Network Performance Monitoring Research Articles

Enhancing Reliability in Rural Networks Using a Software-Defined Wide Area Network

Due to limited infrastructure and remote locations, rural areas often need help providing reliable and high-quality network connectivity. We propose an innovative approach that leverages Software-Defined Wide Area Network (SD-WAN) architecture to enhance reliability in such challenging rural scenarios. Our study focuses on cases in which network resources are limited to network solutions such as Long-Term Evolution (LTE) and a Low-Earth-Orbit satellite connection. The SD-WAN implementation compares three tunnel selection algorithms that leverage real-time network performance monitoring: Deterministic, Random, and Deep Q-learning. The results offer valuable insights into the practical implementation of SD-WAN for rural connectivity scenarios, showing its potential to bridge the digital divide in underserved areas.

THE EVOLUTION OF QUALITY ASSURANCE AND SERVICE IMPROVEMENT IN SATELLITE TELECOMMUNICATIONS THROUGH ANALYTICS: A REVIEW OF INITIATIVES AND THEIR IMPACTS

Satellite telecommunications have undergone significant evolution, driven by the need for enhanced quality assurance and service improvement. This paper provides a comprehensive review of the initiatives undertaken in this field, focusing on the integration of analytics to achieve these objectives. Over the years, advancements in satellite technology have enabled the collection of vast amounts of data, presenting both challenges and opportunities. Leveraging analytics has emerged as a crucial strategy to extract actionable insights from this data, thereby enhancing quality assurance and service delivery. The review encompasses various aspects of quality assurance and service improvement in satellite telecommunications, including network performance monitoring, fault detection and resolution, capacity planning, and customer experience management. It examines how analytics techniques such as machine learning, predictive modeling, and anomaly detection have been applied to address specific challenges within each of these domains. Furthermore, the paper evaluates the impacts of these initiatives on the satellite telecommunications industry. By leveraging analytics, stakeholders have been able to optimize network operations, minimize downtime, and proactively address potential issues before they escalate. Additionally, improved service quality has led to enhanced customer satisfaction and retention, driving business growth and competitiveness in the market. Overall, this review underscores the transformative role of analytics in advancing quality assurance and service improvement in satellite telecommunications. It highlights the importance of continued innovation and investment in analytics-driven solutions to meet the evolving demands of the industry and deliver superior experiences to end-users.
 Keywords: Assurance, Analytics, Satellite, Telecommunication, Impacts, Review.

Resource Critical Flow Monitoring in Software-Defined Networks

Flow monitoring is widely applied in software-defined networks (SDNs) for monitoring network performance. Especially, detecting heavy hitters can prevent the Distributed Denial of Service (DDoS) attack. However, many existing approaches fall into one of two undesirable extremes: (i) inefficient collection where only accuracy is concerned in the method; (ii) sacrifice of accuracy due to fast detection. One practical problem with this is that it does not have the flexibility to adjust the monitoring strategy to the monitoring needs, making it difficult to meet different applications. To alleviate this problem, we propose our design of a novel flow monitoring framework that keeps the balance between accuracy and efficiency. It provides customized monitoring services for applications, where network resources can be saved, and the error rate can also be confined. In this paper, we present cReFeR, a three-step “compression Report-Feedback-Report” framework to monitor SDNs. The IP and the value compressor are specially designed to reduce the volume of flow statistics collection. This framework thus can achieve accuracy-ensured and resource-saving flow monitoring in SDNs. Theoretical analysis and simulated evaluation have proved the effectiveness of our solution. cReFeR keeps the error rate under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\%$</tex-math> </inline-formula> and reduces the amount of monitoring data more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40\%$</tex-math> </inline-formula> , which guarantees high efficiency compared with existing methods.

Implementasi Sistem Monitoring Jaringan Menggunakan Libre Nms Pada Kecamatan Tarik

Network monitoring is needed to find out the various types of equipment and systems that are connected. LibreNMS is one of network monitoring system that can make it easier for administrators to monitor the network and if there are any problems it can be resolved immediately. The features provided are numerous, making it easier to monitor network performance available and ready to use. This study aims to build a network monitoring system in Kecamatan Tarik using LibreNMS. The test results show if LibreNMS can show the condition of the network system and the use of various equipment which is properly monitored.

Fedstellar: A Platform for Decentralized Federated Learning

In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Since its birth, Centralized FL (CFL) has been the most used approach, where a central entity aggregates participants’ models to create a global one. However, CFL presents limitations such as communication bottlenecks, single point of failure, and reliance on a central server. Decentralized Federated Learning (DFL) addresses these issues by enabling decentralized model aggregation and minimizing dependency on a central entity. Despite these advances, current platforms training DFL models struggle with key issues such as managing heterogeneous federation network topologies, adapting the FL process to virtualized or physical deployments, and using a limited number of metrics to evaluate different federation scenarios for efficient implementation. To overcome these challenges, this paper presents Fedstellar, a novel platform designed to train FL models in a decentralized, semi-decentralized, and centralized fashion across diverse federations of physical or virtualized devices. Fedstellar allows users to create federations by customizing parameters like the number and type of devices training FL models, the network topology connecting them, the machine and deep learning algorithms, or the datasets of each participant, among others. Additionally, it offers real-time monitoring of model and network performance. The Fedstellar implementation encompasses a web application with an interactive graphical interface, a controller for deploying federations of nodes using physical or virtual devices, and a core deployed on each device, which provides the logic needed to train, aggregate, and communicate in the network. The effectiveness of the platform has been demonstrated in two scenarios: a physical deployment involving single-board devices such as Raspberry Pis for detecting cyberattacks and a virtualized deployment comparing various FL approaches in a controlled environment using MNIST and CIFAR-10 datasets. In both scenarios, Fedstellar demonstrated consistent performance and adaptability, achieving F1scores of 91%, 98%, and 91.2% using DFL for detecting cyberattacks and classifying MNIST and CIFAR-10, respectively, reducing training time by 32% compared to centralized approaches.

Monitoring Kinerja Jaringan Kampus 2 Politeknik Negeri Ujung Pandang Menggunakan Aplikasi Solarwinds

The internet network on campus 2 of the Ujung Pandang State Polytechnic (PNUP) often experiences interference with the internet network so that it becomes a complaint from students, lecturers and staff on campus. the network on campus is often down and it is difficult to access internet services to access the internet. So this study aims to measure the parameters of the internet network on campus using the solarwinds application. to analyze the results of monitoring network performance based on the parameters measured. To be able to determine the performance of the campus network 2 PNUP, it is necessary to analyze it by measuring the parameters of network latency, packet loss, throughput and bandwidth usage. Based on the measurement results on holidays in all buildings, the network latency and packet loss results were in very good condition, as well as the throughput and bandwidth were in good condition. By obtaining the overall condition is good. Whereas on the active day of lectures in all buildings, the results showed that network latency was not good and packet loss was in good condition, although there was often an increase in packet loss at certain hours of 100% but it returned to stability, and throughput and bandwidth were in poor condition. By obtaining the overall condition that is not good.

Design and implement a real-time network traffic management system using SNMP protocol

The research focuses on designing and implementing a system for managing incoming server traffic using the Simple Network Management Protocol (SNMP). The essence of the method is to provide a real-time monitoring system that empowers network managers to oversee all network activities efficiently. The key difference of the developed system lies in using the SNMP protocol to monitor and control the network without needing a third-party tool. The proposed approach addresses administrators' common network challenges, including inefficient bandwidth utilization and complex network resource monitoring. Upon implementation, the system gathered essential network equipment information from the Management Information Base (MIB). The research results indicate that the SNMP-based network management system prevents network devices from reaching their allotted bandwidth limits. The system notably reduces CPU utilization from 28 % to 8 % and decreases bandwidth usage from 1.7 MiB/s to 260 bytes/s. Additionally, the system's alerting capability enhances the network's resilience against incoming traffic anomalies that may otherwise interrupt server operations. An example of using the proposed system to manage incoming server traffic in small networks. It has several practical uses in managing and maintaining network infrastructure in different fields. Also, it enables real-time monitoring of network devices, resource management, fault detection, and performance optimization. The system can be used in small organizations to monitor network performance, track faults, and manage network resources efficiently. In addition, SNMP-based network monitoring systems can find applications in almost any field where networked devices and infrastructure require monitoring, management, and optimization

Performance monitoring of heat exchanger networks using excess thermal and hydraulic loads

Fouling in heat exchanger networks (HENs) affects thermal and hydraulic efficiencies resulting in economic penalties to the process industries. Conventionally, overall heat transfer rates of the heat exchangers in HENs are monitored to identify fouling. However, apart from fouling, these rates also vary with inlet conditions. Hence, estimating the degree of fouling accurately based only on heat transfer rates can fail. Previously, we had proposed the use of excess thermal and hydraulic loads as the basis for monitoring fouling in standalone exchangers (Patil et al., 2022). These are quantified as thermal and hydraulic performance indicators that can be easily tracked through suitably constructed charts. In this paper, we extend the approach to HENs wherein deviations in temperature drop across a heat exchanger vis-à-vis the clean condition also depends upon upstream heat exchanger(s). The network performance monitoring charts rely on normalized performance indicators and temperatures across heat exchangers and also have explicitly denoted normal, alert, and alarm regions. The proposed charts are applicable to HENs with or without temperature controllers. Two case studies comprising multiple scenarios of flow rate and setpoint changes are studied. The results show that the method can effectively identify the degree of fouling.

Universal and Accurate Sketch for Estimating Heavy Hitters and Moments in Data Streams

In computer networks, traffic measurement is a module in a network probe to measure flow-level statistics from an IP packet stream, which are the basis for network performance monitoring and malicious activity detection. This module extracts the flow IDs from incoming IP packets, classifies packets into flows, and counts the number of packets (or bytes) for each flow. It is a great challenge to measure the per-flow statistics for a high-speed network device, using only the size-limited SRAM on its line cards. Therefore, many algorithms using sublinear memory have been proposed, such as CountMin and CountSketch. However, most of previous algorithms are designed for specific measurement tasks. To obtain multiple types of statistics, people have to deploy multiple sketches, which demands more resources of a network device. It is useful to design a universal sketch that can track not only the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> largest individual flows (called heavy hitters) but also the overall traffic distribution statistics (called moments). Prior work named UnivMon successfully tackled this ambitious quest. However, it incurs large and variable per-packet processing overhead, which may result in a significant throughput bottleneck in high-rate packet stream, given that each packet requires 33 hashes and 32 memory accesses on average and many times of that in the worst case. To address this performance issue, we fundamentally redesign the solution architecture from hierarchical sampling to new progressive sampling and from CountSketch to new GenericCM, which ensure that per-packet overhead is a small constant (5 hashes and 8 memory accesses in the worst case), making it more suitable for online operations, especially for hardware pipeline implementation. This new design also makes effort to reduce memory footprint or equivalently improve measurement accuracy under the same memory. Our experiments show that our solution reduces measurement error by roughly 98.1% for second-order moment and by 91.5% for entropy, when given the same 0.2MB memory as UnivMon.

Optical label-based cost-effective DWDM optical network performance monitoring using low-bandwidth PD with novel SRS mitigation DSP

Large-scale dense wavelength division multiplexing (DWDM) multi-channel performance monitoring is one of the indispensable technologies for the flexible optical networks. The existing Labelbased monitoring scheme requires expensive optical demultiplexing components/equipment to avoid the influence of stimulated Raman scattering (SRS), which is not only costly and bulky, but also could not monitor the wavelength channels simultaneously. In this paper, a low-cost, high-accuracy monitoring scheme based on Optical Label Method is proposed for DWDM networks, where the optical channel power and node identification (ID), as the main monitoring targets that both can indicate or evaluate the channel connection status, could be efficiently monitored. In the scheme, a novel digital signal processing (DSP) method of SRS mitigation is proposed and demonstrated, and an asynchronous code-division multiple access (A-CDMA) based digital label encoding and decoding method is adopted to distinguish the node ID so that channel initial added node can be accurately verified, thereby wavelength connection status can be reliably monitored by combining the channel power and node ID information. The simulation results show that each wavelength channel power and node ID can be accurately monitored only by low bandwidth photoelectric detector (PD) under the condition of 80 wavelengths and 10 spans at C-band.

Analysing the performance of radiological monitoring network during nuclear accidents

JRODOS was used to simulate the dispersion of airborne radioactive material from a point source over the period 2012–2015. In total, 1331 radioactive plumes were simulated with the objective to investigate the influence of changes in meteorological conditions in the performance of the monitoring network. For this purpose, the existing set of 84 monitoring stations included in the EUropean Radiological Data Exchange Platform (EURDEP) system in an area of 200 km around the source point is taken as reference. A methodology is presented for quantitative evaluation of the variability of the number of stations affected, the time of the first detection, the maximum registered and differences between the maximum values match in the network and in the simulated plumes. The results show seasonal differences in all of these parameters according to changes in the size and shape of the affected area due to meteorological conditions. There are large differences in the number of monitoring stations affected by the plumes, from 2 to 74; in the timing and location of the first alert given by the network, from 1 to more than 5 h and faster along the west-east axis from the source; and in level of maximum gamma dose rate detected by the monitoring stations, from 0.17 nSv/h in summer to 0.22 nSv/h in autumn. These results show the need to consider this type of analysis over the years in the design of monitoring networks and in the development of nuclear emergency preparedness and response (EP&R) plans.

Conceptualization and Development of MATLAB-based Application for Local Area Network

Local Area Network is the epicenter of modern businesses and industries and is a critical requirement for productivity and efficiency. It is the most structured way of connecting different networking devices for communication, database access, and sharing resources among users. As technology, design, and applications continue to evolve, high-speed networks are becoming increasingly important. Devices with high bandwidth requirements, such as cameras, VOIP, and PA over IP, is being added to the network, which tends to increase the amount of data traffic and thus utilize the maximum bandwidth. The increase in bandwidth is a cost-intensive process that requires the installation of additional transmission media and other network equipment. Hence, the network resources need to be monitored for smooth and efficient performance. Network performance monitoring is a complex task and requires proficiency. This paper presents the development of a simple GUI-based network monitoring application.

Вызовы в реализации систем глубокого анализа сетевого трафика методом полного протокольного декодирования

This paper presents a summary of experience in developing the deep packet inspection system using full protocol decoding. The paper reviews the challenges encountered during implementation and provides a high-level overview of the solutions to these issues. The challenges can be grouped into two groups. The first group is related to the fundamental tasks which must be addressed when implementing full protocol decoding systems. This includes ensuring correct protocol parsing, which involves identifying and interpreting protocol headers and fields correctly. Moreover, it is necessary to ensure the processing of fragmented packets and the assembly of fragments into the original message. Additionally, the processing and analysis of encrypted traffic is a crucial task that may require the use of specialized algorithms and tools. The second group of problems is related to optimizing the process of full protocol decoding to ensure high-speed traffic processing, as well as supporting new protocols and the ability to add user-defined extensions. While there are open-source systems that address some of the primary issues associated with full protocol decoding, there may be a need for additional effort and specialized solutions to efficiently operate and expand the functionality of such systems. Although implementing deep network traffic analysis tools using full protocol decoding requires the use of advanced hardware and software technologies, the benefits of such analysis are significant. This approach provides a more complete understanding of network traffic patterns and enables more effective detection and prevention of cyber-attacks. It also allows for more accurate monitoring of network performance and the identification of potential bottlenecks or other issues that may impact network efficiency. In this article, we also emphasize the importance of system architecture development and implementation to ensure the successful deployment of deep network traffic analysis tools using full protocol decoding. At last, we conducted an experiment where several advanced optimizations were implemented in the system that had already solved primary issues. These optimizations related to working with memory, based on the features of the traffic processing scheme. By results, we evaluated significant performance improvement in solving secondary tasks, described in this work.

Towards Fault Tolerance Management Systems in SDN

The issue of network operation, monitoring, and availability in traditional network environments has been one of the major challenges to addressing network administrators' struggles to deal with. The capacity for the network to comprehend the status and the performance of every node has been one of the priorities amongst others for network administrators to protect during troubleshooting in their network. However, the growing use of software-defined networks (SDN) technologies, as well as virtualization, pave the way to the solution of network operation, monitoring, and availability of hardware and services in a network environment. SDN has emerged with a novel paradigm of network abstractions that is dynamic, able to monitor network performance and fault management automatically, using its programmable capability. This paper presents a design of a fault tolerance management system for SDN controller framework for small to medium sized networks capable of detecting controller faults and nodes/links faultsin case entire network access is unavailable. The fault management system applies various approaches for fault detection and recovery to ensure the reliability and availability of the network resources.

An artificial intelligence model based on multi-step feature engineering and deep attention network for optical network performance monitoring

Optical network performance monitoring technology, which can effectively identify physical impairment in the network, is of great significance to ensure network stability and prevent accidents. In order to establish a high-precision performance monitoring framework for optical networks, a three-step feature engineering and deep attention mechanism approach is proposed in the paper. The main modeling process consists of the following three steps: In Step I, the AAH (Asynchronous Amplitude Histogram) method is used to initially extract features from the original data. In Step II, the KPCA (Kernel Principal Component Analysis) method and the Q-learning method are utilized to reduce the feature dimension and transmit the optimized feature to the downstream predictor. In step III, the downstream predictor based on the LSTM (long short-term memory network) and attention mechanism can effectively construct the mapping between features and labels and realize data prediction. After several groups of comparative experiments, the following conclusions can be obtained: (a) Ablation experiments and component comparisons demonstrate that the proposed framework is able to effectively combine feature engineering and predictors, which can get excellent results. (b) The optical network performance detection framework proposed in the paper achieves better results than three SOTA (state-of-the-art) models and fifteen alternative frameworks.

Transmission Control Over Satellite Network for Marine Environmental Monitoring System

The transmission of huge amounts of data generated from various sensors is the basis and prerequisite for the analysis and prediction of the marine ecosystem. The special geographical conditions of the ocean determine that its data transmission is different from the common terrestrial data transmission. In this study, a marine environmental monitoring network architecture is proposed to collect environmental data from marine ecosystems using a heterogeneous network constructed by a multi-hop network and a satellite network. Considering the characteristics of satellite networks, such as high bandwidth delay product (BDP), long latency and high bit error rate, this work proposes a transmission scheme that improves the transmission performance of Transmission Control Protocol/Internet Protocol (TCP/IP)-based Internet networks over satellite networks. The improved scheme increases the monitoring data transmission rate, distinguishes between random data loss and congested data loss, rationalizes the threshold in fast recovery, and improves the overall transmission performance of the marine environmental monitoring network. The experimental results show that the proposed scheme can greatly improve the transmission performance of the monitoring network compared with traditional transmission protocols. The network model proposed in this study makes full use of current advanced network technologies, including multi-hop networks, wireless sensor networks, and satellite networks to expand the monitoring area at sea. This study provides a useful reference for the network model and data transmission for ocean monitoring.

Application Based performance monitoring heavy data transmission of Local Area Network

There are many applications should be work online, these applications can be seen in the application layer of OSI reference model, each one of these consumes particular band width, bit rate and network speed depending of the resources that used to make any application operate for example there is a widely difference between video streaming network application and text chatting application. In this paper when any computer network needs to be examined, at least two different applications should be taken for example to monitor network performance when these application going to work and get in action. In this paper the researcher designed and a local area network to implement dedicated applications, monitor the network factors performance, get results, and compare between applications.

Research of current requirements for the developments of telecommunications network monitoring and control systems

The modern telecommunications market dictates the need to modernize the existing requirements for monitoring and management systems of data transmission networks. The infrastructure of existing telecommunications networks is continuously becoming more complex and expanding, therefore, modern troubleshooting and network performance monitoring products must be equipped with the appropriate functionality. This paper determines the current requirements for modern systems, which must be taken into account when developing future monitoring and management software products. This article examines the functional features of software systems used on the networks of telecom operators, namely: TEOCO Netrac 7, SATEL SOVA, EMC Smarts, HP Network Node Manager, Zabbix, Cacti, NOC Project. Comparison and analysis are the tools used in this research. The research topic meets the criteria of the Ministry of Digital Development, Communications and Mass Communications of the Russian Federation (Federal Law No. 204-FL of June 23, 2016 "On Amendments to Articles 2 and 24 of the Federal Law "On Communications").

Aquifer heterogeneity controls to quality monitoring network performance for the protection of groundwater production wells

A groundwater monitoring network surrounding a pumping well (such as a public water supply) allows for early contaminant detection and mitigation where possible contaminant source locations are often unknown. This numerical study investigates how the contaminant detection probability of a hypothetical sentinel-well monitoring network consisting of one to four monitoring wells is affected by aquifer spatial heterogeneity and dispersion characteristics, where the contaminant source location is randomized. This is achieved through a stochastic framework using a Monte Carlo approach. A single production well is considered that results in converging non-uniform flow close to the well. Optimal network arrangements are obtained by maximizing a weighted risk function that considers true and false positive detection rates, sampling frequency, early detection, and contaminant travel time uncertainty. Aquifer dispersivity is found to be the dominant parameter for the quantification of network performance. For the range of parameters considered, a single monitoring well screening the full aquifer thickness is expected to correctly and timely identify at least 12% of all incidents resulting in contaminants reaching the production well. This proportion increases to a global maximum of 96% for a network consisting of four wells and very dispersive transport conditions. Irrespective of network size and sampling frequency, more dispersive transport conditions result in higher detection rates. Increasing aquifer heterogeneity and decreasing aquifer spatial continuity also lead to higher detection rates, though these effects are diminished for networks of 3 or more wells. Statistical anisotropy has no effect on the network performance. Earlier detection, which is critical for remedial action and supply safety, comes with a significant cost in terms of detection rate, and should be carefully considered when a monitoring network is being designed.

Multi-Task Learning Convolutional Neural Network and Optical Spectrums Enabled Optical Performance Monitoring

We propose a simultaneous optical signal recognition (OSR) and optical signal-to-noise ratio (OSNR) monitoring method by using multi-task learning convolutional neural network (MTL-CNN) in conjunction with optical spectrums, which enables optical performance monitoring (OPM) in the optical transmission network. In order to achieve a trade-off between monitoring loss and time consumption of the MTL-CNN constructed for seven commonly used signals with a spectrum resolution of 10 pm, the number of feature map channels in four convolutional layers is delicately designed as 32, 32, 64, and 64 with task weights set to 0.4 and 0.2, corresponding to OSNR monitoring and OSR, respectively. Simulation results manifest that this method can realize the recognition of the received optical signals with an overall accuracy of 100% and also enable OSNR monitoring with the mean absolute error (MAE) of 0.262 dB. The proposed method shows strong robustness to various distortions and exhibits good performance in terms of time consumption. The effectiveness is further verified by proof-of-concept experiments in three signals. These illustrate that our method is a promising solution for multi-parameter monitoring with high accuracy and efficiency.