©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

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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

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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

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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)

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Service Level Expectations and Root Cause Analysis (demo)

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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

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©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

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Anomaly Detection Without False Positives (demo)

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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

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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

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©2019 Mist Systems., A Juniper Company 25©2017 Mist Systems. Proprietary & Confidential

Thank you.