26 Network Performance Metrics to Measure Network Health in 2024
The ability to measure network performance is more crucial than ever for businesses to ensure seamless operations. However, the complexity of modern networks, especially with protocols like the Transmission Control Protocol, often leads to complications in accurately maintaining network health and efficiency. To address this challenge, understanding and effectively utilizing network performance metrics becomes the key resolution.
This article explores key and other network performance metrics, the tools for measuring these and how to analyze the data.
What is network performance monitoring?
Network performance monitoring (NPM), a fundamental aspect of network management, involves the continuous observation and analysis of network performance. It aims to ensure the network’s ability to efficiently and reliably handle data traffic and support various applications.
NPM utilizes an array of network performance monitoring tools and software to assess various aspects of network performance, including:
- Traffic flow
- Network infrastructure
- Network latency
- Overall network availability and efficiency
What are the tools for measuring network performance?
Network performance monitoring software is essential for maintaining optimal network operations as it offers real-time visibility into the health and performance of a network, unlike determining network performance manually. Network monitoring tools enable proactive detection and troubleshooting of issues, ensuring minimal downtime and disruptions. Additionally, they provide valuable insights for strategic planning and capacity management, and contribute to maintaining network security and compliance with industry standards.
What are the key network performance metrics?
This refers to the time it takes for a data packet to travel from the source to the destination. It’s usually measured in milliseconds (ms). Lower latency is vital for real-time applications like video conferencing and online gaming.
Jitter is the variation in the time of arrival of packets. Inconsistent latency can cause jitter, which can be problematic for VoIP calls and video streaming where timing is crucial.
3- Packet loss
This metric indicates the percentage of lost data packets that are sent but not received by the destination. Packet loss can lead to significant degradation in network quality, affecting voice and video quality, and overall data integrity.
This measures the maximum rate of data transfer across a given path in the network. It’s typically measured in bits per second (bps). High bandwidth indicates a higher capacity for data transmission, which is crucial for data-heavy operations.
Different from bandwidth, throughput measures the actual rate of successful data transfer over a network. It’s impacted by various factors including bandwidth, the network’s setup, and the quality of the data being sent.
What are other metrics for analyzing network performance?
Apart from these essential network performance metrics, there are others that can be used to analyze the performance of the system:
6- Error rate
This refers to the number of corrupted packets over a network in relation to the total sent. A high error rate can indicate problems with network hardware or interference.
This metric measures the time a network is available and operational, often represented as a percentage. High availability is crucial for critical systems where downtime can lead to significant business losses.
This measures the extent to which network resources are being used. It helps in capacity planning and ensuring that the network can handle peak demands.
9- Round-Trip Time (RTT)
RTT measures the time it takes for a signal to go from the source to the destination and back again. It’s important for understanding the responsiveness of the network.
10- Quality of Service (QoS)
While not a metric per se, QoS refers to the overall performance of the network and its ability to prioritize different types of traffic, which is essential in ensuring that critical applications receive the bandwidth they require.
11- Connection time
The time it takes to establish a connection between two points in the network. This can be important for systems where rapid connection establishment is critical.
12- Server response time
Measures how long it takes for a server to respond to a request. This is particularly important in web-based services.
13- Network congestion levels
Indicates the level of traffic load on the network. High congestion can lead to increased packet loss and latency.
14- Retransmission rate
The frequency with which data packets are retransmitted over the network. High retransmission rates can indicate problems with network stability or congestion.
15- Error rates by type
Detailed breakdown of different types of errors (like CRC errors, frame errors, etc.) can help in diagnosing specific network issues.
16- Session duration
The length of time a network session remains active. This can be important for understanding user behavior and network usage patterns.
17- Signal strength
In wireless networks, the strength of the signal is a key factor in performance, affecting data rates and connection quality.
18- Signal-to-Noise Ratio (SNR)
In wireless communications, SNR compares the level of the desired signal to the level of background noise. A higher ratio means a clearer and better quality signal.
19- Network reachability
Measures the ability to connect to and communicate with various parts of a network.
Similar to throughput, goodput measures the rate of successful delivery of payload data (excluding protocol overhead and retransmitted data).
21- Path change frequency
Indicates how often the path taken by data through the network changes, which can affect performance and stability.
22- DNS lookup time
The time it takes to resolve a domain name into an IP address. This is crucial for web browsing and accessing online services.
23- Application-specific metrics
For networks supporting specific applications, metrics tailored to the performance requirements of these applications (like video streaming quality, voice clarity in VoIP, etc.) can be crucial.
The total data carrying capacity of a network. Understanding capacity helps in planning for expansion and scaling.
25- Network efficiency
Measures how effectively the network uses its resources. This can include metrics like bandwidth efficiency, power efficiency in wireless networks, etc.
26- Cost per data unit
In some contexts, particularly in business environments, understanding the cost associated with transmitting a certain amount of data can be important for budgeting and cost optimization.
How can the network performance data be analyzed?
Analyzing and utilizing network performance data is crucial for maintaining optimal network operations, ensuring security, and making informed decisions for future network planning and improvements. Here’s a guide on how to effectively analyze and use network performance data:
1. Data collection
- Use network monitoring tools to collect data on key performance metrics like bandwidth usage, latency, packet loss, jitter, throughput, error rates, etc.
- Ensure continuous monitoring for real-time data and historical analysis.
2. Data analysis
- Identify Trends: Look for patterns in network performance over time. This could include peak usage times, recurring periods of high latency, or frequent packet loss.
- Benchmarking: Compare current network performance against established benchmarks or standards to evaluate if the network is performing optimally.
- Root Cause Analysis: When issues are identified (like high latency), delve deeper to find the root cause (such as network congestion, hardware failure, etc.).
- Correlation Analysis: Determine how different metrics affect each other. For example, how bandwidth utilization impacts throughput and latency.
3. Performance optimization
- Address Identified Issues: Use your analysis to resolve issues. This might involve rerouting traffic, upgrading hardware, or optimizing configurations.
- Capacity Planning: Use historical data to predict future network needs and plan for capacity upgrades.
4. Security analysis
- Anomaly Detection: Look for unusual patterns that might indicate security breaches, such as unexpected spikes in traffic, which could signify a DDoS attack.
- Compliance Monitoring: Ensure that network usage complies with relevant policies and regulations.
5. Reporting and documentation
- Create reports for different stakeholders (IT staff, management, etc.) highlighting network health, performance issues, and improvements.
- Maintain documentation for future reference and for tracking the evolution of network performance over time.
6. Proactive management
- Implement alerts based on thresholds for key metrics to get notified of potential issues before they become critical.
- Regularly review and adjust thresholds and alerts as the network and its usage evolve. We have an article on proactive network performance monitoring.
7. Cost management
- Analyze network performance data to identify inefficient use of resources.
- Optimize network configurations and resource allocation to reduce costs without impacting performance.
8. Quality of Service (QoS) adjustments
- Prioritize network traffic based on business needs to ensure critical applications have the necessary resources.
9. Future planning
- Use trend analysis for long-term planning regarding network expansion or upgrades.
- Assess the impact of new technologies or applications on the network before deployment.
10. User Experience monitoring
- Monitor metrics that directly impact user experience, like latency and jitter in VoIP and video conferencing, to ensure user satisfaction.
If you have questions or need help in finding vendors, feel free to reach out:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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