Network Control Functions Research Articles

T‐RESIN: Throughput‐aware dynamic resource orchestration for Internet of Everything‐enabled software‐defined edge networks

SummaryIn this work, we address the problem of resource allocation in Internet of Everything (IoE)‐enabled software‐defined edge networks. In the existing literature, the researchers considered optimizing the performance of the software‐defined networking (SDN) platform using a single‐tier architecture, where the Internet of Things (IoT) devices are in the same tier. However, with the advent of edge computing, we can explore the two‐tier architecture of edge networks—local tier and edge tier—in the presence of SDN, which has not been explored. Hence, we propose an evolutionary game‐based resource allocation scheme for software‐defined edge networks. Additionally, we aim to optimize the QoS of the edge‐based IoE services while optimizing the throughput of the system. The IoT devices use the proposed scheme in the local tier to identify the optimized mapping to the SDN switches. On the other hand, in the edge tier, the proposed scheme aims to optimize the throughput while allocating the IoE service to the optimal subset of edge devices. We evaluated the performance of the proposed scheme using the Python3‐based Mininet platform in the presence of Ryu Controller and Open vSwitches. Ryu is an element‐based software defined networking framework, which offers software elements with well‐described API that enables developers to produce new network control functions. We observed that by using T‐RESIN, the network throughput increases by 27.98%–31.84% than using the existing schemes.

Online Corrections to Neural Policy Guidance for Pinpoint Powered Descent

This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables. The proposed approach is to linearize the dynamics around the baseline values of its arguments and then to solve for the corrective input required to transfer the perturbed trajectory to precisely known or desired values at specific time points, in other words, the interim points. Depending on the type of decision variables to adjust, parameter correction and control function correction methods are developed. These incremental correction methods can be used as a means to compensate for the prediction errors of pretrained neural networks in real-time applications where high accuracy of the prediction of dynamical systems at prescribed time points is imperative. In this regard, the online update approach can be useful for enhancing overall targeting accuracy of finite-horizon control subject to point constraints using a neural policy. A numerical example demonstrates the effectiveness of the proposed approach in an application to a powered descent problem on Mars.

Retracted: A Novel Distributed Denial of Service (DDoS) Detection Method in Software Defined Networks

The software Defined Networks (SDN) are the most promising solutions in solving challenges regarding complex network control. The SDNs are responsible for separating network control functions from the network switch. By applying SDNs, communication companies and operators can control and manage networks through one central controller unit therein with no need by applying hardware or the manufacture's guideline. Lack of complex DDoS attack detection, lack of rapid detection sensitivity on packet type attack, lack of methods in promoting detection attack to specific unknown protocols, lack of ability in detecting at switch level, lack of assessing attacks packet types, constitute drawbacks. Although is made in this article to overcome these drawbacks, consequently the GainRatioAttributeEval algorithm and Ranker method are applied. The datasets involved here are the ISCX-SlowDDoS2016, ISCX-IDS2012, CTU-10 and CTU-11.This proposed method is compared with similar methods and it is found that this method with 99.95% accuracy and 0.04 FPR for UNB-ISCX dataset and 99.84% accuracy and 0.1 FPR for CTU-13 dataset is better than others.

Optimizing Telematics Network Performance through Resource Virtualization in a Disruptive Environment: The Case of the IP/MPLS Core Network

We offer a security solution to considerably reduce latency in an IP network by virtualizing the IP/MPLS core network. It consists of adapting a virtualization method to a complex IP network, presenting the simulation of the implementation of this virtualization and the modifications to be made to certain aspects of the code of the solution. These modifications would take into account the key performance indicators of the network in order to guarantee its security and the transmission through very wide bands of data. To do this, we use Software Defined Network (SDN) technology. It allows us to have an emergent, scalable, dynamic, secure, laudable and adaptable network architecture, making it suitable for today's high bandwidth applications and IT services. This architecture decouples network control and digital data transfer functions, making network control directly programmable and the underlying infrastructure abstracted to network applications and services. After describing the soft failover to a virtualized network, we present the new architecture that describes the separation of the control and data planes of the core of the IP/MPLS network of the Autonomous Port of Kribi (PAK) in Cameroon, as part of our research work. We will then present the aspects in which modifying the code would contribute to improving one of the key qualities of service, namely latency in the heart of the network. We go from latencies above 100 ms to latencies below 1 ms; finally we recommend the approach for a continuous modification of the code with a view to optimizing the performance of the network in a continuous process for the reduction of its latency.

A pilot study on improvements in attention function in major depressive disorder after 12 weeks of escitalopram monotherapy or combined treatment with agomelatine.

This study aimed to explore both impairments in attention function in patients with major depressive disorder (MDD) and the efficacy of escitalopram monotherapy or combination therapy with agomelatine. A total of 54 patients with MDD and 46 healthy controls (HCs) were included. Patients were treated with escitalopram for 12 weeks; those who presented with severe sleep impairments were also given agomelatine. Participants were evaluated using the Attention Network Test (ANT), which included tests of alerting, orienting, and executive control networks. Concentration, instantaneous memory, and resistance to information interference were tested using the digit span test, and the logical memory test (LMT) was used to evaluate abstract logical thinking. The Hamilton Depression Rating Scale-17 items, Hamilton Anxiety Rating Scale, and Pittsburgh Sleep Quality Index were used to assess depression, anxiety, and sleep quality, respectively. Patients with MDD were assessed at the end of weeks 0, 4, 8, and 12. HCs were assessed once at baseline. Compared with HCs, patients with MDD showed significantly different alerting, orienting, and executive control functions of attention networks. Treatment with escitalopram alone or combined with agomelatine significantly improved LMT scores at the end of weeks 4, 8, and 12 and restored scores to the level of HCs at the end of week 8. Total Toronto Hospital Test of Alertness scores in patients with MDD increased significantly after 4 weeks of treatment. The ANT executive control reaction time in patients with MDD decreased significantly after 4 weeks of treatment, with this decrease lasting until the end of week 12, but scores did not return to the levels of HCs. Combined treatment with escitalopram and agomelatine led to more improvement in ANT orienting reaction time and was accompanied by a greater reduction of total scores on the Hamilton Depression Rating Scale-17 items and Hamilton Anxiety Rating Scale compared with escitalopram monotherapy. Patients with MDD showed overall impairments in three domains of attention networks as well as the LMT and a test of subjective alertness. Escitalopram monotherapy significantly improved the LMT scores and the executive control function scores in the ANT at the end of the fourth week of treatment, and the improvement was more extensive with combined escitalopram and agomelatine treatment.

An Effective Multi-Agent Ant Colony Optimization Algorithm for QoS-Aware Cloud Service Composition

In order to improve the efficiency of wireless network virtualization resource processing, this paper combines the dynamic resource allocation algorithm to construct a wireless network virtualization resource sharing model. This paper proposes a task-oriented resource management model and uses the task-oriented TRS model to describe resources and service processes, reducing the complexity of formulating resource allocation strategies. Moreover, this paper comprehensively considers factors such as centralized coordination control cost and limited domain topology visibility to improve the dynamic resource allocation algorithm. Through the abstraction and unified representation of network resources, resource sharing, and efficient reuse, the coexistence and integration of heterogeneous wireless networks can be realized. Wireless network virtualization can decouple complex and diverse network management and control functions from hardware and extract them to the upper layer for unified coordination and management, thereby reducing network management costs and improving network management and control efficiency

Research and Application on High Quality Power Supply Technology of New Distribution System

Under the dual-carbon target, the intermittent grid-connection of high-permeability distributed power generation and random charging of large-scale electric vehicles increase the volatility and randomness of the distribution network. Meanwhile, the coupling correlation between the power network, information network and social network will be significantly enhanced. In view of the highly uncertain operation of the new distribution system, under the existing distribution network operation control and operation and maintenance management system, This paper puts forward the realization mode of high quality power supply for the new distribution system from three aspects: the adaptability of the grid, the overall perception ability and the improvement of the operation control ability.

A Security Enforcement Framework for SDN Controller Using Game Theoretic Approach

Software-defined networking (SDN) has gained significant attention as the future deployment platform for the Internet and enterprise networks. The major advantages of SDN include effective traffic management, dynamic configuration of policy and flow rules, and better scalability with heterogeneous traffic requirements. However, centralized network control and the use of OpenFlow protocols introduce various security challenges for the underlying network. The attacks on the SDN controller is critical as it hosts all network control functions. Motivated by a systematic analysis of different attack scenarios in SDN using the STRIDE attack model, this article presents an effective security enforcement framework for proactive prevention of potential attacks on SDN controllers. First, based on a signaling game approach, we design a trust-based controller attack detection (TCAD) model that calculates the trust value of each incoming packet to take necessary action. Next, we propose a risk-based attack prevention (RAP) model that detects and filters malicious traffic flows in the network. Finally, we evaluate our proposed security enforcement framework on different scenarios with varying traffic requirements and by injecting attacks based on the STRIDE model. Experimental results show 95% accuracy in the potential attack detection and prevention.

Toward the Design of an Efficient and Secure System Based on the Software-Defined Network Paradigm for Vehicular Networks

The advent of Intelligent Transport Systems (ITS) has led to the appearance of vehicles increasingly connected to their environment on global road networks. Due to the strict requirements for low latency and secure interactions in a vehicular environment, the proposal of new architectures is a crucial topic for discussion. This paper aims to develop a vehicular network using several access technologies based on SDN (Software Defined Network) paradigm, to take advantage of the capacities of the various access networks and provide flexibility in their control and management. Confidentiality, integrity, and authentication are essential services to prevent an adversary from compromising the security of vehicular networks. Therefore, good security and privacy management system is necessary to ensure this protection. We represent then a hybrid SDN-VANET architecture that can address all of the challenges we mentioned earlier. We are in the process of implementing a dynamic approach to optimize the positioning of controllers according to changes in network topology due to fluctuations in road traffic. We will also detail the topology estimation service based on machine learning techniques to provide network control functions with potential insight into the future state of the network, unlocking proactive and intelligent network control. We also provide a scheme that prevents and informs about basic and compound attacks and reacts to the privacy and security conditions of the vehicular network, managing the requirements of security management systems. The simulation results showed the effectiveness of the proposed schemes in terms of message loss rates, packet delivery rates (PDR), Round Trip Time (RTT), and delays. With used our scheme, the performance of the network is improved when SDN triggers the change of the RSU entity. Such as, we notice that the average RTT is lowered by 68 ms and that the PDR remains around 94%. We also notice with the integration of the security and privacy scheme (SPS) that the performance of the network is improved, the average RTT is reduced by 51 ms and the PDR persists around 99%.

Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybrid technique to recognize denial-of-service (DDoS) attacks that combine deep learning and feedforward neural networks as autoencoders. Two datasets were analyzed for the training and testing model, first statically and then iteratively. The auto-encoding model is constructed by stacking the input layer and hidden layer of self-encoding models’ layer by layer, with each self-encoding model using a hidden layer. To evaluate our model, we use a three-part data split (train, test, and validate) rather than the common two-part split (train and test). The resulting proposed model achieved a higher accuracy for the static dataset, where for ISCX-IDS-2012 dataset, accuracy reached a high of 99.35% in training, 99.3% in validation and 99.99% in precision, recall, and F1-score. for the UNSW2018 dataset, the accuracy reached a high of 99.95% in training, 0.99.94% in validation, and 99.99% in precision, recall, and F1-score. In addition, the model achieved great results with a dynamic dataset (using an emulator), reaching a high of 97.68% in accuracy.

Application of Software-Defined Networking

Software-Defined Networking (SDN) is an architecture purporting to be dynamic, manageable cost-effective, and adaptable, seeking to be suitable for the high bandwidth, dynamic nature of today’s applications. SDN architectures decouple network control and forwarding functions enabling the network control to become directly programmable and the underlying infrastructure to be abstract from application and network services. In this paper, the advantages and challenges of Software-Defined Networking (SDN) are discussed.However, the Software Defined Networking application (SDN App) and the application of SDN are also discussed in detail.

Blockchain-Based Data Dissemination Scheme for 5G-Enabled Softwarized UAV Networks

Unmanned aerial vehicles (UAVs) are widely used in applications such as surveillance, healthcare, rescue, and crowdsensing. However, the network management of these applications is quite challenging, given the mobility of UAVs. In this scenario, the network softwarization, which decouples the hardware from the network control functions, becomes essential. Moreover, the fifth-generation (5G) communication network is used to disseminate the data from controller-to-UAV and UAV-to-Internet of Things (IoT) enabled devices. However, handling the security and privacy of data against the eavesdropper is a challenging aspect that needs to be resolved efficiently. Most of the solutions presented in the literature are based on a centralized architecture having a single point of failure and are also vulnerable to various security attacks such as controller hijacking and man-in-the-middle attacks. Motivated by these facts, we present a blockchain-based secure data dissemination scheme for softwarized UAV networks. The proposed scheme can detect an eavesdropper, analyze malicious data, and mitigate various security attacks on the SDN controller. The performance analysis shows that the proposed scheme achieves superior results in detecting the eavesdropper and anomalous data before adding them into the blockchain compared to other state-of-the-art schemes.

Software-defined networks for resource allocation in cloud computing: A survey

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation.

Software defined networking architecture, traffic management, security, and placement: A survey

Software-defined networking (SDN) is the potential deployment platform for the future Internet and enterprise networks. In SDN, the logical control (programs) and the forwarding logic (data) are separated. Therefore, it allows to configure the network parameters, routing policies effectively based on the requirements of the environment. The control plane (set of controllers) implements network functions such as, load balancing, energy-efficient routing, security. The data plane (set of switches) receives the forwarding logic from control plane and forwards the packets in the network. This in turn enhances the traffic management performance of the network, and strengthens the security perimeter. The researchers have worked on various directions towards the enhancement of network control functions, performance, and security. However, none of those talks about traffic management challenges in integration with security, energy efficiency and performance dependencies.This survey presents an extensive study, analysis and report of state-of-the-art works on effective traffic management including load balancing and energy-efficient routing, SDN control implementation and deployment architecture, controller security and optimal controller placement that affect traffic management. Moreover, this survey discusses a few unexplored problems in SDN such as scalable deployment, integration with legacy network, and future research road maps to these problems. This will help researchers understanding different theoretical, experimental, and applied research in SDN, challenges in these problems; developing effective solutions; and designing future research road maps.

Performance Comparison of File Transfer Protocol Service Between Link State and Distance Vector Routing Protocol in Software Defined Network

Software-Defined Network (SDN) is a new concept in computer networks where the network control function is separated from the data forwarding function (data plane). The control plane and data plane which are separated from each other is the answer for faster, more flexible, and secure internet service. File transfer protocol (FTP) is an internet protocol that runs at the application layer that is used to exchange data between server and client. Open Shortest Path First (OSPF) is a routing protocol for internet networks that belongs to the Interior Gateway Protocol (IGP) group and uses the Link State Routing algorithm (LSR). Routing Information Protocol (RIP) is a type of Distance Vector Routing protocol that is still used today. This study aims to compare the performance of FTP services using OSPF and RIP on SDN networks and conventional networks. The research was conducted with direct implementation on the device with a planned topology. The measurement results show that FTP using OSPF routing has better results than using RIP. FTP that runs on SDN also has a better quality of service value compared to conventional networks.

Controlling Next-Generation Software-Defined RANs

Compared to previous mobile generations, the 5G system architecture offers more flexibility to fulfill a richer set of diverse requirements. Three system aspects play a major role here: support of network slicing, a split between the user plane and the control plane, and a service oriented control architecture. Emerging techniques of network softwarization such as software-defined networking are leveraged to provide this notion of flexibility manifested in a software-defined core network. However, in addition to the 5G core (5GC), analogous concepts could be applied to the next generation radio access network to sustain the required system performance. Driven by the increased attention that is currently paid to a software-defined radio access network, in this article, we propose the radio access network control function (RANCF) as an additional 5G control function. We define the main functionalities of RANCF, and propose interfaces and protocols for the interaction with 5GC functions. Exploiting network slicing as an example, we demonstrate how RANCF facilitates the slice selection and slice management procedure.

A virtual execution platform for OpenFlow controller using NFV

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

Model of the synchronization network functioning process in the context of intellectualization of network control functions

This paper provides a simulation model of the synchronization network (SN) functioning process, which is distinguished by the completeness of accounting for the functioning states. The key indicator of the synchronization network functioning process is the duration of the SN control cycle, which is understood as the time from the moment a failure occurs on the synchronization network until the moment the synchronization signal transmission mode is restored. On the basis of the developed simulation model of the synchronization network functioning process, the duration of the SN control cycle is estimated depending on the time spent in individual states of the SN functioning process and on the characteristics of individual subsystems of the synchronization system. The results obtained can serve as a basis for developing sound strategies and plans for controlling the synchronization network.

Комплекс моделей функционирования и управления пакетной радиосетью КВ-диапазона

The author develop the conceptual control system model to ensure the sustainable projected promising packet radio network HF-band operating. It is proposed to implement the control functions at the Element Management System level on the basis of automated radio centers, and to implement the Network Management System level management functions on the basis of a dedicated server. Such a management approach allows you to build a hierarchical centralized radio network management system architecture. We can observe a simulation modeling for the developed algorithms of the selected radio network control functions. The paper presents the simulation results, namely probabilistic-temporal characteristics of the processes reliability, routing and functioning under attacks.

Distribution Network Operation Control Algorithm for Distributed Data Quality Management System

Distribution Network Operation Control Algorithm for Distributed Data Quality Management System