Wireless, 5G
Article | May 18, 2023
Discover key network performance metrics to enhance user experience. Explore in-depth latency, throughput, jitter, packet loss, VOIP quality, and MOS score to optimize network performance analysis.
Contents
1. Importance of Network Performance Metrics for Performance Analysis
2. Critical Key Network Performance Metrics to Monitor
2.1 Latency
2.2 Throughput
2.3 Jitter
2.4 Packet Loss
2.5 VOIP Qualiy
2.6 MOS Score
3. Steps to Monitor and Measure Network Performance
4. Significance of Monitoring Metrics in Network Troubleshooting
4.1 Provides Network Visibility
4.2 Prevents Network Downtime
4.3 Observe Bandwidth Usage
5. Overcome Monitoring Challenges in Network Performance Metrics
6. Key Takeaway
1. Importance of Network Performance Metrics for Performance Analysis
Network performance involves analyzing and evaluating network statistics to determine the quality of services provided by the underlying computer network. Considering various key network metrics, it is primarily measured from the end-users’ perspective. Measuring these metrics, analyzing performance data over time, and understanding the impact on the end-user experience is essential to assess network performance.
Measuring network performance requires considering factors such as the location and timing of measurements. For instance, network performance may differ when comparing paths between cities or during periods of varying user demands throughout the day. Therefore, a comprehensive approach to monitoring network performance involves identifying these variables and identifying areas for improvement.
Network performance metrics offer valuable insights into any network infrastructure and services. These metrics provide real-time information on potential issues, outages, and errors, allowing one to allocate IT resources efficiently. Understanding end-user demands can create an adaptive network to meet future business needs. However, comprehensive monitoring requires an advanced network monitoring tool to gather, analyze, and interpret data effectively, optimizing network performance. Leveraging relevant metrics can improve network performance, help make informed decisions, enhance network reliability, and deliver a superior user experience.
2. Critical Key Network Performance Metrics to Monitor
2.1 Latency
Latency, or network delay, is a crucial performance metric in network monitoring and management. It quantifies the time required to transmit data between destinations. Factors like packet queuing and fiber optic cabling affect network latency. Consistent delays or sudden spikes in latency indicate significant network performance issues. Monitoring and minimizing latency are essential for ensuring optimal network performance. By actively tracking latency, organizations identify and address issues that may cause delays in data transmission, thereby improving overall network responsiveness and minimizing disruptions for end-users.
2.2 Throughput
Throughput metrics for network monitoring enable measurement of the data transmission rate across various network segments. Unlike bandwidth, which represents the theoretical data transfer limit, throughput reflects the successful delivery of data packets to their destination. Variations in throughput can occur across different network areas. A low throughput indicates the presence of dropped packets requiring retransmission, and highlights potential performance issues that need attention. Monitoring throughput is crucial for effective network management. By monitoring this performance metric, organizations can gain insights into the actual data transmission rate, ensuring that it aligns with expected levels.
2.3 Jitter
Jitter, a key performance metric in network monitoring, refers to the variation in delay between packets, measured as the difference between expected and actual arrival times. It results due to network congestion, routing issues, or other factors, leading to packet loss and degraded application performance. Jitter disrupts the standard sequencing of data packets and can arise due to network congestion or route changes. Monitoring jitter is crucial for identifying and addressing network stability issues and ensuring reliable data transmission. By actively monitoring this performance metric, organizations can address variations in packet delay, mitigating issues that leads to packet loss and enabling proactive troubleshooting.
2.4 Packet Loss
Packet loss, a performance management network monitoring metric, represents the number of data packets lost during transmission. It directly affects end-user services, leading to unfulfilled data requests and potential disruptions. Packet loss can arise from various factors, including software problems, network congestion, or router performance issues. Monitoring the entire process precisely to detect and address packet loss, ensures reliable data transmission and optimal network performance. Monitoring packet loss with the right network monitoring software enables timely troubleshooting and optimization of network infrastructure, ultimately enhancing overall network reliability and performance.
2.5 VOIP Quality
VoIP (Voice over Internet Protocol) quality is a crucial network performance metric. It refers to the overall performance of a VoIP system in delivering clear and reliable voice communications over the Internet, replacing traditional phone lines. Factors influencing VoIP quality include network bandwidth, latency, packet loss, jitter, and the quality of end-user devices. Monitoring VoIP quality ensures optimal system functionality and high-quality voice communications. Key performance indicators (KPIs) such as mean opinion score (MOS), jitter, latency, packet loss, and call completion rates are utilized to assess and optimize VoIP quality.
2.6 MOS Score
Mean opinion score (MOS) is a vital performance metric in network monitoring, rating the perceived quality of a voice call on a scale of 1 to 5. It is a standardized measurement developed by the ITU, an international agency focused on enhancing communication networks. Initially designed for traditional voice calls, the MOS has been adapted to evaluate Voice over IP (VoIP) calls. The MOS score considers various factors, including the specific codec employed for the VoIP call, providing a comprehensive assessment of voice calls quality in network monitoring.
3. Steps to Monitor and Measure Network Performance
Step 1: Deploy a Software for Network Monitoring
To effectively measure network performance, deploying dedicated network monitoring software is crucial. While temporary tools like traceroutes and pings can provide insights into ongoing problems, they are insufficient for troubleshooting intermittent network issues. Relying on periodic tools for intermittent issues is reliant on chance, as it may only detect problems when they occur during tool usage. By implementing comprehensive network monitoring software, one can proactively monitor and analyze network metrics, historical data, and performance, allowing for timely detection and resolution of both ongoing and intermittent network issues.
Step 2: Distribute Monitoring Agents
For comprehensive network performance measurement, businesses must distribute monitoring agents strategically across key network locations. These specialized software agents continuously monitor network performance using synthetic traffic, simulating and assessing the end-user perspective.
By distributing Monitoring Agents, organizations can:
• Measure key network metrics, including jitter, packet loss, and throughput.
• Identify and troubleshoot intermittent network issues that are challenging to pinpoint.
• Receive alerts regarding any performance degradation, ensuring a timely response.
• Collect valuable data for in-depth troubleshooting and analysis, facilitating proactive network management and optimization.
Step 3: Measure Network Metrics
After deploying the monitoring agents, they continuously exchange synthetic User Datagram Protocol (UDP) traffic, forming a network monitoring session. During this session, the agents measure network performance by evaluating key metrics and conducting network traffic analysis. The metrics used in the analysis include specific parameters, and the results of these measurements are presented in a network response time graph, providing a visual representation of the network's performance characteristics. Monitoring and analyzing these metrics enable organizations to gain valuable insights into network performance, facilitating informed decision-making and convenient network performance troubleshooting.
4. Significance of Monitoring Metrics in Network Troubleshooting
4.1 Provide Network Visibility
Monitoring metrics plays a vital role in network troubleshooting by offering network visibility. They enable the identification of performance bottlenecks, configuration problems, and security vulnerabilities that detrimentally affects network performance. These issues can be addressed through targeted troubleshooting efforts, resulting in improved network performance and enhanced end-user experience. Organizations identify and resolve network issues by monitoring metrics, ensuring optimal network functionality and overall business productivity.
4.2 Prevent Network Downtime
Effective monitoring metrics are instrumental in preventing network downtime, a costly concern for businesses. Swift identification and resolution of network issues through proactive network performance troubleshooting help minimize downtime, ensuring uninterrupted business operations. By promptly addressing potential problems, network troubleshooting safeguards against lost productivity, revenue, and customer dissatisfaction. Maintaining a proactive approach to monitoring and resolving network issues to enhance network reliability and business continuity.
4.3 Observe Bandwidth Usage
Monitoring metrics are essential in network troubleshooting as they enable the observation of bandwidth usage. This allows organizations to detect abnormal or excessive utilization, pinpoint key performance issues and ensure optimal resource allocation. It allows for identifying critical bandwidth-hogging applications or network intrusions, helping experts take immediate action to mitigate risks, safeguard data, and protect the overall network integrity. Additionally, experts can optimize network performance and ensure a seamless user experience for organizations relying on efficient network infrastructure.
5. Overcome Monitoring Challenges in Network Performance Metrics
Enterprises seeking to ensure optimal network performance and improve overall business operations must overcome network monitoring obstacles. Effectively monitoring, tracking, and improving network performance requires a strategic combination of skilled personnel, advanced technologies, and well-defined strategies. Failing to address these requirements results in various challenges that hinder the ability to enhance network performance effectively.
The challenges that businesses often encounter include managing scalability, handling massive data volumes, achieving real-time monitoring, dealing with multi-vendor environments, addressing network security and privacy concerns, and adapting to evolving network demands. Each obstacle presents unique complexities that require tailored approaches and expert insights.
To overcome these challenges, enterprises must invest in comprehensive monitoring tools capable of handling the scalability demands of growing networks. These tools should provide real-time network visibility, robust analytics capabilities, and intelligent data filtering mechanisms to extract meaningful insights from vast network data. Establishing clear monitoring objectives aligned with business goals and defining key performance indicators (KPIs) are essential in effectively addressing network performance challenges.
6. Key Takeaway
Monitoring network performance metrics is crucial for assessing the quality of services a computer network provides from an end-user perspective. It involves continuously tracking and analyzing key metrics such as latency, throughput, jitter, packet loss, VOIP quality, and MOS score. Organizations can actively monitor and assess performance, proactively identify intermittent issues, and collect valuable data for in-depth analysis by implementing dedicated network monitoring software and strategically deploying monitoring agents across the network. In addition, it is imperative to emphasize the significance of monitoring metrics in mitigating the potential financial impact of network downtime, enhancing the utilization of available bandwidth resources, and efficiently tackling the complexities inherent in scaling operations, real-time monitoring, diverse vendor ecosystems, security concerns, and the ever-evolving requirements of modern networks.
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5G
Article | September 28, 2023
Transparency, explainability and trust are big and pressing topics in AI/ML today. Nobody wants to find themselves at the receiving end of a black AI box system that makes consequential decisions (e.g., about jobs, healthcare, citizenship, etc.), especially if those decision are unfair, biased, or just plainly not in our favor. And most organizations agree that consumer trust and confidence that AI is being used ethically and transparently are key to unlocking its true potential.
And while there are literally hundreds of documents describing and prescribing AI principles, frameworks and other good things, last year there was no practical tool that could help with implementing transparency. And this tool is AI registers…
The Cities of Helsinki and Amsterdam jointly announce the launch of their public AI registers. The two registers were developed in collaboration with Saidot.ai, a Finnish company which specializes in “bringing transparency to consumer services” and which, to the best of my knowledge, is the only vendor in this space.
The idea for the company grew from the personal frustration of its founder and CEO, Meeri Haataja, who was “seeing how important transparency of AI is for the future of each one of us, and not being able to find too many meaningful ways to act on it.”
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Network Management, Network Security
Article | July 17, 2023
Applications of AI/ML
Modern businesses are adopting Artificial Intelligence (AI) that encompasses disciplines like machine learning (ML), natural language processing (NLP), evolutionary computation, etc., to increase their productivity and management capabilities.
Companies like Qualcomm are using AI and machine learning to improve their customer experience.
“Across many industries, we are currently experiencing the creation of intelligent machines that is using AI to simulate smart behavior.”
-Dr. Vinesh Sukumar, Senior Director- Head of AI/ML Product Management at Qualcomm, ( in an interview with Media7)
The application of machine learning in networking is swiftly taking shape. However, as the problems in modern computer networks are getting tedious to handle, AI tools are being introduced to hard-carry their smooth functioning.
Let’s take a look at how network complexity impacts businesses:
Difference in Network Parameters
Different client devices like laptops, smartphones, CCTV cameras, etc., are connected to a single network. However, their requirements and parameters are different. Therefore, the IT team of the business needs to meet them without compromising the functionality and security of the network.
Users Prefer Wireless Networks
Wireless networks are more complex than wired ones. They perform dynamically depending on the number of users, applications, and other variables.
Impact of Cloud Computing
Most applications are now cloud-based, and such a network has multiple data entry points and requires more support.
User Experience
Deciphering root cause analysis, finding correlation and solutions becomes tedious without an AI/ML model. Complex patterns remain unanalyzed, and this creates a vacuum between the customer and the business.
What Does ML Bring to the Table?
Machine learning applications in networking correlate to solving four types of network problems: clustering, extraction, regression, and classification.
For classification and regression, ML clusters similar data and creates a gap between data groups. It then successfully maps a new set of data to a pre-set continuously valued output. As for extraction, it easily establishes a statistical relationship between the data it analyzes.
Machine learning applications in networking encompass the following:
Automation and Cognitive Computing
ML enables automation in data processing by eliminating the human error factor and constantly improving with time. It analyzes data, improves the productivity, security, and health of the network. Cognitive computing allows processing diverse data sets, detecting and finding root causes and common traits within the system.
Network Monitoring & Security
Network monitoring is used to solve problems in a large dataset by deciphering the hidden pattern in the data. It then predicts the outcome for clustered data, malware attacks, or impending network failure. It recognizes impending threats in time and sends out warnings. ML uses anomaly-based intrusion, misuse-based intrusion, or hybrid intrusion to prevent misuse, modification, unauthorized access, or malfunction.
Traffic Prediction, Classification, and Routing
Network traffic prediction is important to handle any mishaps proactively. Network analysis in machine learning is done by using Time Series Forecasting (TSF). By using a regression model solution, TSF finds a correlation between the traffic volume in the future and the traffic previously observed.
Traffic classification ensures Quality of Service (QoS), planning ahead for capacity, security, performance analysis, etc. It helps with proper resource utilization by pinpointing unnecessary traffic in a critical application.
Factors like cost-effectiveness, link utilization, operational capabilities, and policies are also considered by the ML model.
Congestion Control
ML models control the number of packets that enter a network to ensure that the network is stable, fairly utilize resources, and follow queue management employed for congestion control.
Efficiently Managing Resources
ML efficiently manages network resources like the CPU, frequency, switches, memory, routers, etc., by using analytical decision-making.
ML Learning Curve
ML models learn in the following ways:
Pitfalls
Like any other technology, machine learning application in networking comes with pitfalls and limitations. Here are a few:
Data Quality
The efficiency of an ML model is based on the quality, quantity, and diversity of data it processes so it can deduce patterns or identify root causes. Most ML models use simplistic synthetic data for training, validation, and performance. The same cannot be said about practical settings because the data comes from different applications and services and is more complex.
Feasibility
There are scalability and feasibility issues because each network and application is different. Moreover, there are no set standards for uniformity for implementation which makes it hard to set benchmarks or best practices. Control over autonomic networks is distributed and remains limited based on the vendor’s specific devices.
Predictive Analysis and Its Cost
Network analysis and machine learning prediction require additional accurate and effective monitoring investments. Moreover, fault management may have some potholes as there may be a scarcity of normal fault data.
High FPR (False Positive Rates)
Anomaly detection by ML in networking has not created enough buzz in the industry because it generates high FPRs during operations. Also, no detailed anomaly report is generated, so no anomaly history log can be maintained.
Striking a Balance
ML requires time to learn and mitigate issues. It is difficult to identify, in advance, how complex the ML’s approach will be. Striking a balance between the performance and computational cost is difficult. Deciphering comprehensive evaluation metrics is also a tedious task.
No Theoretical Model
There is no theoretical model, in turn, a unified theory, for ML in networking, so each network may have to be learned separately. The current machine learning applications in networking are made keeping in mind certain applications. Over time, more research to tailor ML for certain networks needs to be done. Cross-domain experts who understand both ML and networking are also rare.
Solutions
Software Defined Networking (SDN)
CISCO helped PwC Italy set up a secure network at their new twenty-eight-floor tower with the help of their SD-Access product. PwC wanted a secure, robust network with increased Wi-Fi and wired connectivity for their 3000 employees by streamlining network operations.
“We needed a robust and highly reliable wireless network infrastructure that’s as advanced as the tower itself.”
-Simone Demaria,Network Architect and Infrastructure Manager at PwC Italy
By applying Software Defined Network (SDN), IT personnel can remotely govern network policies in real-time through open interfaces, so traffic engineering is easily possible. SDN also contributes to network virtualization.
SDN supports the upcoming 5G ecosystem. When combined with NFV and VNF, SDN can revolutionize networking.
Going Beyond Traffic Volume & Prediction
To tackle the limitations that TSF-based traffic prediction models have, leveraging features beyond traffic prediction and concentrating on traffic interpolation and sampling could be viable. Research is ongoing on this possibility.
Summing It Up
As the influx of data keeps on increasing, the complexity of networks will increase in tandem. For successfully implementing ML for streamlining networking, the ML approaches we are aware of today need to be upgraded to accommodate multi-layer networks and multi-tenancy so autonomic networking can be a reality.
FAQs
How Can ML Help in Making Networking Smarter?
ML can streamline the network by automation, threat detection, and improving its performance.
How Complex Is Integrating ML into Networking?
The complexity depends on the type of network you are integrating it into.
What to Keep in Mind Before Using Ml in Networking?
Consider investment costs, data availability, feasibility, and scalability.
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Article | May 19, 2021
In today’s shop-from-anywhere world, the model for success continues to change. Tried and true techniques are becoming obsolete as consumer expectations evolve, demand volatility rises, and supply chain disruptions become more frequent. Retailers are also dealing with online shopping surges that add new complexities to existing data strategies due to an influx of raw, unprepped, and largely underutilized data.
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