Contrastive Analysis of Software Networks Based on Different Coupling Relationships

Several software network models are constructed based on the relationships between classes in the object-oriented software systems.Then,a variety of well-known open source software applications are statistically analyzed by using these models.The results show that: (1) Dependency network does play a key role in software architecture;(2) The exponents of in-degree and total-degree distribution functions of different networks differ slightly,while the exponent of out-degree varies obviously;(3) Weak-coupling relationships have greater impact on software architecture than strong-coupling relationships.Finally,a theoretically analysis on these statistical phenomena is proposed from the perspectives of software develop technology,develop process and developer’s habits,respectively.

Research on the Controllability of Software Networks Based on the Source-Driven Model

The complexity of software system has been increasing with software evolution, which affects the stability of software structure. Most of the existing measurement methods focus on the analysis of the macro-characteristics of the network topology, but lacked a certain depth and expansion to explore the nature of the complexity of the software structure, for this purpose, the complex network control theory was applied to the study of software network controllability. Firstly, the Source-Driver (SD) model was established based on the system control theory, the driver node sets were obtained by the minimum input theorem in the control process of software network topology;Then the relationship between the degree and center degree, the relationship between the in-degree and the out-degree of the software network topology were further analyzed owing to the non-uniqueness of the driver node sets;Finally, the values of the four indicators in the software system were compared. Experimental results show that the driver node sets in the software networks are mainly composed of nodes with low degree values, but it does not mean that the nodes whose in-degree values and out-degree values are also low;The action on control nodes and driver nodes are not random, the controllability of the driver nodes is closely related to the in-degree, when selecting the driver node sets, the network topology characteristics should be considered comprehensively, and the nodes with high degree and center degree are the first choice. The results have important guiding significance for the control, maintenance and redesign of software architecture.

Software defined satellite networks:A survey

In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.

Artificial Intelligence Based Smart Routing in Software Defined Networks

In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the network.Software Defined Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible uni-versally.The various features of routing are supported by the information centric network,which minimizes the congestion in the dataflow in a network and pro-vides the content awareness through its mined mastery.Due to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-defined networks.Although there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in SDN.In this regard,artificial intelligence meth-odology has been used in the proposed approach to solve these difficulties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with routing.The novel aligned internet investigation technique has been proposed to process the deep reinforcement learning.The performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Modeling and Dynamic Analysis in Software Systems Based on Complex Networks

A software network model with multiple links is constructed on the basis of a dynamical model of a general complex network with mukiple links. The principle of network division of multiple links is introduced. Following these principles, the software network model is decomposed into three types of subnets and different relationships between classes are revealed. Then, the dynamic analysis of software networks is presented. A sufficient condition for the stability of general complex networks is obtained followed by that of software networks. Finally, the dynamics of an open-source software system is analyzed, and their simulations are provided to demonstrate the effectiveness of the presented model.

Measuring Structural Quality of Object-Oriented Softwares via Bug Propagation Analysis on Weighted Software Networks

The quality of a software system is partially determined by its structure（topological structure）,so the need to quantitatively analyze the quality of the structure has become eminent.In this paper a novel metric called software quality of structure（SQoS） is presented for quantitatively measuring the structural quality of object-oriented（OO） softwares via bug propagation analysis on weighted software networks（WSNs）.First,the software systems are modeled as a WSN,weighted class dependency network（WCDN）,in which classes are nodes and the interaction between every pair of classes if any is a directed edge with a weight indicating the probability that a bug in one class will propagate to the other.Then we analyze the bug propagation process in the WCDN together with the bug proneness of each class,and based on this,a metric（SQoS） to measure the structural quality of OO softwares as a whole is developed.The approach is evaluated in two case studies on open source Java programs using different software structures（one employs design patterns and the other does not） for the same OO software.The results of the case studies validate the effectiveness of the proposed metric.The approach is fully automated by a tool written in Java.

The Impact of Delay in Software-Defined Integrated Terrestrial-Satellite Networks

Satellite communication networks have been evolving from standalone networks with ad-hoc infrastructures to possibly interconnected portions of a wider Future Internet architecture. Experts belonging to the fifth-generation(5 G) standardization committees are considering satellites as a technology to integrate in the 5 G environment. Software Defined Networking(SDN) is one of the paradigms of the next generation of mobile and fixed communications. It can be employed to perform different control functionalities, such as routing, because it allows traffic flow identification based on different parameters and traffic flow management in a centralized way. A centralized set of controllers makes the decisions and sends the corresponding forwarding rules for each traffic flow to the involved intermediate nodes that practically forward data up to the destination. The time to perform this process in integrated terrestrial-satellite networks could be not negligible due to satellite link delays. The aim of this paper is to introduce an SDN-based terrestrial satellite network architecture and to estimate the mean time to deliver the data of a new traffic flow from the source to the destination including the time required to transfer SDN control actions. The practical effect is to identify the maximum performance than can be expected.

Service Function Chain in Small Satellite-Based Software Defined Satellite Networks

Software Defined Satellite Networks(SDSN) are proposed to solve the problems in traditional satellite networks, such as time-consuming configuration and inflexible traffic scheduling. The emerging application of small satellite and research of SDSN make it possible for satellite networks to provide flexible network services. Service Function Chain(SFC) can satisfy this need. In this paper, we are motivated to investigate applying SFC in the small satellite-based SDSN for service delivery. We introduce the structure of the multi-layer constellation-based SDSN. Then, we describe two deployment patterns of SFC in SDSN, the Multi-Domain(MD) pattern and the Satellite Formation(SF) pattern. We propose two algorithms, SFP-MD, and SFP-SF, to calculate the Service Function Path(SFP). We implement the algorithms and conduct contrast experiments in our prototype. Finally, we summarize the applicable conditions of two deployment patterns according to the experimental results in terms of hops, delay, and packet loss rate.

A PAIR OF COUPLING METRICS FOR SOFTWARE NETWORKS

Complex networks are everywhere. A typical example is software network. How to measure and control coupling interactions of software components is a largely explored research problem in software network. In terms of graph theory and linear algebra, this paper investigates a pair of coupling metrics to evaluate coupling interactions between the classes of object-oriented systems. These metrics differ from the majority of existing metrics in three aspects： Taking into account the strength that one class depends on other ones, reflecting indirect coupling, and distinguishing various coupling interaction. An empirical comparison of the novel measures with one of the most widely used coupling metrics is described. Specifically, an experiment about the relationships of this pair metrics is conducted. The result shows that software complexity derived from coupling interaction could not be accurately reflected by one dimension of coupling metric for negative correlation.

Software Defined Optical Networks and Its Innovation Environment

Software defined optical networks （SDONs） integrate software defined technology with optical communication networks and represent the promising development trend of future optical networks. The key technologies for SDONs include software-defined optical transmission, switching, and networking. The main features include control and transport separation, hard-ware universalization, protocol standardization, controllable optical network, and flexible optical network applications. This paper introduces software defined optical networks and its innovation environment, in terms of network architecture, protocol extension solution, experiment platform and typical applications. Batch testing has been conducted to evaluate the performance of this SDON testbed. The results show that the SDON testbed has good scalability in different sizes. Meanwhile, we notice that controller output bandwidth has great influence on lightpath setup delay.

DDoS Attack in Software Defined Networks: A Survey

Distributed Denial of Service(DDoS) attacks have been one of the most destructive threats to Internet security. By decoupling the network control and data plane, software defined networking(SDN) offers a flexible network management paradigm to solve DDoS attack in traditional networks. However, the centralized nature of SDN is also a potential vulnerability for DDo S attack. In this paper, we first provide some SDN-supported mechanisms against DDoS attack in traditional networks. A systematic review of various SDN-self DDo S threats are then presented as well as the existing literatures on quickly DDoS detection and defense in SDN. Finally, some promising research directions in this field are introduced.

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

In light of the escalating demand and intricacy of services in contemporary terrestrial,maritime,and aerial combat operations,there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks.Software-Defined Networking(SDN)proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts,due to its intrinsic ability to flexibly allocate and centrally administer network resources.This study pivots around the optimization of SDN controller deployment within airborne data link clusters.A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed.Within this architectural framework,the controller deployment issue is reframed as a two-fold problem:subdomain partition-ing and central interaction node selection.We advocate a subdomain segmentation approach grounded in node value ranking(NDVR)and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm(AFSA).The advanced NDVR-AFSA(Node value ranking-Improved artificial fish swarm algorithm)algorithm makes use of a chaos algorithm for population initialization,boosting population diversity and circumventing premature algorithm convergence.By the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations,the algorithm’s search range adaptability is enhanced,thereby increasing the possibility of obtaining globally optimal solutions,while concurrently augmenting cluster reliability.The simulation results verify the advantages of the NDVR-IAFSA algorithm,achieve a better load balancing effect,improve the reliability of aviation data link cluster,and significantly reduce the average propagation delay and disconnection rate,respectively,by 12.8%and 11.7%.This shows that the optimization scheme has important significance in practical application,and can meet the high requirements of modern sea,land,and air operations to aviation airborne communication networks.

Mitigating while Accessing:A Lightweight Defense Framework Against Link Flooding Attacks in SDN

Link flooding attack(LFA)is a type of covert distributed denial of service(DDoS)attack.The attack mechanism of LFAs is to flood critical links within the network to cut off the target area from the Internet.Recently,the proliferation of Internet of Things(IoT)has increased the quantity of vulnerable devices connected to the network and has intensified the threat of LFAs.In LFAs,attackers typically utilize low-speed flows that do not reach the victims,making the attack difficult to detect.Traditional LFA defense methods mainly reroute the attack traffic around the congested link,which encounters high complexity and high computational overhead due to the aggregation of massive attack traffic.To address these challenges,we present an LFA defense framework which can mitigate the attack flows at the border switches when they are small in scale.This framework is lightweight and can be deployed at border switches of the network in a distributed manner,which ensures the scalability of our defense system.The performance of our framework is assessed in an experimental environment.The simulation results indicate that our method is effective in detecting and mitigating LFAs with low time complexity.

Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data centers.Modern TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow rerouting.In this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance deterioration.We look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a solution.Simulations are used to assess the proposed NUFTCP.Our findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

Multipath Selection Algorithm Based on Dynamic Flow Prediction

Traditional traffic management techniques appear to be incompetent in complex data center networks, so proposes a load balancing strategy based on Long Short-Term Memory (LSTM) and quantum annealing by Software Defined Network (SDN) to dynamically predict the traffic and comprehensively consider the current and predicted load of the network in order to select the optimal forwarding path and balance the network load. Experiments have demonstrated that the algorithm achieves significant improvement in both system throughput and average packet loss rate for the purpose of improving network quality of service.

A Dormant Multi-Controller Model for Software Defined Networking

In order to improve the scalability and reliability of Software Defined Networking(SDN),many studies use multiple controllers to constitute logically centralized control plane to provide load balancing and fail over.In this paper,we develop a flexible dormant multi-controller model based on the centralized multi-controller architecture.The dormant multi-controller model allows part of controllers to enter the dormant state under light traffic condition for saving system cost.Meanwhile,through queueing analysis,various performance measures of the system can be obtained.Moreover,we analyze the real traffic of China Education Network and use the results as the parameters of computer simulation and verify the effects of parameters on the system characteristics.Finally,a total expected cost function is established,and genetic algorithm is employed to find the optimal values of various parameters to minimize system cost for the deployment decision making.