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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' |
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. |
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| 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. |
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| 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). |
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| 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) |
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| 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 |
No |
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| 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 |
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| 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 |
Manual |
- 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 |
No. |
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Gartner Magic Quadrant for Enterprise Wired and Wireless LAN Infrastructure, Mike Toussaint, Christian Canales, Tim Zimmerman, December 21, 2022.
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