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AI-Driven RF Optimization (RRM)
AI-Driven RF Optimization (RRM)

Based on reinforcement learning:

- Optimizes channel/power with AI-based reinforcement learning

- AI continuously maximizes User experience (SLE) and minimizes interference in real-time

- Adapts dynamically on an ongoing basis while network under load learning from client experience

- Learns and deprioritized triggered DFS channels to boost network uptime

- Coverage SLE is an ongoing 'Site Survey'

Watch the video

Basic RRM

- will monitor DFS failure patterns

- AP's remember their settings through power failures

- Won't make changes in 'busy hours'

ARM - Basic pattern recognition for comparing and optimizing low-level RF settings only across managed sites:

- Not a true AI solution: doesn’t leverage reinforcement learning to improve over time

- Doesn’t adjust RF to maximize user experience

- Analyzes periodical and static data for daily but not ongoing dynamic updates

- Requires Controller and Mobility Master for AirMatch RF optimization

- Requires data collector appliances and NetInsight server

15-year old algorithm

- Based on how APs hear each other

- Optimizes channel/power based solely on AP interference graph

- RRM is performed on a static, periodic basis when the load is low

Basic RRM. No AI/ML, requires several days of tuning.

Virtual Network Assistant
Virtual Network Assistant

- Continuous learning through Supervised Machine Learning

- Performs root cause analysis for most detected network issues

- Supports wireless, wired and WAN at a site level

- Troubleshoot issues instead of pulling logs

- Can be accessed through WebUI or API

- Built on 6 years of continuous learning and rich data science toolbox

- Dashboard

- No virtual assistant

- Dashboard

- No virtual assistant

- Dashboard

- Chatbot rumored but not productized or available to customers in beta

- Dashboard and network assistant only on cloud.

- Chatbot called Co-Pilot, very limited, No AI. Allows NLP version 1.0. No query.

- In beta the last 2 years.

Anomaly Detection
Anomaly Detection

- Proactively identifies anomalies and uses data science tools to determine root cause

- Leverages both Wired and Wireless SLEs for anomaly detection

- 3rd generation algorithm with ARIMA boosts efficacy

- Anomaly detection performed across Wi-Fi, LAN, WAN, Security Domains

- ChatGPT integrated

- 1st generation anomaly detection algorithm

- Will go through a weeks worth of data to find some basic anomalies

- Limited set of anomaly detection (DHCP, AAA, RF utilization)

- Requires NetInsight Data Collector appliance

- 1st generation anomaly detection algorithm

- Limited anomalies detected (DHCP, AAA, Association, Throughput)

- Requires Cisco DNA appliances (3+)

Client 360 tracks basic anomalies.

Pilot and CoPilot supported.

1st generation anomaly detection algorithm.

Limited anomalies detected (Latency, Throughput, airtime).

Self-driving capabilities
Self-driving capabilities

- Marvis Actions Framework for self-driving or driverassist mode (e.g. RF optimization, proactive RMA, unhealthy APs, missing VLANs, bad cables, switch config errors, etc.)

- Validated by Mist

- Customer Service to solve or help train system

- Closed loop feedback providing actionable intel to administrators “bottoms up”

- Dashboards

- No self-driving capabilities

- Will offer “suggestions”

- Top down

- digging

- Dashboards

- Lacks self-driving, only having “driver-assist” capabilities where it provides recommendations to IT

- Very basic driver-assist capabilities (identifies channel utilization issues and poor DHCP/AAA performance for IT to manually investigate)

- Top down digging for next generation log files

- Dashboards

- No self-driving capabilities

- Top down Need to ‘nominate’ troubled user to begin any active monitoring

- Dashboards generated by basic math.

- Lacks self-driving, only having “drive-assist” capabilities where it provides recommendations to IT

- Limited self-driving capabilities (Latency, Throughput, Airtime)

AI-driven location
AI-driven location

Creation of probability surfaces in the cloud and ongoing unsupervised machine learning to constantly update the model.

- Triangulation dependent on accurate map placement

- Errors introduced by variance in BLE clients

- Triangulation dependent on accurate map placement

- Errors introduced by variance in BLE clients

- Meridian sidelined

- Requires CMX appliance onsite (even for DNA Spaces)

- Requires 3rd party BLE integration

- Triangulation dependent on accurate map placement. Errors introduced by variance in BLE clients


AI-driven support
AI-driven support

- Mist Support utilizes Marvis to troubleshoot issues

- Marvis efficacy is continuously evaluated and when support issues arise where data or answer is not available, we train Marvis or add the missing data collection

- When Marvis detects a hardware failure in an AP, it can perform an automatic RMA minimizing the ‘burden of proof’ on IT teams rather than escalating issues with a vendor

- As AP deployments have grown at a rapid pace, support tickets have remained flat due to the use of Mist AI

- Dashboards

- No use of AI to automate support or support operations

- Dashboards

- Lacks automated support capabilities driven by AI

- Aruba AI Assist is a basic manual button to gather logs to email to Aruba Support for manual analysis

- Dashboards

- No use of AI to automate support or support operations

- Dashboards.

- Lacks automated support capabilities driven by AI

Dynamic Packet Capture
Dynamic Packet Capture

- Proactively captures packets when an error event occurs in real-time

- Eliminates need to reproduce issues as every failure has a PCAP starting before the failure and playing though it

- No more sending out tech folks with sniffers *after* the problem has happened

Watch the video


- Primarily manual - limited auto capture on authentication failure events

- Requires an additional, separate cloud dashboard for troubleshooting and analysis (Cape Networks)

- Requires overlay network of Aruba UXI wireless sensor hardware

Intelligent Packet Capture

- first a client needs to file a ticket

- then the client will be tagged to collect data going forward

- not at all automatic


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Gartner Magic Quadrant for Enterprise Wired and Wireless LAN Infrastructure, Mike Toussaint, Christian Canales, Tim Zimmerman, December 21, 2022.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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