Best practices for network observability and enriching network telemetry

Network observability, a cornerstone of modern network management, provides a comprehensive understanding of network performance, health, and security. As networks evolve with the introduction of cloud services, IoT devices, and hybrid infrastructures, the role of robust network telemetry becomes increasingly crucial.

Network telemetry, the practice of collecting, sending, and analyzing data on network operations, is the bedrock of effective observability. It empowers IT teams to ensure optimal performance and swift troubleshooting, making it a vital tool in your network management arsenal.

To truly harness network telemetry's power, it must be effectively enriched. This involves augmenting the raw telemetry data with additional context and transforming it into actionable intelligence. This process can help identify issues before they affect users, optimize network performance based on real-time data, and enhance security by swiftly detecting anomalous behaviors.

This post covers best practices for enhancing network observability with better network telemetry. It serves as a guide for network operators to maximize telemetry investments; streamline operations; and develop a more dynamic, responsive, and resilient network infrastructure.

Challenges in achieving network observability

Network observability is crucial for maintaining the efficiency and effectiveness of an organization's network infrastructure. However, achieving complete network observability can be demanding.

Cloud services can complicate data collection, because they have proprietary performance monitoring methods designed to be used within that cloud. Metrics and logs, however, can be copied to external storage. Other critical obstacles organizations may encounter include:

• Real-time data processing: Network telemetry data must be collected and processed in real time to facilitate immediate actionable insights.
• Integration of diverse data sources: Modern networks are often heterogeneous environments, with a mix of legacy systems, IoT devices with various protocols and data formats, and cloud services with diverse proprietary metrics and logging systems that can complicate data collection.
• Data volume and complexity: As networks grow, so does the volume of data generated; across multiple devices, applications, and services, this increase can be massive.
• Privacy and compliance: With stricter data protection regulations, organizations must ensure that their network observability practices comply with legal standards.

To counter these challenges, organizations can adopt best practices in a few key areas, outlined below.

Network telemetry

Enriching network telemetry involves enhancing the methods and tools used to collect, analyze, and utilize data from network environments.

Data collection tools include OpenNMS, SolarWinds, and vendor-supplied monitoring software. The crucial feature of these solutions is the ability to forward metrics to your network telemetry system.

Alongside these tools, implementing best practices can significantly aid in achieving detailed network observability and proactive management, as well as improving performance and security. Below, we provide a comprehensive guide.

Utilize comprehensive monitoring tools

The foundation of enriched network telemetry lies in employing a range of monitoring tools that provide various perspectives on your network. Examples of such solutions include:

• Network performance monitors (NPMs)
• Application performance management (APM)
• Packet capture tools
• Synthetic monitoring
• Security information and event management (SIEM)

Automate the telemetry processes

Automation is critical in managing the scale and complexity of modern networks. Leveraging its potential will include implementing:

• Automated data collection: Implement automated mechanisms to gather data continuously across the network.
• Real-time analysis and alerts: Automate the processing of real-time telemetry data to discover and mitigate suspect behavior/issues causing poor performance.
• Automated remediation: Integrate automated responses to common network issues to increase operational efficiency and reduce downtime.

Leverage machine learning and AI

Machine learning (ML) and artificial intelligence (AI) can significantly enhance the capabilities of network telemetry by providing advanced analytics and predictive insights. These include:

• Predictive analytics: Use ML algorithms to predict potential network failures and performance degradations before they occur.
• Anomaly detection: Employ AI to monitor for atypical behavior that could indicate security threats or operational problems.
• Capacity planning: Apply AI models trained on historical data and trend analysis to predict future network capacity requirements.

Data collection

Effective data collection practices are crucial; they ensure that the most relevant and valuable information is being gathered and done so in a way that doesn't overwhelm system resources or complicate data analysis. Below, we cover a few vital best practices for data collection in network telemetry.

Identify relevant data sources

The first step in enriching network telemetry is selecting the most suitable data sources to provide the most meaningful insights for your needs. This involves:

• Comprehensiveness: Include a variety of sources such as network devices (routers, switches), servers, applications, and security systems.
• Relevance: Focus on data directly impacting business outcomes and network performance, such as traffic data, error rates, and transaction times.
• Quality and integrity: To maintain the integrity of your telemetry, verify data to make sure it is trustworthy and correct.

Managing data overload

With the suitable data sources identified, the challenge becomes managing the sheer volume of data efficiently. A few best practices can greatly help in this process:

• Data filtering: Implement preliminary filtering at the data collection point to reduce noise and focus on the most critical data. For instance, capture only specific packet types or transactions that fall under a given set of criteria.
• Sampling: Instead of collecting all data, statistical sampling methods collect a representative subset, which reduces the storage and processing requirements while still providing meaningful insights.
• Aggregation and compression: Aggregate data at the source and summarize it to avoid having to transfer and store high volumes of data. Compression techniques can also minimize your data footprint.
• Data lifecycle management: Implement policies for data retention based on compliance requirements and practical value, ensuring efficient use of storage resources.

Prioritization and stratification

Different types of data have distinct levels of importance and urgency. Managing this involves:

• Prioritization in real time: Prioritize data processing based on its importance to business functions or potential impact on network performance.
• Stratified storage: Leverage tiered storage solutions to better manage data. Critical data that requires real-time analysis can be stored on faster, more accessible storage, while less critical data or data accessed less frequently can be archived.

Data analysis and visualization

Analyzing and visualizing telemetry data is a must for improving network observability and obtaining actionable insights. These methods help organizations identify network issues and address them proactively.

Proper analytical techniques with compelling visualizations can significantly enhance the insights gained from network telemetry data. The following is a selection of tools at your disposal:

• Descriptive statistics: To understand the data's central tendency and variability, begin by summarizing key statistics including variance, mean, mode, median, and standard deviation.
• Time-series analysis: Analyze data over a given period to uncover trends, anomalous behavior, and other patterns. Tracking moving averages, trend lines, and seasonal decomposition are examples of how to get the information you need.
• Correlation analysis: Recognize how changes to one variable impact other variables in telemetry data by identifying the relationships between them. Correlation coefficients and scatter plots can visually represent these relationships.
• Anomaly detection: Identifying unusual patterns in data points within a data set where they contrast widely from expected behavior indicates potential network issues or security threats.
• Cluster analysis: This statistical method for grouping comparable data points into clusters according to their features can reveal hidden structures or behaviors.

Selecting the appropriate tools and platforms for visualizing telemetry data can significantly impact data usability. Effective visualization techniques also enable faster and more accurate interpretations. Organizations should consider implementing:

• Dashboards: Use interactive dashboards for a real-time view of network performance and alerts. Tools such as Grafana, Tableau, and Kibana offer customizable interfaces that can integrate data from multiple sources.
• Graphs and charts: Employ various visual representations, such as line graphs, bar charts, heat maps, and scatter plots, to depict different aspects of network data clearly and intuitively.
• Geospatial maps: For networks spread over large geographical areas, use maps to visualize metrics like signal strength, latency across regions, or the status of remote devices.
• Temporal visualizations: Show how metrics change over time to identify trends or detect when anomalies occur.
• Comparative visualizations: Use charts to show performance benchmarks or comparisons between network segments.
• Hierarchical data visualization: For complex networks, visualizing data in a hierarchical (tree-like) manner can help demonstrate the impact of one part of the network on another.

Security and compliance considerations

Data security, privacy, and regulatory compliance are crucial considerations in network telemetry. Organizations should thus ensure they have guidelines in place to safeguard any collected data. This will entail:

• Data anonymization: Where necessary, anonymize data to comply with privacy laws and regulations, particularly when handling personally identifiable information (PII).
• Regulatory compliance: Be aware of and compliant with industry-specific regulations such as GDPR, HIPAA, or CCPA; these may dictate how specific data is collected, processed, and stored.
• Secure storage practices: Use access controls and encryption to safeguard against unapproved access to stored telemetry data and ensure compliance with privacy regulations.

Future trends in network telemetry

Knowing the emerging trends and technologies shaping network telemetry can also help organizations ensure that their network observability practices are up-to-date.

Key emerging trends include:

• Further integration of AI and ML for network observability and security
• Auto-remediation to attempt to combat the growth and evolution of threats to network security
• Rise in demand for SaaS-based network telemetry providers as networks become increasingly complex

Conclusion

Achieving comprehensive network observability requires a multi-faceted approach encompassing various best practices for the topics covered above.

Companies can gain valuable lessons from real-world examples of successful telemetry implementations. Many conferences present best practices and case studies you can leverage to boost your own network observability.

Organizations that implement best practices can attain full network observability, reduce network downtime, and guarantee the seamless operation of their network infrastructure.