Network Management Research Articles

Temporal Scenarios-based Assessment of Maximum Hosting Capacity of Renewable Generation in Distribution Systems Considering ANM Techniques

Abstract Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.

Linear optimal power flow model for modern distribution systems: Management of normally closed loop grids and on-load tap changers

Modern distribution systems face new challenges due to the rise of small-scale renewable energy resources and the electrification of energy demand. Additionally, the complexity of network management increases with the exchange of flexibility services between distribution and higher voltage levels. The system operator's role involves optimizing the planning and operation of the distribution network. In current scenarios, this includes managing flexibility resources (such as generators, demand response, and on-load tap changers) and implementing new operating strategies (such as closed-loop topologies and dynamic reconfiguration). This paper proposes a reformulation of one of the most used linear approximations of the Optimal Power Flow model for distribution networks. The novelty of this reformulation lies in integrating equations to handle tap-changing transformers with closed-loop/meshed network operation, while preserving the original linear formulation and computational tractability.

Grid impact analysis on wind power plant interconnection in strengthening electricity systems

The Timor system is one of the large systems in the East Nusa Tenggara region. Based on the general plan for electricity supply for 2021-2030, there is a plan to interconnect a 2x11 MW wind power plant. The addition of wind power plants will pose a considerable threat to the system due to the intermittency nature of renewable energy plants. Therefore, a comprehensive grid impact study is needed to convince network managers that adding wind farms will not cause disruptions to the system either locally or in general and is expected to strengthen the electricity system. The power flow simulation results, installing a 2x11 MW wind farm on the Timor system can improve voltage quality and reduce losses on both 70 and 150 kV systems. For transient stability, the frequency value on the Timor system still meets the grid code requirements. In addition, the simulation results of the intermittency impact of the wind power plant output show that the Timor system is still in a stable condition. The stability of the rotor angle of the existing power plant when the transient stability simulation is carried out shows that it is still in a balanced condition.

Robust Assessment of Hosting Capacity of Renewable Generation in Distribution Systems Considering the Active Network Management

Abstract As the penetration rate of renewable distributed generation (RDG) increases, its random fluctuation characteristics have brought many problems to the safe operation of the active distribution network (ADN), such as voltage over-limit, reverse flow, and deterioration of power quality. Therefore, it has effectively affected the new energy hosting capacity of the ADN. Therefore, the effective assessment of the new energy hosting capacity of the ADN is a key part of the distribution network planning. In order to cope with the random fluctuation characteristics of new energy, this paper presents a two-stage robust assessment method of RDG hosting capacity of distribution networks by considering active network management (ANM) techniques such as network reconfiguration, reactive power compensation and on-load voltage regulation. Finally, the improved IEEE-33 distribution system is used to verify the results. The proposed method has been shown to be effective for evaluating the ADN’s RDG hosting capacity.

Machine Learning-based Enhanced Deep Packet Inspection for IP Packet Priority Classification with Differentiated Services Code Point for Advance Network Management

In modern networking, the efficient prioritization and classification of network traffic is paramount to ensure optimal network performance and optimization. This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). The DSCP resides inside the Differentiated Services (DS) field of the Internet Protocol (IP) packet header in an OSI or TCP/IP model, which prioritizes different types of packets for forwarding to the router based on the attached payload. Similarly, DPI plays a crucial role in network management, enabling the identification of applications, services, and potential threats within the network traffic. In this study, various machine learning models, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Logistic Regression and ensemble models such as, XGBoost, AdaBoost were used to harness the capabilities of network packet classification based on DSCP. Detailed experimentation was conducted to evaluate their performance. The results show that AdaBoost demonstrated superior performance with an accuracy of around 89.91%, showcasing its ability to adapt the evolving network configurations and conditions while maintaining high classification accuracy on the IP packets. The random forest model also performed well, achieving an accuracy of 89.41%, making it a strong candidate for the DSCP classification in network transmission. This study has the potential to significantly improve how networks manage traffic, prioritize packets, and secure complex and dynamic network environments.

Traffic data classification in SDN network based on machine learning algorithms

Traffic classification plays a crucial role in various domains of network management, including service measurement, architectural design, security monitoring, and advertising. Software-defined networks (SDN) is a new technology that has the potential to solve typical network problems by simplifying network management, the introduction of network programmability, and the provision of a global perspective of a network. In recent years, SDN has brought new opportunities to classify traffic. Traffic classification techniques in SDN have been investigated, proposed, and developed. This survey delves into traffic classification under SDN, which is a vital component for improving network services, administration, and security. We give an in-depth assessment of traffic categorization algorithms adapted for SDN, emphasizing the fresh opportunities and problems they present. We cover the many metrics for assessing the effectiveness of these traffic classification algorithms, such as accuracy, precision, recall, and F1 score, and we examine the numerous datasets that serve as performance benchmarks. The study also synthesizes the findings of existing research, revealing trends and the efficacy of various techniques in the context of SDN-enabled settings. This document serves as a resource for scholars and practitioners seeking to optimize traffic classification strategies by providing a complete review and assessment of existing traffic classification approaches

Perancangan Sistem Informasi Ticketing Jaringan Pada Dinas Komunikasi, Informatika dan Statistik Provinsi DKI Jakarta

The rapid development of technology affects all aspects of life, especially information systems. With the development of this technology, speed and accuracy are needed to send information so that it can be received by the recipient. For the process of sending data, a communication network is needed as a means of transportation. In the process, it doesn't always go well; sometimes network interference problems occur. To overcome this problem, a system is needed that can record network disturbances so that problems can be resolved and analyzed immediately. The process of recording network disturbances at the DKI Jakarta Province Communication, Informatics, and Statistics Office is currently still using conventional or manual methods, which often creates problems. So it is necessary to have a system that can record these disturbances. The design of this system is made to make it easier for companies to record network disturbances and facilitate analysis if there are repeated disturbances. By using the waterfall method, existing problems can be examined so that the designed system can perform as expected. So that this system makes it easier for network managers to document reported network disturbance data, which results in not requiring a long time to search for or collect manual records. Therefore, this system will make the work system more effective and efficient in terms of time, effort, and cost.Keywords : network, ticketing, waterfall

Структурные метаморфозы политических процессов на пороге посткапиталистической эпохи: новые акторы, интеракции и феномены

The author of the study addresses the peculiarities of structural changes in political processes in the context of an interformational transition caused by the crisis of objective capitalist production relations. Based on the analysis of the dynamic model of the political process by G. Almond and J. The author concludes that modern political and economic transformations no longer allow us to effectively describe the structure of the political process from the point of view of established theoretical models built in the era of stable economic growth. Changes in the actor-resource base of political processes, generating new actors and modernizing the resource base of political interactions, are explained by the transition to network and horizontal management formats that destroy capitalist verticals and hierarchies. The author identifies new types of political actors and resources, and also attempts to describe the trends of their interaction through the prism of economic and technological transformations. In addition, it is suggested that it is more logical to study the structure of the political process in modern conditions from the point of view of actor-network theory, focused on the description of "assemblies" that take into account the multifactorial and chaotic nature of politics.

Network traffic prediction by learning time series as images

Network traffic prediction is crucial for cost-effective network management, resource allocation, and security in the emergent software-defined and zero-touch networks. Machine Learning (ML) models such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown great promise in traffic prediction. This work proposes a hybrid ML model consisting of pre-trained AlexNet and ResNet models, the 2D Convolutional Neural Network (CNN), Vision Transformer (ViT), and LSTM/GRU models and their variants to predict network traffic. This work explores two approaches for converting the time series data to images by allowing more precise feature extraction and then performing traffic prediction on an image dataset, thus increasing accuracy. Given their proficiency in extracting image features we use three pre-trained models: the AlexNet, the ResNet combined with CNN, and the ViT. Then, we use those models as inputs to the LSTM, Bi-LSTM, and the GRU, Bi-GRU models for predicting network traffic data. The models are evaluated on the GÉANT and Abilene traffic networks and assessed based on the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Comparison of results with existing network traffic prediction techniques shows considerable improvements in all performance metrics revealed by the Wilcoxon test. Our best model results for the GÉANT and Abilene datasets are respectively; MSE of 0.00058 and 0.03078, RMSE of 0.02415 and 0.17544, and MAE of 0.00774 and 0.13384.

Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

Revolutionizing Enterprise Network Management: The Role of Ai-Driven Solutions in Modern Computer Networking

In the rapidly evolving landscape of enterprise network management, artificial intelligence (AI) is emerging as a transformative force. This paper, titled "Revolutionizing Enterprise Network Management: The Role of AI-Driven Solutions in Modern Computer Networking," delves into the significant impact of AI technologies on the efficiency, security, and scalability of enterprise networks. By integrating AI-driven solutions, organizations can achieve unprecedented levels of automation, predictive maintenance, and real-time anomaly detection, thus enhancing overall network performance. This study provides a comprehensive analysis of the latest AI techniques employed in network management, including machine learning algorithms, neural networks, and advanced data analytics. Through case studies and empirical data, we demonstrate how AI enhances network security, reduces downtime, and optimizes resource allocation. Our findings suggest that the adoption of AI in network management not only improves operational efficiency but also offers a competitive advantage in the digital economy. Keywords: AI-driven network management, enterprise network security, machine learning in networking, predictive maintenance, network automation, real-time anomaly detection, computer networking, digital transformation.

Analysis of methods of data flow management in mobile radio communication means

The article analyzes the existing methods of managing data flows in mobile radio communications. During the analysis, the peculiarities of the functioning and construction of these networks were considered. It has been established that such networks have features different from other networks, such as mobility, dynamic topology, independent organization, use of mobile means of communication, lack of fixed data transmission routes, etc. The task of providing routing in mobile radio communication devices, mobile radio networks, and the types of routing in communication networks are considered. The conditions for the implementation of the routing task and the features that characterize the routing process have been established. The levels of the OSI open systems model and the most well-known protocols that work at these levels and are used in data flow management are considered. Data flow management methods are classified by type and a list of requirements for the process of data flow transmission in mobile radio networks is defined, taking into account the peculiarities of the functioning of mobile radio communication means, which must be taken into account when building a mobile radio network management system. It has been established that at the current stage of information technology development, data flow management methods are mostly adapted for use in computer, wired or fixed networks and do not take into account the conditions of use in mobile radio communication devices. It has been established that the construction and operation of an effective data flow control subsystem in the control systems of mobile radio networks and mobile radio communication means requires the use of the latest technologies and modern approaches in the development of data flow control methods and methods that will ensure the functioning of the corresponding data flow control subsystem. When choosing protocols, methods, methods of managing data flows, it is proposed to focus on adaptation, modification of approaches or their combined use based on the target function, taking into account the peculiarities of the functioning of mobile radio networks and mobile radio communication means.

Machine Learning-based fixed access Network Interface Congestion Prediction

Network interface congestion refers to a situation in which network interfaces or communication channels within a network experience high levels of traffic, leading to performance degradation or potential network failures. In modern networking, this issue remains relevant and can manifest in various contexts due to a surge in data traffic, Internet of Things (IoT) devices can generate substantial data, increased use of VPNs for remote work and when multiple users or applications concurrently access and transfer data in and out of the cloud. Efforts to forecast and address network interface bottlenecks are crucial to sustaining the performance and consistency of modern networks. In the PTCL fixed access network infrastructure, network blocking is a serious problem that necessities to be addressed. The main objective of this study is to overcome network congestion problems that occur in PTCL's fixed access network through the use of machine learning techniques. Despite having real-time monitoring tools such as SolarWinds, there remains a dearth of software capable of predicting imminent bottlenecks in the network interface. The primary aim is to build an analytical model which can predict potential blockages within 30 days and facilitate proactive management strategies for better administration. Top of Form The study classifies the most effective machine learning technique for this purpose and evaluates its performance against current methods. To achieve the goal, a diagnostic dataset was created, leveraging appropriate configurations, software tools, and hardware resources. Data was sourced from SolarWinds, consisting of information from over 23,000 interfaces across 11,000 access network nodes. This data was analyzed in PTCL's Data Warehouse, where a historical aggregated dataset was constructed. Exploratory data analysis was performed using Python libraries, including Matplotlib, NumPy, Pandas, and Seaborn. This investigation sets the foundation for proactive network management and enhanced user experiences within PTCL's fixed access network, addressing the critical essential for advanced predictive tools to combat congestion and enhance network competence.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

Adaptive Artificial Intelligence Techniques for QoS and Congestion Management in 5G Wireless Networks: A Literature

In the rapidly evolving landscape of 5G wireless networks, ensuring Quality of Service (QoS) and managing congestion effectively are critical challenges that must be addressed to meet the stringent performance requirements of modern applications. This paper presents an in-depth study on the application of adaptive artificial intelligence (AI) techniques for QoS and congestion management in 5G wireless networks. By leveraging machine learning (ML) and deep learning (DL) algorithms, we propose an intelligent framework that dynamically adjusts network parameters in real-time to optimize performance and mitigate congestion. The proposed approach integrates reinforcement learning for adaptive decision-making, convolutional neural networks (CNNs) for traffic pattern recognition, and long short-term memory (LSTM) networks for predictive analysis of network congestion trends. Through extensive simulations and real-world testing, our framework demonstrates significant improvements in QoS metrics such as latency, throughput, and packet loss, while efficiently managing congestion across diverse network scenarios. The results indicate that adaptive AI techniques hold immense potential in enhancing the robustness and efficiency of 5G wireless networks, paving the way for more reliable and high-performance communication systems. KEYWORDS: artificial intelligence (AI), machine learning (ML), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks

Mobile traffic prediction with attention-based hybrid deep learning

Mobile data consumption is ever-increasing due to the continuous emergence of bandwidth-hungry mobile applications as well as the drastic increase in number of mobile terminals resulted from the pervasive adoption of Internet of Things. In the 5G+ era, network operators have to deal with new technical challenges on network management, resource optimization and mobility scheduling, which require the ability to perform efficient mobile traffic prediction. Traditional prediction methods using convex optimization, Markov model and linear equations cannot describe the various nonlinearity changes of network traffic. In this paper, we propose a novel traffic prediction algorithm for mobile networks by introducing a hybrid deep learning method equipped with the attention mechanism, which analyzes the traffic in cellular zones to determine the spatiotemporal characteristics in the residential community. The hybrid deep learning model includes autoregressive integrated moving average mode (ARIMA) preprocessing with decomposition ability for time series, external spatial feature variables, sequence-to-sequence (seq2seq; attention-based CNN-LSTM) pretraining and extreme gradient boosting (XGBoost) optimization processing for integrating nonlinear relationships. The CNN method shows good nonlinear generalization ability to mine deep features of community traffic and capture the nonlinear trend. Our proposed approach first decomposes the community traffic data into linear and residual parts through ARIMA and then seizes the deep features of community traffic through the constructed pretraining seq2seq framework. Finally, the pretraining outcomes are further adjusted by the XGBoost method. Simulation results show that the proposed solution improves the accuracy of prediction apparently.

Enabling IP-optical integration in core and metro networks [Invited

IP-optical (Internet protocol and optical) integration, or “IP over DWDM (dense wavelength division multiplexing),” is a concept spanning over two decades since the advent of coherent DSP (digital signal processing). In recent years, coherent DSP technology has progressed to a point where it can be integrated on small pluggable form factors that can be equipped in optical line systems and disaggregated transponders or directly in IP routers to support a range of metro and core applications. The option of equipping pluggable digital coherent optics (DCOs) directly in IP routers has the biggest potential for operational cost savings but hinges on the availability of a unified management environment with a single pane of glass to coordinate and automate IP routing and optical transport functions. This work presents alternative software-defined networking (SDN) architectures and evaluates the challenges associated with the evolution to IP over DWDM network architectures. It demonstrates the first, to our knowledge, implementation of the Transport API supporting colored pluggable interfaces in routers in a real network testbed. This work contributes to the realization of end-to-end network management for IP-optical networks, offering operators comprehensive visibility into multi-layer and multi-domain services and empowering revenue generation.

Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric Visibility

The Internet architecture has recently shifted towards a framework characterized by multiple interconnected cloud sites, all linked via an L3 IP network. With this shift, managing networking controls among multiple cloud sites is becoming a significant operational challenge. In particular, ensuring effective networking control necessitates a deeper understanding of flow-level dynamics for comprehensively monitoring interconnection statuses across multiple sites. In this paper, we first propose an IO Visor-enabled tracing solution for Linux-based boxes to efficiently enable the comprehensive collection of network packet flows across interconnected sites. Next, we apply IP prefix-based flow-level analysis at a centralized location to support the intent-based networking control application. This flow-level analysis involves generating policy-based specific action (i.e., redirect) via SDN controllers for specific source IP prefixes, which are causing unknown or potentially vulnerable flows. Furthermore, we employ an open-source ONOS SDN controller to tackle the challenge of managing the hybrid SDN-IP interconnections. By leveraging intent-based networking control, we effectively apply ONOS intents based on IP routing information and generated a set of forwarding action. We evaluate our proposed solution in an experimental SDN-cloud testbed, demonstrating its effectiveness in real-world scenarios. Overall, through the seamless integration of these monitoring and control approaches, we manage to enhance the adaptability and security of the interconnected cloud sites of the testbed.

The Behavioural Change of Distribution Networks with Increasing Circuit Length and Load

Purpose: This study investigated the behavioural change of distribution networks with increasing circuit length and load. Design/Methodology/Approach: This study employed a simulation-based experimental design using OpenDSS to model and analyse the behaviour of the distribution network under different scenarios. A combination of varying network lengths and loading levels was chosen to determine the behavioural changes of a network. A total of 100 simulations were conducted, representing 10 different circuit lengths and 10 different loading conditions. Data collection on voltage levels at various points along the circuit, focusing on the voltage at the end of the circuit was carried out. Each scenario will be modelled and analysed to study the voltage drop and stability. The simulation results were validated by comparing them with theoretical calculations and real-world measurements from similar networks. Findings: It has been shown that, for every circuit length there is a maximum load that will result in a linear relationship maintaining the correct energy balance to respond to voltage regulation and power flow. With circuit length increase, capacitive reactance reduces while resistance and inductive reactance increase leading to a more inductive circuit. Research Limitation/Implications: The research considered only changes in circuit length and loading as it was based on a simplified source-load equivalent circuit. Practical implication: It draws the attention of practising engineers and designers to be mindful of the implications of load increase and network expansions on system characteristics. It forms a guide to the limit a circuit could be extended or loaded if system balance is to be maintained. Social Implication: A stable and reliable power supply supports economic growth, public health, and safety, leading to improved quality of life. Ensuring all communities have reliable electricity fosters social equity and inclusion, promoting fairness and improving the quality of life for disadvantaged groups. Originality/Value: The novelty of this research lies in its comprehensive, realistic, and advanced approach to studying the behavioural changes in distribution networks with increasing circuit length and load. By addressing previously overlooked aspects and integrating modern computational and data analysis techniques, the study provides valuable new insights that can significantly improve the design, operation, and management of long distribution networks.