Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks

A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.

A Novel Outage Capacity Objective Function for Optimal Performance Monitoring and Predictive Fault Detection in Hybrid Free-Space Optical and RF Wireless Networks

This study develops an optimal performance monitoring metric for a hybrid free space optical and radio wireless network, the Outage Capacity Objective Function. The objective function—the dependence of hybrid channel outage capacity upon the error rate, jointly quantifies the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The objective function is developed from the basic information-theoretic capacity of the optical and radio channels using the gamma-gamma model for optical fading and Ricean statistics for the radio channel fading. A simulation is performed by using the hybrid network. The objective function is shown to provide significantly improved sensitivity to degrading performance trends and supports of proactive link failure prediction and mitigation when compared to current thresholding techniques for signal quality metrics.

Lossy nodes inference based on end-to-end passive monitoring in wireless sensor networks

This paper presents a passive monitoring mechanism, loss）, nodes inference （LoNI）, to identify loss）, nodes in wireless sensor network using end-to-end application traffic. Given topology dynamics and bandwidth constraints, a space-efficient packet marking scheme is first introduced. The scheme uses a Bloom filter as a compression tool so that path information can bc piggybacked by data packets. Based on the path information, LoNI then adopts a fast algorithm to detect lossy nodes. The algorithm formulates the inference problem as a weighted set-cover problem and solves it using a greedy approach with low complexity. Simulations show that LoNI can locate about 80% of lossy nodes when lossy nodes are rare in the network. Furthermore, LoNI performs better for the lossy nodes near the sink or with higher loss rates.

MPDP: A Probabilistic Architecture for Microservice Performance Diagnosis and Prediction

In recent years,container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources.However,there is a noticeable absence of techniques for predicting microservice performance in current research,which impacts cloud service users’ability to determine when to provision or de-provision microservices.Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention,which potentially leads to user confusion.In this paper,we propose,develop,and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction(MPDP).MPDP considers various factors such as response time,throughput,CPU usage,and othermetrics to dynamicallymodel interactions betweenmicroservice performance indicators for diagnosis and prediction.Using experimental data fromourmonitoring tool,stakeholders can build various networks for probabilistic analysis ofmicroservice performance diagnosis and prediction and estimate the best microservice resource combination for a given Quality of Service(QoS)level.We generated a dataset of microservices with 2726 records across four benchmarks including CPU,memory,response time,and throughput to demonstrate the efficacy of the proposed MPDP architecture.We validate MPDP and demonstrate its capability to predict microservice performance.We compared various Bayesian networks such as the Noisy-OR Network(NOR),Naive Bayes Network(NBN),and Complex Bayesian Network(CBN),achieving an overall accuracy rate of 89.98%when using CBN.

A Design of Modern Greenhouse Environmental Monitoring System

Based on the problems in the current greenhouse environmental monitoring system such as difficult connection layout,low flexibility and high costs,this paper builds the greenhouse environmental monitoring system based on wireless sensor network,and designs the sensor nodes and gateway nodes. The sensor nodes of this system are responsible for collecting environmental parameters and sending the data to gateway nodes via wireless sensor network. And the gateway nodes transmit the data to the remote monitoring platform. The microprocessor module of node hardware uses MSP430F149 microprocessor for data processing and control; wireless communication module consists of nRF905 RF chip and peripheral circuit,responsible for transmitting and receiving data; sensor module uses AM2301 sensor for data measurement; the power supply module uses the circuit consisting of LT1129-3. 3,LT1129-5 and Max660 to provide 3. 3 and ± 5V power. The C language development is employed for wireless routing protocol of node and time synchronization algorithm,to achieve node data acquisition and processing,rule forwarding and remote transmission. Remote monitoring software uses NET. ASP,HTML and C# development to provide visual WEB mode remote data management platform for users. The system goes through networking testing in greenhouse in Xining City,and test results show that the system operation is stable and reliable,and the average network packet loss rate is 2. 4%,effectively solving the problems in greenhouse environmental monitoring system and meeting the application requirements of greenhouse cultivation environmental monitoring.

水文水井钻探泥浆特征参数在线监测技术

水文水井钻探是开发地下水资源的一项重要技术手段,钻探过程中产生的泥浆排放会对生态环境造成一定程度的破坏。为对泥浆进行有效净化并循环利用,开展了泥浆在线监测方案的研究,首先利用管道黏度计对泥浆密度、表观黏度、塑性黏度、动切力进行实时监测,然后通过预先训练基于泥浆密度、流变性参数和含砂量的神经网络模型,预测经过固相控制之后的泥浆中的含砂量。结果表明:通过管道黏度计监测的泥浆密度、表观黏度、塑性黏度、动切力的平均误差分别为0.2%、1.7%、3.4%、3.7%;通过神经网络模型预测的含砂量的平均误差为15.9%,预测模型不依赖模型训练中使用的泥浆配方,能够适用于相同体系的其他泥浆配方,表明预测模型具有一定的泛化性。该泥浆在线监测系统能够满足绿色勘查的要求,适合水文地质及水井钻探的现场应用。

基于机器学习的光网络监测与优化方法

近年来,许多新型调制和复用技术以及动态网络概念被提出,以适应不断提高的网络带宽和质量需求。网络控制平台向系统化、智能化趋势发展,要求网络管理者不断监测网络各项参数,时刻优化网络状态。然而,大范围部署额外的监测设备获取参数信息从成本控制的角度缺乏可行性,利用已知数据与特殊算法进行网络性能监测和优化是更优的选择。机器学习方法因为其足够准确和高效逐渐被学术界采纳用于完成上述任务。文章梳理了在光网络监测与优化任务中使用机器学习算法的不同应用场景,综述了该领域的研究成果,并提出了现存的基于机器学习的光网络监测与优化方法存在的问题及可能的进一步研究的方向。基于机器学习的光学性能监测包括光学损伤辨别、信道质量评估以及通道功率预测,基于机器学习的网络配置优化方法包括强化学习优化通道功率。进一步研究方向可以考虑加强与运营商的合作,使用真实的现场数据,不断获取数据动态训练模型,并使用迁移学习和数据增强等技术,以保证算法的鲁棒性与泛化能力。

Performance Monitoring in Transparent Reconfigurable WDM Networks

This paper classifies and surveys different approaches proposed for performance monitoring, in particular the optical signal-to-noise ratio (OSNR) monitoring, in transparent reconfigurable WDM networks. Some considerations for future monitoring schemes are discussed.

Performance Modeling for Data Monitoring Services in Smart Grid: A Network Calculus Based Approach

This paper focuses on solving the modeling issues of monitoring system service performance based on the network calculus theory.First,we formulate the service model of the smart grid monitoring system.Then,we derive the flow arrival curve based on the incremental process related functions.Next,we develop flow arrival curves for the case of the incremental process being a fractional Gaussian process,and then we obtain the generalized Cauchy process.Three technical theorems related to network calculus are presented as our main results.Mathematically,the variance of arrival flow for the continuous time case is derived.Assuming that the incremental process of network flow is a Gaussian stationary process,and given the auto-correlation function of the incremental process with violation probability,the formula of the arrival curve is derived.In addition,the overall flow variance under the discrete time case is explicitly derived.The theoretical results are evaluated in smart grid applications.Simulations indicate that the generalized Cauchy process outperforms the fractional Gaussian process for our considered problem.

某大桥墩柱变形监测与结构性能分析研究

针对桥墩微小变形难以发现,易造成较大危害的问题,论文以四川东部的一座预应力混凝土桥为例,分析其工程现状及监测方案优缺点,通过控制网点部署的方式对桥墩变形监测进行监测。监测结果表明,大桥的平面基准网络稳定,符合正常运用标准。所有记录的位移值均在结构设计允许的安全边界之内,没有检测到任何不寻常的突变现象。

一种遥测智能终端设备双CPU架构系统软件升级的方案研究

针对双CPU架构的遥测智能终端设备提出了一种基于Inotify机制与NFS文件系统的系统软件升级方案。Inotify是Linux操作系统中一种实时监控文件系统变化的机制,NFS(Network File System)文件系统是一种网络文件系统,允许用户在不同的计算机或者各节点之间共享文件和存储资源。详细介绍了该方案的设计和实现,并对方案的性能进行了分析和评估。实验结果表明,该方案具有较高的实时性和可靠性,可有效提高遥测智能终端设备多CPU架构的升级效率。

医院网络运维中的负载均衡与流量优化策略

针对医院网络系统面临的挑战,文中提出并实现了一种包括网络拓扑重构、流量优化、应用性能提升和安全加固在内的综合优化方案。通过部署高性能设备、实施QoS策略、优化数据传输和加强安全措施,显著提升了网络性能。经过优化,医院的网络吞吐量、核心系统响应时间、远程会诊质量和安全性均得到了明显改善,医护人员的满意度显著提升,为推动医疗信息化和智慧医疗建设提供了借鉴。

移动通信网络性能监控与预测维护技术分析

随着移动通信技术的发展和进步,网络规模不断扩大,网络复杂程度逐渐提升,传统的网络性能维护模式已难以满足现代通信网络的需求。在新的时代背景下,探讨移动通信网络性能监控和预测维护技术的应用问题,具有较强的现实意义。

基于全光网络的光纤通信传送网性能监测技术

文章针对全光网络中光纤通信传送网的性能监测问题,提出一种基于光电检测、信号处理、大数据分析以及机器学习等技术的智能监测系统。该系统采用模块化设计,可实现光信号质量监测、网络性能分析、故障诊断与定位等功能。通过实验验证,该系统在实时性、准确性、可靠性等方面均达到预期目标,为全光网络的智能化管理和优化控制提供了有力支撑。

基于5G通信技术的智能配电网实时监控系统

本研究聚焦于智能配电网实时监控系统的性能优化,通过引入5G通信技术,构建仿真实验环境进行全面评估。研究对比分析了基于5G通信技术与4G通信技术的智能配电网实时监控系统在数据传输延迟、数据丢失率、系统稳定性、处理能力以及资源利用率等关键指标上的性能差异。实验结果表明,5G通信技术显著提高了智能配电网实时监控系统的整体性能,具有更低的延迟、更高的稳定性和效率。

基于模型驱动双向主动测量协议的IP网络遥测系统研究与实现

IP层的OAM技术中,双向主动测量协议(TWAMP)通过主动向网络注入测量报文,并根据对测量报文进行采集分析来实现任意2个具备IP可达性网络节点之间的网络层性能监测。文章首先分析了TWAMP测量方案在实际运营部署时存在的主要问题和挑战,然后针对网络的运维管理日益智能化的网络发展趋势下,需要对网络运行状况进行连续实时主动监测的新需求进行了相关分析。基于此,文章提出了一种面向互联网遥测架构的硬探针TWAMP网络监控系统,以此实现IP传送网的性能监测改进,为实现更实时可靠的智能运营提供有力支撑。

一个基于SNMP的简单网络管理系统的设计与实现

本文提出了一个基于 SNMP的简单网络管理系统 ,首先讨论这个系统的功能和设计 ,然后以此作为基础 ,给出这个系统的实现方案 。

网络仿真软件ns的结果输出和分析

为了在仿真环境下对网络协议进行性能评价，必需对仿真结果进行有效的分析和处理．介绍了ｎｓ的两层体系结构及安装过程，描述了ｎｓ的跟踪和监视机制，并在分析ｎｓ的跟踪和流监视输出格式的基础上讨论了仿真结果的处理．除ｎｓ提供的标准输出外，还以实例说明了如何根据自己的需要在仿真脚本中对数据进行处理，只输出感兴趣的结果．

Internet网络性能实时监测关键技术研究

详细探讨了基于 OWDP(One way delay and package loss protocol)网络性能监测及评价方法 ,提出了OWDP在实际应用中存在的时钟同步和最小资源占用问题 ;并采用 GPS(全球定位系统 )技术和 Poison信息流得到解决 .在此基础上 ,设计并实现了实时性能监测系统 ,实验结果表明 :该系统满足 CA INONET(中国高速信息示范网 )性能实时监测各项要求 ,具有测量精度高 ,实时性好 ,配置灵活等优点 .