Enhance Network Performance with Juniper Mist AI

Table of Contents

1. Introduction
2. About Mist AI
   • What is Mist AI?
   • Benefits of Mist AI
3. Service Level Expectations
   • Overview of SLEs
   • Wireless SLEs
     • Successful Connects
     • Time to Connect
     • Throughput
     • Capacity and Coverage
     • Roaming
   • Wired SLEs
     • Layer 2 LAN
     • Switch Performance
     • Capacity and Throughput
   • SD-WAN SLEs
     • Application Experience
     • Link Health
     • Gateway Performance
4. Deep Dive into Mist AI Features
   • Client-Level Visibility
   • Artificial Intelligence and Telemetry
   • Marvis: The Virtual Network Assistant
5. Live Demonstration
   • Wireless SLEs Monitoring
   • Analyzing Successful Connects
   • Drilldown on User Experience
   • Investigating Throughput and RF Capacity
   • Location-Based Insights
6. Conclusion
7. FAQs

Introduction

In this article, we will explore Juniper Network's Mist AI, a cloud-based solution powered by artificial intelligence (AI). We will dive into the concept of Service Level Expectations (SLEs) offered by Mist AI and understand how it can enhance the performance and user experience of your network. Furthermore, we will take a closer look at the various features and capabilities of Mist AI, including its client-level visibility, artificial intelligence-driven operations, and the virtual network assistant called Marvis. To provide a better understanding, we will also walk through a live demonstration of Mist AI's SLE monitoring and analysis. So, let's get started!

About Mist AI

What is Mist AI?

Mist AI is a cloud-based microservice solution that utilizes artificial intelligence at its Core. It offers a range of capabilities, including Wi-Fi assurance, wired assurance, and SD-WAN assurance. With its cloud-based architecture and microservices approach, Mist AI ensures 24/7 availability with zero downtime. This allows the solution to keep up with network changes and drive weekly updates seamlessly.

Benefits of Mist AI

Mist AI provides several benefits to network administrators and organizations. Firstly, it enables client-to-cloud visibility, allowing administrators to monitor the experience of users connecting to the wireless and wired networks. Additionally, Mist AI leverages AI and rich telemetry to proactively monitor the network and provide actionable insights. This empowers administrators to identify potential issues and optimize network performance, resulting in improved user experience.

Service Level Expectations

Overview of SLEs

Service Level Expectations (SLEs) play a crucial role in ensuring the performance and quality of a network. Within Mist AI, SLEs define the expectations and metrics that determine the level of service for specific functionalities. There are different SLEs for wireless, wired, and SD-WAN networks, each focusing on specific aspects of network performance.

Wireless SLEs

Successful Connects

One of the primary wireless SLEs is measuring the success rate of client connections. This metric determines whether users can connect to the network successfully or not. In case of unsuccessful connections, Mist AI provides insights into the underlying issues, such as DHCP authorization or association failures. By analyzing these metrics, administrators can identify the root causes of connection failures and take appropriate measures to improve successful connection rates.

Time to Connect

The time taken by users to connect to the network is another important SLE. Mist AI monitors the duration it takes for successful connections to occur, helping administrators identify any delays in the connection process. By analyzing the time to connect, network administrators can take steps to reduce connection latency and provide a seamless user experience.

Throughput

Throughput, or the speed at which data can be transmitted, is a critical factor in assessing network performance. Mist AI measures throughput for both wireless and wired connections to ensure optimal data transfer rates. By monitoring throughput, administrators can identify any bottlenecks or limitations that may be affecting network performance and take corrective actions.

Capacity and Coverage

Mist AI's SLEs also include metrics related to network capacity and coverage. Administrators can assess the network's capacity to handle Wi-Fi interference, non-Wi-Fi interference, and excessive client loads. Additionally, coverage metrics help analyze the extent of network reach and detect any issues affecting signal strength and coverage areas. By monitoring these metrics, administrators can optimize network capacity and coverage for better performance.

Roaming

Roaming is an essential aspect of wireless networks, especially in environments with multiple access points. Mist AI monitors roaming behavior to ensure smooth transitions between access points. This includes analyzing the speed of roaming, the impact on user experience, and identifying any issues related to sticky or slow roaming. By improving roaming performance, Mist AI helps deliver a consistent and seamless wireless experience for users.

Wired SLEs

Wired SLEs focus on monitoring the performance and health of the wired infrastructure. This includes assessing Layer 2 LAN connectivity, switch performance, capacity, and throughput. Mist AI enables administrators to monitor individual switches, ports, and clients to pinpoint any issues affecting performance. By analyzing the SLE metrics related to wired networks, administrators can troubleshoot problems quickly and optimize network performance.

SD-WAN SLEs

Mist AI extends its SLE capabilities to SD-WAN networks, providing insights into application experience, link health, and gateway performance. By monitoring application-level metrics such as latency, jitter, and loss, administrators can ensure optimal application performance over the SD-WAN infrastructure. Additionally, Mist AI analyzes the health of SD-WAN links, detecting any issues that may impact application performance. This includes monitoring link stability, bandwidth utilization, and gateway health. By proactively monitoring and troubleshooting SD-WAN networks, Mist AI helps administrators maintain a reliable and efficient network infrastructure.

Deep Dive into Mist AI Features

Client-Level Visibility

One of the key features of Mist AI is its ability to provide client-level visibility. This means that administrators can monitor the experience of individual users connecting to the network. Mist AI captures and analyzes telemetry data from client devices, allowing administrators to track performance metrics, connectivity issues, and roaming behavior on a granular level. By having client-level visibility, administrators can identify and resolve user-specific issues promptly, resulting in an improved overall network experience.

Artificial Intelligence and Telemetry

At the heart of Mist AI is artificial intelligence (AI) and rich telemetry. Through the use of AI algorithms, Mist AI processes the vast amount of telemetry data collected from network devices and clients. By applying AI analytics, Mist AI can proactively monitor the network, detect anomalies, and predict potential issues before they impact user experience. This enables administrators to take preventive actions and optimize network performance based on data-driven insights.

Marvis: The Virtual Network Assistant

Marvis is the virtual network assistant powered by Mist AI. It leverages AI and natural language processing to provide operators with actionable recommendations and contextual information about the network. Administrators can ask Marvis questions about network performance, user experience, and troubleshooting steps. Marvis understands human queries and provides helpful suggestions, allowing administrators to quickly identify and resolve network issues. With Marvis, network administrators have an AI-powered assistant that enhances their troubleshooting capabilities and simplifies network management.

Live Demonstration

In this live demonstration, we will explore the practical usage of Mist AI's SLE monitoring and analysis capabilities. By accessing the Mist AI platform, administrators can view real-time telemetry data and analyze various SLE metrics. The demonstration will cover monitoring wireless SLEs, analyzing successful connects, investigating user-specific issues, and examining throughput and RF capacity. Additionally, we will explore location-based insights and the ability to drill down into specific clients for deeper analysis. This live demonstration showcases how Mist AI empowers administrators to gain actionable insights and optimize network performance efficiently.

Conclusion

Mist AI, with its cloud-based solution and AI-driven capabilities, revolutionizes network management and optimization. By leveraging innovative features like client-level visibility, artificial intelligence, and the virtual network assistant Marvis, Mist AI equips administrators with powerful tools to enhance network performance, troubleshoot issues, and deliver an exceptional user experience. The comprehensive SLE monitoring and analysis capabilities of Mist AI provide administrators with actionable insights and enable proactive network management. With Mist AI, organizations can achieve heightened network efficiency, minimize downtime, and ensure optimal user satisfaction.

FAQs (Frequently Asked Questions)

Q: How does Mist AI ensure 24/7 availability of network services? A: Mist AI utilizes a cloud-based microservice architecture, allowing it to operate continuously without downtime. The microservices architecture enables seamless updates and maintenance while delivering uninterrupted service availability.

Q: Can Mist AI detect and troubleshoot roaming issues in wireless networks? A: Yes, Mist AI can monitor and analyze roaming behavior in wireless networks. It identifies any sticky or slow roaming patterns, enabling administrators to optimize roaming performance and provide users with a seamless wireless experience.

Q: How does Marvis, the virtual network assistant, enhance troubleshooting capabilities? A: Marvis employs artificial intelligence and natural language processing to understand human queries and provide actionable recommendations. It can assist administrators in troubleshooting network issues, optimizing performance, and answering questions about network operations.

Q: Can Mist AI monitor and optimize the performance of SD-WAN networks? A: Yes, Mist AI extends its capabilities to SD-WAN networks. It provides insights into application experience, link health, and gateway performance, enabling administrators to ensure optimal application performance and troubleshoot SD-WAN-related issues.

Q: Does Mist AI support wired network monitoring? A: Yes, Mist AI offers SLE monitoring and analysis for wired networks. It allows administrators to assess Layer 2 LAN connectivity, switch performance, capacity, and throughput. This enables troubleshooting and optimization of wired network performance.

Q: How can Mist AI help improve wireless network throughput? A: Mist AI monitors throughput metrics and identifies factors affecting wireless network performance. By analyzing metrics such as limited RF capacity or high Wi-Fi interference, administrators can take necessary steps to optimize throughput and improve network performance.

Q: Can Mist AI capture and analyze network packets for troubleshooting purposes? A: Yes, Mist AI can capture network packets for analysis. Administrators can download packet captures and use tools like Wireshark to decode and analyze the packets. This capability aids in troubleshooting network issues and obtaining detailed information for further investigation.

Q: What is meant by client-level visibility in Mist AI? A: Client-level visibility refers to Mist AI's ability to monitor and analyze the performance and behavior of individual clients connected to the network. It allows administrators to track metrics specific to each client, such as connectivity issues, roaming behavior, and throughput, enabling prompt identification and resolution of client-specific network issues.