Unlock the Power of AIML in Cisco DNA Center for Enhanced Network Management

Unlock the Power of AIML in Cisco DNA Center for Enhanced Network Management

Table of Contents

1. Introduction
2. Setting Up AIML Feature in Cisco DNA Center
3. Enabling AI and Network Analytics
4. Selecting Data Storage Location
5. Sending Network Data to the Cloud
6. Benefits of AIML in Cisco DNA Center
7. The Issues Dashboard
8. Drill Down Into Specific Issues
9. Location and Impacted Clients
10. Baseline and Real Onboarding Failures
11. Probable Network Causes
12. Failed Distribution and Root Cause Analysis
13. AI Network Insights
14. Deviations in Radio Throughput
15. Client Count Deviations
16. Access Point Summary and Analysis
17. Network Heat Map and Client Count
18. Conclusion

🔍 Introduction In this article, we will explore the AIML (Artificial Intelligence and Machine Learning) feature in Cisco DNA Center. AIML empowers network engineers with advanced analytics and insights to better manage and troubleshoot their networks. By utilizing AIML, engineers can proactively identify issues, analyze patterns, and make data-driven decisions for enhanced network performance and user experience.

🛠️ Setting Up AIML Feature in Cisco DNA Center To start leveraging the AIML feature in Cisco DNA Center, you need to configure the AI Network Analytics settings. Access the main menu, navigate to the system settings, and select the external servers option. Here, you will find the Cisco AI Analytics option, which allows you to enable the AI Network Analytics. A detailed step-by-step process will be discussed in this section.

📊 Enabling AI and Network Analytics Once the AI Network Analytics is enabled, you can select the desired location for data storage. Cisco DNA Center sends the collected network data to the cloud, creating a large data lake for machine learning algorithms to run and uncover patterns and insights. You can choose locations based on your preferences and requirements, ensuring efficient data analysis and correlation.

💡 Benefits of AIML in Cisco DNA Center AIML in Cisco DNA Center offers numerous benefits to network engineers. By setting baselines for key KPIs (Key Performance Indicators), AIML helps identify what is normal behavior for different time periods, such as weekdays or weekends. This eliminates false alarms and reduces noise, allowing engineers to focus on real issues and prioritize their efforts effectively.

📟 The Issues Dashboard The Issues Dashboard in Cisco DNA Center provides an overview of network issues detected by AIML. It presents a tab specifically driven by AI, showcasing the identified issues. By accessing this dashboard, engineers can gain insights into any excessive failures, connection issues, and more. This section will guide you through exploring the Issues Dashboard in detail.

🔍 Drill Down Into Specific Issues Within the Issues Dashboard, engineers can drill down into specific issues for a comprehensive analysis. This section will highlight how engineers can explore detailed information about issues, including the location, impacted sites and clients, baselines, and real onboarding failures. A step-by-step guide on troubleshooting and resolving these issues will also be provided.

📍 Location and Impacted Clients Understanding the location and number of impacted clients is crucial for effective issue resolution. By accessing the AI-driven tab within the Issues Dashboard, engineers can gain insights into the specific sites and clients affected by network issues. This section will discuss how location data and impact analysis can aid engineers in troubleshooting.

📈 Baseline and Real Onboarding Failures AIML sets baselines for KPIs, indicating normal behavior for different time periods. This section will explain how engineers can use baselines and identify real onboarding failures that deviate from the established norms. By distinguishing between real issues and false alarms, engineers can streamline their troubleshooting process and allocate resources efficiently.

🔬 Probable Network Causes AIML not only detects network issues but also provides insights into the probable network causes. By analyzing various KPIs, engineers can identify potential factors contributing to the detected issues. This section will explore how AIML helps in root cause analysis and provides suggestions for further investigation and resolution.

📊 Failed Distribution and Root Cause Analysis Examining the distribution of failures and conducting a root cause analysis is crucial for comprehensive troubleshooting. This section will delve into the failed distribution tab, which provides detailed information about access points, clients, and failure reasons. Network engineers can use this data to pinpoint the causes of failures and apply appropriate fixes.

🔍 AI Network Insights Apart from issue detection, AIML in Cisco DNA Center also offers network insights. These insights are generated based on deviations in radio throughput and client count for access points. Monitoring and analyzing these deviations can help engineers identify traffic patterns and make informed decisions regarding access point placement, capacity management, and network optimization.

📈 Deviations in Radio Throughput This section will explore how AIML detects deviations in radio throughput for access points. Understanding fluctuations in radio performance is vital for maintaining optimal network connectivity and performance. By analyzing radio throughput data, engineers can identify areas with increasing or decreasing traffic and take appropriate actions to ensure efficient network operations.

👥 Client Count Deviations Client count deviations act as indicators of access point capacity and usage. By monitoring client count fluctuations, engineers can proactively manage network capacity and prevent potential connectivity issues. This section will discuss how AIML detects and presents client count deviations, empowering engineers to make data-driven decisions regarding access point expansion and relocation.

🌐 Access Point Summary and Analysis In this section, we will explore detailed insights into specific access points experiencing client count deviations. Through access point summaries, engineers can gain valuable information about the maximum deviation observed in the past four weeks. By analyzing this data, engineers can effectively plan access point placements and optimize network coverage.

🔥 Network Heat Map and Client Count To visualize network data and client count deviations, Cisco DNA Center provides a network heat map. This section will delve into the network heat map, which presents a visual representation of client count deviations on a per-day basis. By analyzing the heat map, engineers can identify consistent trends in client counts and make data-driven decisions regarding network optimization.

✅ Conclusion In conclusion, the AIML feature in Cisco DNA Center revolutionizes network management and troubleshooting. By leveraging advanced analytics and insights, engineers can proactively identify and resolve issues, resulting in increased productivity, improved user experience, and significant time and cost savings. Cisco DNA Center's AIML capabilities empower engineers to optimize network performance, adapt to changing requirements, and enhance overall network efficiency and reliability.

Highlights:

• AIML (Artificial Intelligence and Machine Learning) in Cisco DNA Center enhances network management and troubleshooting.
• Setting up AIML involves configuring the AI Network Analytics settings in Cisco DNA Center.
• Enabling AI and Network Analytics allows for data analysis and correlation in the cloud.
• AIML in Cisco DNA Center sets baselines for KPIs, eliminating false alarms and focusing on real issues.
• The Issues Dashboard provides an overview of network issues and AI-driven insights.
• Drill down into specific issues to analyze location, impacted clients, baselines, and real onboarding failures.
• AIML helps in root cause analysis, identifying probable network causes and suggesting further investigation.
• Failed distribution and client count deviations provide valuable data for issue resolution.
• AI Network Insights offer in-depth analysis of radio throughput and client count deviations.
• Access point summaries, network heat maps, and client count analysis aid in network optimization.