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©2019 Mist Systems., A Juniper Company 1
The Network for the Next DecadeAI Driven. Cloud Enabled. Agile.
Matt Fowler – Consulting Engineer
©2019 Mist Systems., A Juniper Company 2
The world has changed, but wireless networks have not
Wi-Fi Gen 1 Wi-Fi Gen 2No modern
wireless architecturesThe new
wireless network
2007 20162003
©2019 Mist Systems., A Juniper Company 3
Challenges with traditional WLANs:
⚠️ “Up” is not the same as “good”
⚠️ Difficult to troubleshoot, configure
⚠️ Expensive to scale
⚠️ Limited insight
Goal: End Mediocre Wi-Fi
©2019 Mist Systems., A Juniper Company 4
The Mist Learning WLAN
Marvis Virtual
Network Assistant
Wi-Fi
Assurance
Asset
Tracking
User
Engagement
Mist Cloud Services
Infrastructure
AP21
AP41
AP61 (outdoor)
BT11 (BLE)
Domain Expertise
Data Science
Data
Marvis
AI Foundation
Message Bus
Microservices
AP43 (Wi-Fi 6)
43
Mist Edge
©2019 Mist Systems., A Juniper Company 5
Marvis - A Journey to an AI-Driven Enterprise
Distributed Software Architecture
Wired/Wireless
Data AI Primitives
Event Timeline
Data Science
Toolbox Anomaly
Detection 3.0
Virtual AssistantSelf Driving
Action Framework
It all starts with the right data
©2019 Mist Systems., A Juniper Company 6
Examples
Artificial Intelligence
Machine Learning
Deep Learning
Data Science
Nest
Tesla
Moneyball
©2019 Mist Systems., A Juniper Company 7
Examples at Mist
Artificial Intelligence
Machine Learning
Deep Learning
Data Science
Marvis - Virtual
Assistant
Unsupervised ML for Location.
Supervised ML Throughput SLE
NLP, AI Driven RRM
Lift / Mutual Information /
Bayesian Inference
©2019 Mist Systems., A Juniper Company 8
AIOPs requires a well stocked Data Science Toolbox
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
DEEP LEARNING
1950’s 1980’s 2010’s1960’s 1970’s 1990’s 2000’s
Deep learning
CNN, RNN, GAN
( Time Series Anomaly, NLP,
GeoSpatial Analysis )Reinforcement
Learning
( RRM )
Decision-tree learning
XGBoost
(Throughput)
Unsupervised
Learning
( Location )
Mutual Information
( Feature Discovery)
Domain Expertise
Classification
(Service Level Metrics)
(Event Timeline)
Online ARIMA
( Time Series Anomaly )
Temporal Correlation
( Root Cause)
©2019 Mist Systems., A Juniper Company 99
Service Level Expectations and Root Cause Analysis (demo)
©2019 Mist Systems., A Juniper Company 10
Identify root cause of issues
Are SLEs
being met?
When did problem occur?
When did config. and system changes take place?
©2019 Mist Systems., A Juniper Company 11
Mutual Information
Bayesian Inference
©2019 Mist Systems., A Juniper Company 12
©2019 Mist Systems., A Juniper Company 13
Rewind to when an event occurred to see what happened
Automatically detect and capture packetsTrack event logs
©2019 Mist Systems., A Juniper Company 14
Baselining – What is normal?
• If you (or the system) understands what is normal, then the system can detect and most
importantly notify when an anomaly occurs
• Important look over time and at several factors
Failure percentage is rising
Static threshold met, trigger alert
©2019 Mist Systems., A Juniper Company 15
Solution: Anomaly Detection 3.0 Using Deep Learning
Moving Average
50%
ARIMA
80% LSTM Recurrent Neural
Networks (RNN)
>95%
Value
Time
Anomaly
©2019 Mist Systems., A Juniper Company 16
Anomaly Detection Without False Positives (demo)
©2019 Mist Systems., A Juniper Company 17
Reinforcement Learning - RRM
Agent
Environment
ActionReward
State
Reinforcement Learning Action
• Channel
• Power
• Channel bandwidth
State
• SLE capacity utilisation
• SLE coverage anomaly
• SLE AP uptime
• Radar events
Reward
• User Experience (SLE Metrics)
-Client data rate symmetry
-Roaming
What is New?
Long term vs. short term reward
Optimise user experience vs. just interference
Global and Local optimisation
©2019 Mist Systems., A Juniper Company 18
Virtual Assistant with a conversational interface
18
©2019 Mist Systems., A Juniper Company 19
Marvis (demo)
©2019 Mist Systems., A Juniper Company 20
Marvis has Added Wired Data Elements (demo)
©2019 Mist Systems., A Juniper Company 21
New Entity Event and Action Framework
Marvis
ActionFramework
Root Cause
Action
EVENT FRAMEWORK
User Experience
Service Level
Expectation (SLE)
Framework
Network element
AP health
Switch health
Marvis Anomaly
Detection 3.0
Configuration
Management
Event
Correlation
Timeline
Framework
External Sources
Self-
driving
IT Admin
©2019 Mist Systems., A Juniper Company 22
Marvis Actions…Turning Root Cause Into Human Actions
©2019 Mist Systems., A Juniper Company 23
The old IT model is dead… long live AI for IT!
AI-Driven Enterprise
Engineered for Connectivity
Old ITSM
AI-driven
automation
AI-driven
insight
Abstracted
control with
programmability
Proactive, Predictive, Self-healing
Thank you!!
©2019 Mist Systems., A Juniper Company 24
Please take a short survey
©2019 Mist Systems., A Juniper Company 25©2017 Mist Systems. Proprietary & Confidential
Thank you.
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