IPv6 is the foundation of the development of Next Generation Internet (NGI). An IPv6 network management and operations support system is necessary for real operable NGI. Presently there are no approved standards yet a...IPv6 is the foundation of the development of Next Generation Internet (NGI). An IPv6 network management and operations support system is necessary for real operable NGI. Presently there are no approved standards yet and relevant equipment interfaces are not perfect. A Network Management System (NMS) at the network layer helps implement the integrated management of a network with equipment from multiple vendors, including the network resources and topology, end-to-end network performance, network failures and customer Service Level Agreement (SLA) management. Though the NMS will finally realize pure IPv6 network management, it must be accommodated to the management of relevant IPv4 equipment. Therefore, modularized and layered structure is adopted for the NMS in order to implement its smooth transition.展开更多
The power communication network is a separate network from the power grid whose primary purpose is to ensure the power grid's safe operation.This paper expounds the composition of the comprehensive network managem...The power communication network is a separate network from the power grid whose primary purpose is to ensure the power grid's safe operation.This paper expounds the composition of the comprehensive network management architecture of the power communication data network and the implementation of the data acquisition module in the network management system through theoretical analysis,for the reference of relevant personnel,in order to better promote the collection of power grid communication network data.展开更多
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 ne...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.展开更多
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 ...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.展开更多
Active network management(ANM)of electricity distribution networks include many complex stochastic sequential optimization problems.These problems need to be solved for integrating renewable energies and distributed s...Active network management(ANM)of electricity distribution networks include many complex stochastic sequential optimization problems.These problems need to be solved for integrating renewable energies and distributed storage into future electrical grids.In this work,we introduce Gym-ANM,a framework for designing reinforcement learning(RL)environments that model ANM tasks in electricity distribution networks.These environments provide new playgrounds for RL research in the management of electricity networks that do not require an extensive knowledge of the underlying dynamics of such systems.Along with this work,we are releasing an implementation of an introductory toy-environment,ANM6-Easy,designed to emphasize common challenges in ANM.We also show that state-of-the-art RL algorithms can already achieve good performance on ANM6-Easy when compared against a model predictive control(MPC)approach.Finally,we provide guidelines to create new Gym-ANM environments differing in terms of(a)the distribution network topology and param-eters,(b)the observation space,(c)the modeling of the stochastic processes present in the system,and(d)a set of hyperparameters influencing the reward signal.Gym-ANM can be downloaded at https://github.com/robinhenr y/gym-anm.展开更多
With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and security.In recent years,packet sampling has been widely used in most network management...With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and security.In recent years,packet sampling has been widely used in most network management systems.In this paper,in order to improve the accuracy of network traffic identification,sampled NetFlow data is applied to traffic identification,and the impact of packet sampling on the accuracy of the identification method is studied.This study includes feature selection,a metric correlation analysis for the application behavior,and a traffic identification algorithm.Theoretical analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling environment.Meanwhile,in this paper,the correlation analysis results in different trends according to different features.However,as long as the flow number meets the statistical requirement,the feature selection and the correlation degree will be independent of the sampling ratio.While in a high sampling ratio,where the effective information would be less,the identification accuracy is much lower than the unsampled packets.Finally,in order to improve the accuracy of the identification,we propose a Deep Belief Networks Application Identification(DBNAI)method,which can achieve better classification performance than other state-of-the-art methods.展开更多
This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provid...This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.展开更多
The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures.Consequently,the arrival of multiple management solutions to cope with this explosio...The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures.Consequently,the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure.The Zero-touch Network and Service Management(ZSM)concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience(QoE)demanded by users.Machine Learning(ML)is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system.This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance.To this end,the main related standardization activities and the aligned international projects and research efforts are deeply examined.From this dissection,the skyrocketing growth of the ZSM paradigm can be observed.Concretely,different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration.Aligned with these advances,diverse ML techniques are being currently exploited to build further ZSM developments in different aspects,including multi-tenancy management,traffic monitoring,and architecture coordination,among others.However,different challenges,such as the complexity,scalability,and security of ML mechanisms,are also identified,and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.展开更多
The trend of economic globalisation and advances in i nformation technology has led to the emergence of dispersed manufacturing system s as a form of the virtual organisation. New manufacturing strategy pays more at t...The trend of economic globalisation and advances in i nformation technology has led to the emergence of dispersed manufacturing system s as a form of the virtual organisation. New manufacturing strategy pays more at tention to the management of the total value chain and therefore puts emphasis o n outsourcing. In fact, outsourcing is an efficient way of utilizing available r esources and has become one key aspect of the manufacturing strategy. Improved d ecision and organization on outsourcing will result in cost production and short er lead-times. However, most concepts and practice of traditional outsourcing do not adapt to t he changing environment and meet increasing performance requirements. On the oth er hand, virtual organisations might display instability between pure outsourcin g and establishing alliance. Balance and trade-off between independent agents a nd creating alliance are thus required. Therefore, the purpose of this paper is to develop a model to support decision-making, management and control on outsou rcing in a dispersed network manufacturing system and to discuss several key iss ues that are relevant to the relationship between the agents of the network. Dev elopment of the model will deploy Applied System Theory and will be built on fou ndations of earlier research on industrial management such the theories of Outso urcing, Order Entry Points, Design of Organisations and Logistic Control. The is sues that will be addressed in this paper are: · The selection of suppliers and co-makers; · Communication between suppliers and clients; · The mechanisms for profit-sharing between agents; · The product data management to integrate the knowledge of the different agent s into product design. Industrial companies will benefit from this research by the practical methods an d implementation extending their business models beyond concepts for outsourcing and alliances. Additionally, the exploration will lead to proactive contributio n of manufacturing during engineering, which would improve management and contro l of dispersed manufacturing systems.展开更多
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
To manage and orchestrate Network Slices (NSs) for 5G Core (5GC), the MANO (MANagement and Orchestration) framework is proposed by European Telecommunications Standard Institute (ETSI). In most research testbeds, MANO...To manage and orchestrate Network Slices (NSs) for 5G Core (5GC), the MANO (MANagement and Orchestration) framework is proposed by European Telecommunications Standard Institute (ETSI). In most research testbeds, MANO systems such as Tacker, OSM and ONAP are used to initiate network slices. However, this doesn’t comply with the 3GPP 5G standards as MANO should only be responsible for dynamic management of NSs, and the static management such as provisioning or unprovisioning a network slice should be left to OSS/BSS (Operation/Business Support System). Thus, in our testbed, an integrated architecture was designed in which the management of network slices will be coordinated by both MANO and OSS/BSS. MANO would handle on-boarding, instantiating, scaling and terminating of network slices while OSS/BSS is responsible for static management of slices including provisioning and unprovisioning of network slices. To evaluate our system, it was compared with the management systems equipped with only OSS/BSS or MANO in order to analyze the shortfalls of those systems when used to deploy network slices. Through this analysis, this research confirms the necessity of applying both OSS/BSS and MANO for the coordinated management of 5G core slices as adopted by 3GPP.展开更多
The Internet plays increasingly important roles in everyone's life; however, the existence of a mismatch between the basic architectural idea beneath the Internet and the emerging requirements for it is becoming m...The Internet plays increasingly important roles in everyone's life; however, the existence of a mismatch between the basic architectural idea beneath the Internet and the emerging requirements for it is becoming more and more obvious. Although the Internet community came up with a consensus that the future network should be trustworthy, the concept of "trustworthy networks" and the ways leading us to a trustworthy network are not yet clear. This research insists that the security, controllability, manageability, and survivability should be basic properties of a trustworthy network. The key ideas and techniques involved in these properties are studied, and recent developments and progresses are surveyed. At the same time, the technical trends and challenges are briefly discussed. The network trustworthiness could and should be eventually achieved.展开更多
Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node ...Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.展开更多
Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent i...Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer(ChatGPT)has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays,the exploration of network resource management,control,and operation is still in the initial stage.In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic(NetLM),a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics.The continual convergence of knowledge space and artificial intelligence(AI)technologies constitutes the core of intelligent network management and control.Multi-modal representation learning is used to unify the multi-modal information of network indicator data,traffic data,and text data into the same feature space.Furthermore,a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels.Finally,some potential cases are provided that NetLM can benefit the telecom industry.展开更多
A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node ag...A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node agents. Considering the resource limitations on the sensor nodes, we design new management messages, new data types and new management information base completely. The management messages between the cluster proxy and node agents are delivered as normal data packets. The experiment results show that LW-SNMP can meet the management demands in the resource-limited wireless sensor networks and has a good performance in stability, effectiveness of memory, extensibility than the traditional Simple Network Management Protocol (SNMP).展开更多
Achieving high programmability has become an essential aim of network research due to the ever-increasing internet traffic.Software-Defined Network(SDN)is an emerging architecture aimed to address this need.However,ma...Achieving high programmability has become an essential aim of network research due to the ever-increasing internet traffic.Software-Defined Network(SDN)is an emerging architecture aimed to address this need.However,maintaining accurate knowledge of the network after a failure is one of the largest challenges in the SDN.Motivated by this reality,this paper focuses on the use of self-healing properties to boost the SDN robustness.This approach,unlike traditional schemes,is not based on proactively configuring multiple(and memory-intensive)backup paths in each switch or performing a reactive and time-consuming routing computation at the controller level.Instead,the control paths are quickly recovered by local switch actions and subsequently optimized by global controller knowledge.Obtained results show that the proposed approach recovers the control topology effectively in terms of time and message load over a wide range of generated networks.Consequently,scalability issues of traditional fault recovery strategies are avoided.展开更多
This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen ...This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.展开更多
The inability to achieve the target of universal access to electricity is influenced by several factors including funding limitations, the use of obsolete equipment, power theft, and system losses confronting the elec...The inability to achieve the target of universal access to electricity is influenced by several factors including funding limitations, the use of obsolete equipment, power theft, and system losses confronting the electricity distribution services of the Electricity Company of Ghana Limited (ECG). The study assessed the components of system losses within the ECG by determining and computing the percentage of system losses within ECG, examining the causes of both commercial and technical losses in ECG, and determining ways to improve energy efficiency by reducing system losses in the most cost-efficient manner. The study adopted deductive reasoning and a quantitative approach to guide data collection and analysis of the research output. A sample of 345 technical and non-technical staff of ECG in the Greater Accra Metropolis was selected from a population of 2500. Purposive, simple random, and cluster sampling techniques were used in identifying and accessing respondents for the study. Descriptive statistics were applied to measure central tendency and degrees of dispersion and the Relative Importance Index (RII) to predict criterion and predictor variables. The impact of low voltage network losses can adversely contribute to technical losses (20%) and reduce energy efficiency in power or electricity distribution companies. Non-technical losses are mainly caused by illegal connections, meter problems, and billing problems. Each of the non-technical losses contributes a maximum of 10% to system losses. Contributors to system losses at ECG are ranked first for power theft and least for lack of incentives. System losses at ECG include metering inaccuracies, bad workmanship, unmetered supply, and lengthy distribution lines, each recording a mean value of above 3.5. Measures to improve monitoring of the networks and systems at ECG and discourage power theft should include an extensive quantification, patrolling, and inspection of the entire network to assess the extent of the network and conditions relevant for the placement of systematically planned maintenance programmes.展开更多
Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone...Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.展开更多
文摘IPv6 is the foundation of the development of Next Generation Internet (NGI). An IPv6 network management and operations support system is necessary for real operable NGI. Presently there are no approved standards yet and relevant equipment interfaces are not perfect. A Network Management System (NMS) at the network layer helps implement the integrated management of a network with equipment from multiple vendors, including the network resources and topology, end-to-end network performance, network failures and customer Service Level Agreement (SLA) management. Though the NMS will finally realize pure IPv6 network management, it must be accommodated to the management of relevant IPv4 equipment. Therefore, modularized and layered structure is adopted for the NMS in order to implement its smooth transition.
文摘The power communication network is a separate network from the power grid whose primary purpose is to ensure the power grid's safe operation.This paper expounds the composition of the comprehensive network management architecture of the power communication data network and the implementation of the data acquisition module in the network management system through theoretical analysis,for the reference of relevant personnel,in order to better promote the collection of power grid communication network data.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number(DSR2022-RG-0102).
文摘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.
基金extend their appreciation to Researcher Supporting Project Number(RSPD2023R582)King Saud University,Riyadh,Saudi Arabia.
文摘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.
文摘Active network management(ANM)of electricity distribution networks include many complex stochastic sequential optimization problems.These problems need to be solved for integrating renewable energies and distributed storage into future electrical grids.In this work,we introduce Gym-ANM,a framework for designing reinforcement learning(RL)environments that model ANM tasks in electricity distribution networks.These environments provide new playgrounds for RL research in the management of electricity networks that do not require an extensive knowledge of the underlying dynamics of such systems.Along with this work,we are releasing an implementation of an introductory toy-environment,ANM6-Easy,designed to emphasize common challenges in ANM.We also show that state-of-the-art RL algorithms can already achieve good performance on ANM6-Easy when compared against a model predictive control(MPC)approach.Finally,we provide guidelines to create new Gym-ANM environments differing in terms of(a)the distribution network topology and param-eters,(b)the observation space,(c)the modeling of the stochastic processes present in the system,and(d)a set of hyperparameters influencing the reward signal.Gym-ANM can be downloaded at https://github.com/robinhenr y/gym-anm.
基金supported by Key Scientific and Technological Research Projects in Henan Province(Grand No 192102210125)Key scientific research projects of colleges and universities in Henan Province(23A520054)Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2020-2-01).
文摘With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and security.In recent years,packet sampling has been widely used in most network management systems.In this paper,in order to improve the accuracy of network traffic identification,sampled NetFlow data is applied to traffic identification,and the impact of packet sampling on the accuracy of the identification method is studied.This study includes feature selection,a metric correlation analysis for the application behavior,and a traffic identification algorithm.Theoretical analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling environment.Meanwhile,in this paper,the correlation analysis results in different trends according to different features.However,as long as the flow number meets the statistical requirement,the feature selection and the correlation degree will be independent of the sampling ratio.While in a high sampling ratio,where the effective information would be less,the identification accuracy is much lower than the unsampled packets.Finally,in order to improve the accuracy of the identification,we propose a Deep Belief Networks Application Identification(DBNAI)method,which can achieve better classification performance than other state-of-the-art methods.
文摘This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.
基金This work has been supported by Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia-under the FPI Grant 21429/FPI/20,and co-funded by Odin Solutions S.L.,Región de Murcia(Spain)the Spanish Ministry of Science,Innovation and Universities,under the projects ONOFRE 3(Grant No.PID2020-112675RB-C44)+1 种基金5GHuerta(Grant No.EQC2019-006364-P)both with ERDF fundsthe European Commission,under the INSPIRE-5Gplus(Grant No.871808)project.
文摘The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures.Consequently,the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure.The Zero-touch Network and Service Management(ZSM)concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience(QoE)demanded by users.Machine Learning(ML)is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system.This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance.To this end,the main related standardization activities and the aligned international projects and research efforts are deeply examined.From this dissection,the skyrocketing growth of the ZSM paradigm can be observed.Concretely,different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration.Aligned with these advances,diverse ML techniques are being currently exploited to build further ZSM developments in different aspects,including multi-tenancy management,traffic monitoring,and architecture coordination,among others.However,different challenges,such as the complexity,scalability,and security of ML mechanisms,are also identified,and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.
文摘The trend of economic globalisation and advances in i nformation technology has led to the emergence of dispersed manufacturing system s as a form of the virtual organisation. New manufacturing strategy pays more at tention to the management of the total value chain and therefore puts emphasis o n outsourcing. In fact, outsourcing is an efficient way of utilizing available r esources and has become one key aspect of the manufacturing strategy. Improved d ecision and organization on outsourcing will result in cost production and short er lead-times. However, most concepts and practice of traditional outsourcing do not adapt to t he changing environment and meet increasing performance requirements. On the oth er hand, virtual organisations might display instability between pure outsourcin g and establishing alliance. Balance and trade-off between independent agents a nd creating alliance are thus required. Therefore, the purpose of this paper is to develop a model to support decision-making, management and control on outsou rcing in a dispersed network manufacturing system and to discuss several key iss ues that are relevant to the relationship between the agents of the network. Dev elopment of the model will deploy Applied System Theory and will be built on fou ndations of earlier research on industrial management such the theories of Outso urcing, Order Entry Points, Design of Organisations and Logistic Control. The is sues that will be addressed in this paper are: · The selection of suppliers and co-makers; · Communication between suppliers and clients; · The mechanisms for profit-sharing between agents; · The product data management to integrate the knowledge of the different agent s into product design. Industrial companies will benefit from this research by the practical methods an d implementation extending their business models beyond concepts for outsourcing and alliances. Additionally, the exploration will lead to proactive contributio n of manufacturing during engineering, which would improve management and contro l of dispersed manufacturing systems.
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
文摘To manage and orchestrate Network Slices (NSs) for 5G Core (5GC), the MANO (MANagement and Orchestration) framework is proposed by European Telecommunications Standard Institute (ETSI). In most research testbeds, MANO systems such as Tacker, OSM and ONAP are used to initiate network slices. However, this doesn’t comply with the 3GPP 5G standards as MANO should only be responsible for dynamic management of NSs, and the static management such as provisioning or unprovisioning a network slice should be left to OSS/BSS (Operation/Business Support System). Thus, in our testbed, an integrated architecture was designed in which the management of network slices will be coordinated by both MANO and OSS/BSS. MANO would handle on-boarding, instantiating, scaling and terminating of network slices while OSS/BSS is responsible for static management of slices including provisioning and unprovisioning of network slices. To evaluate our system, it was compared with the management systems equipped with only OSS/BSS or MANO in order to analyze the shortfalls of those systems when used to deploy network slices. Through this analysis, this research confirms the necessity of applying both OSS/BSS and MANO for the coordinated management of 5G core slices as adopted by 3GPP.
基金the National Key BasicResearch Program (973 Program) under Grant2007CB307104.
文摘The Internet plays increasingly important roles in everyone's life; however, the existence of a mismatch between the basic architectural idea beneath the Internet and the emerging requirements for it is becoming more and more obvious. Although the Internet community came up with a consensus that the future network should be trustworthy, the concept of "trustworthy networks" and the ways leading us to a trustworthy network are not yet clear. This research insists that the security, controllability, manageability, and survivability should be basic properties of a trustworthy network. The key ideas and techniques involved in these properties are studied, and recent developments and progresses are surveyed. At the same time, the technical trends and challenges are briefly discussed. The network trustworthiness could and should be eventually achieved.
文摘Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.
基金This work was supported by the National Natural Science Foundation of China under Grants of 62071067,62101064,62201072,62171057,and 62001054,Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer(ChatGPT)has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays,the exploration of network resource management,control,and operation is still in the initial stage.In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic(NetLM),a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics.The continual convergence of knowledge space and artificial intelligence(AI)technologies constitutes the core of intelligent network management and control.Multi-modal representation learning is used to unify the multi-modal information of network indicator data,traffic data,and text data into the same feature space.Furthermore,a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels.Finally,some potential cases are provided that NetLM can benefit the telecom industry.
基金supported by the Fundamental Research Funds for the Central Universities under grant No.2009JBM007supported by the National Natural Science Foundation of China under Grants No. 60802016, 60833002 and 60972010
文摘A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node agents. Considering the resource limitations on the sensor nodes, we design new management messages, new data types and new management information base completely. The management messages between the cluster proxy and node agents are delivered as normal data packets. The experiment results show that LW-SNMP can meet the management demands in the resource-limited wireless sensor networks and has a good performance in stability, effectiveness of memory, extensibility than the traditional Simple Network Management Protocol (SNMP).
基金This work has been supported by the Ministerio de Economía y Competitividad of the Spanish Government under project TEC2016-76795-C6-1-R and AEI/FEDER,UE.
文摘Achieving high programmability has become an essential aim of network research due to the ever-increasing internet traffic.Software-Defined Network(SDN)is an emerging architecture aimed to address this need.However,maintaining accurate knowledge of the network after a failure is one of the largest challenges in the SDN.Motivated by this reality,this paper focuses on the use of self-healing properties to boost the SDN robustness.This approach,unlike traditional schemes,is not based on proactively configuring multiple(and memory-intensive)backup paths in each switch or performing a reactive and time-consuming routing computation at the controller level.Instead,the control paths are quickly recovered by local switch actions and subsequently optimized by global controller knowledge.Obtained results show that the proposed approach recovers the control topology effectively in terms of time and message load over a wide range of generated networks.Consequently,scalability issues of traditional fault recovery strategies are avoided.
基金Sponsored by the National High Technology Research and Development Program(Grant No.2012AA02A604)
文摘This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.
文摘The inability to achieve the target of universal access to electricity is influenced by several factors including funding limitations, the use of obsolete equipment, power theft, and system losses confronting the electricity distribution services of the Electricity Company of Ghana Limited (ECG). The study assessed the components of system losses within the ECG by determining and computing the percentage of system losses within ECG, examining the causes of both commercial and technical losses in ECG, and determining ways to improve energy efficiency by reducing system losses in the most cost-efficient manner. The study adopted deductive reasoning and a quantitative approach to guide data collection and analysis of the research output. A sample of 345 technical and non-technical staff of ECG in the Greater Accra Metropolis was selected from a population of 2500. Purposive, simple random, and cluster sampling techniques were used in identifying and accessing respondents for the study. Descriptive statistics were applied to measure central tendency and degrees of dispersion and the Relative Importance Index (RII) to predict criterion and predictor variables. The impact of low voltage network losses can adversely contribute to technical losses (20%) and reduce energy efficiency in power or electricity distribution companies. Non-technical losses are mainly caused by illegal connections, meter problems, and billing problems. Each of the non-technical losses contributes a maximum of 10% to system losses. Contributors to system losses at ECG are ranked first for power theft and least for lack of incentives. System losses at ECG include metering inaccuracies, bad workmanship, unmetered supply, and lengthy distribution lines, each recording a mean value of above 3.5. Measures to improve monitoring of the networks and systems at ECG and discourage power theft should include an extensive quantification, patrolling, and inspection of the entire network to assess the extent of the network and conditions relevant for the placement of systematically planned maintenance programmes.
基金This work has supported by the Xiamen University Malaysia Research Fund(XMUMRF)(Grant No:XMUMRF/2019-C3/IECE/0007)。
文摘Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.
