocols as a performance metric; the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) that flows via a forwarded plane. During the simulation of the SDN OpenFlow network, the metrics are tested using MININET. The results demonstrate that the existence of SDN conflict rules allows TCP and UDP to have a significant average change in bandwidth that eventually affects the network and operations performance.</span>

Effective Route Scheme of Multicast Probing to Locate High-loss Links in OpenFlow Networks

With the prevalence of cloud computing and contents delivery networking, OpenFlow-based centrally-managed networks with flexible and dynamic traffic engineering are demanded. To maintain a high quality of service of the network, detecting and locating high-loss links is essential. Therefore, in this paper, a measurement framework is proposed to promptly locate all high-loss links with a minimized load on both data-plane and control-plane incurred by the measurement, which assumes only standard OpenFlow functions. It combines an active measurement by probing multicast packets along a designed route and a passive measurement by collecting flow-stats of the probing flow at selected switch ports in an appropriate sequential order to access switches. In particular, by designing the measurement route based on the backbone-and-branch tree (BBT) route scheme, the measurement accuracy and the measurement overhead (i.e., the number of accesses to switches until locating all high-loss links) can be balanced. The numerical simulation demonstrates the effectiveness of our proposal.

Machine Learning in OpenFlow Network: Comparative Analysis of DDoS Detection Techniques

Software Defined Network (SDN) allows the separation of a control layer and data forwarding at two different layers. However, centralized control systems in SDN is vulnerable to attacks namely Distributed Denial of Service (DDoS). Therefore, it is necessary for developing a solution based on reactive applications that can identify, detect, as well as mitigate the attacks comprehensively. In this paper, an application has been built based on machine learning methods including, Support Vector Machine (SVM) using Linear and Radial Basis Function kernel, K-Nearest Neighbor (KNN), Decision Tree (DTC), Random Forest (RFC), Multi-Layer Perceptron (MLP), and Gaussian Naïve Bayes (GNB). The paper also proposed a new scheme of DDOS dataset in SDN by gathering considerably static data form using the port statistic. SVM became the most efficient method for identifying DDoS attack successfully proved by the accuracy, precision, and recall approximately 100 % which could be considered as the primary algorithm for detecting DDoS. In term of the promptness, KNN had the slowest rate for the whole process, while the fastest was depicted by GNB

Efficient Topology Discovery for Software-Defined Networks

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 De-materialization in Open Flow Network

This review paper presents about the study of De-Materialization of Network known as Network Virtualization in SDN(Software-Defined Networking). It interprets the different paths to network virtualization in SDN. Also defines some contrasting mechanism to allocate resources.

Using learning analytics to understand collective attention in language MOOCs

Learning analytics (LA) has the potential to generate new insights into the complexities of learning behaviours in language massive open online courses (LMOOCs). In LA, the collective attention model takes an ecological system view of the dynamic process of unequal participation patterns in online and flexible learning environments. In this study, the ‘Oral Communication for EFL Learners (spring)’ on XuetangX was selected as an example with which to examine the allocation of learner attention in the context of LMOOCs. The open-flow network of collective attention was used to model the dynamics of learning behaviours to understand how different cohorts of second language (L2) learners allocated their attention at the collective level. The results showed that what distinguished high-performing L2 learners was related less to where they started with LMOOC resources or how much attention they allocated to certain learning units and more to the extent to which their attention could be maintained and circulated into other learning units. In addition, learners’ attention typically followed the pre-designed course structure each time they entered the online space. No learning resources offered in the selected LMOOC were found to dominate the collective attention flow, which suggested that L2 learners’ online engagement followed classroom learning patterns. The use of LA to understand the allocation of L2 attention at the collective level provides new perspectives on digital behaviour in LMOOCs, which may facilitate the design of cost-effective L2 resources that prevent learner overload in the information-rich age.

Flow-based anomaly intrusion detection using machine learning model with software defined networking for OpenFlow network

Moving towards recent technologies, Software Defined Networking (SDN) produces a promising network framework to combine the overall network management system with network programming. It gives a more effective tracking system towards the data center. By centralized system and symmetric controller, it prevents security cracks from creating new threats during OpenFlow packet transmission with vulnerabilities. It creates more interest to the researchers to work towards Flow-based SDN for the priority-driven algorithm in anomaly intruder detection. In this paper, we made a study towards a priority-based model using SDN to control the flow of data packets over the network, gives assurance to the bandwidth enforcement, and reallocation is made through virtual circuits. The network behavior of the system is continuously monitored through the machine learning model for normal and abnormal traffic data transmission to detect anomaly intruders. Flow-based machine learning (ML) model with SDN act as an intelligent system to limits the throughput virtually through the flow of reserved bandwidth and make use of extra bandwidth, which presents more than the utilization bandwidth for priority-based applications with minimal cost while compared with the traditional methods. The proposed work also compared with the schemes available at the network to produce outcomes with fast routing and the fault tolerance of existing networks to overcome the gap open at the security of the SDN architecture to detect and identify vulnerabilities.

The impact of firewall on TCP and UDP throughput in an openflow software defined network

Software Defined Networking (SDN) is an emerging networking paradigm that provides more flexibility and adaptability in terms of network definition and control. However, SDN is a logically centralized technology. Therefor the control plane (i.e. controller) scalability in SDN in particular, is also one of the problems that needs further focus. OpenFlow is one of the protocol standards in SDN, which allow the separation of the controller from the forwarding plane. The control plane has an SDN embedded firewall and is able to enforce and monitor the network activity. This firewall can be used to control the throughput. However, it may affect SDN performance. In this paper, throughput will be used as a performance metric to evaluate and assess the firewall impact on two protocols; Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) that passes through the forwarding planes. The evaluations have been verified through simulating the SDN OpenFlow network using MININET. The results show that an implementation of firewall module in SDN creates a significant 36% average drop for TCP and 87% average drop for UDP in the bandwidth which eventually affect the quality of the network and applications.

Construction of Distributed Optoelectronic Switching Network and OpenFlow Anycast Mechanism of Network on Cloud Computing

Traditional electrical switching network faces the problems of limited bandwidth and large energy consumption. The optoelectronic hybrid data network fully combines the advantages of optical switching and electrical switching, and dynamically adjusts the topology and bandwidth in accordance to the demand, thereby effectively improving network performance and reducing power consumption. A high-performance distributed optoelectronic switching network architecture called DOIN_W was proposed in the study, aiming to solve the problems of the scalability of the optoelectronic switching network architecture and the insufficient flexibility of network resource allocation. The architecture was connected topologically according to the method of 2DTorus, thereby effectively improving the network scale of the photoelectric switching network and supporting the dynamic adjustment of the network scale. The optical switching of DOIN_W adopted the "broadcastselect" method to support different forms of broadcasting communication. A multi-dimensional optical signal switch was designed, and the optical signal broadcast of the switch can reach any of the optical switches in DOIN_W, thereby supporting the direct connection between optical switches. Considering the problems that the multi-broadcast service of DOIN_W was unable to access freely, stateful optical signal communication was hard to maintain and the single path weight resulted in lack priority of communication, the OpenFlow network protocol of the cloud computing network was introduced in the DOIN_W architecture. The anycast mechanism of DOIN_W was optimized based on this protocol. Based on the stream-level network simulator for network performance simulation analysis, the DOIN_W architecture can meet the requirements of network scalability and flexibility. Compared with the OVS architecture and Jellyfish architecture, the DOIN_W architecture can effectively reduce the average flow completion time by more than 30% and reduce average energy consumption by over 25%.

PPNet

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.