Cisco Connect Toronto 2018 DNA assurance

Tim Szigeti
Principal Engineer—Technical Marketing
October 18, 2018
The Shortest Path to Network Innocence
DNA Assurance

Agenda
© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public
• Business Requirements
• Context
• Learning
• User Requirements
• Technology Requirements
• DNA Assurance
• Client Assurance
• Network Assurance
• Application Assurance
• Machine Learning
• Summary
2

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
Network Quality is a Complex, End-to-End Problem
APs
Local WLCs
Network services DCOffice site
ISE
Mobile clients
CUCM
Client firmware
AP coverage
WAN Uplink usage End-User services
RF Noise/Interf.
Cisco Prime™
Configuration
AuthenticationWLC Capacity
WAN
Client density
DHCP
Addressing
WAN QoS, Routing, ...
There are
100+ points of
failure between
user and app
What is the problem?
Where is the problem?
How can I fix the problem fast?
3

. 1 McKinsey Study of NetworkOperationsfor Cisco – 2016
IT Challenge: 43% of IT Time spent in Troubleshooting
Network operators
spend more time collecting
data than analyzing
while troubleshooting
Impossible for IT to
troubleshoot if they cannot
replicate the issue or see it
real time
Most network quality issues
take hours to either resolve
or to prove the network
innocent
4x Replication
challenge
Slow resolution
4

Business Objective of Analytics and Assurance
https://www.cisco.com/c/en/us/solutions/enterprise-networks/dna-analytics-assurance.html
5

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
• Intent
• Context
• Learning
• DNA Assurance
• Summary
Agenda
S E C U R I T Y
CONTEXT
L E A R N ING
I N T E NT

The Challenge of Context:
Transforming Data into Actionable Insights
Data
Insight
Information
Action
Create value at the right
time
Extract meaningful insights
from data
Volume
Data size
• TB per day
• Streaming telemetry,
NetFlow, Syslog, SNMP,
logs
Velocity
Data speed
• Firehose
• Streaming, low-latency
push/pull
Variety
Data forms
• Structured, unstructured
• Switch, router, AP,
IoT sensor, firewall,
load balancer, DHCP, DNS
Veracity
Data trustworthiness
• Quality, validity
• Internal, partner, public
Analytics
7

Transforming Data into Actionable Insights
Example
Data
Insight
Information
Action
WebExhas 63 ms of jitter
WebExApplication Health Score is 70%
WebExis not being marked on the WLAN
Enable Fastlane for WebEx
8

The Value of Context
9

The Value of Context
10
Connecting the Dots

DDI
NetFlow
AVC
Topology
Location
Device
Analytics
Engine
User: George BakerGroup: Marketing
Contextual Correlation Example
ISEISE
M AC: B8:8D:12:36:15:22
M AC: 60:F4:45:78:96:9F

DDI
NetFlow
AVC
Topology
Location
Device
Analytics
Engine
ISEISE
DDI
Source IP: 1.1.1.2
M AC: B8:8D:12:36:15:22
M AC: 60:F4:45:78:96:9F

DDI
NetFlow
AVC
Topology
Location
Device
Analytics
Engine
ISEISE
DDI
D est IP: 2.2.2.2
D est Port: 80 ?
D est IP: 3.2.2.2
D est Port: 80 ?
NetFlow
Source IP: 1.1.1.2
M AC: B8:8D:12:36:15:22
M AC: 60:F4:45:78:96:9F

DDI
Netflow
AVC
Topology
Location
Device
Analytics
Engine
ISEISE
DDI
D est IP: 2.2.2.2
D est Port: 80
NetFlow
D est IP: 3.2.2.2
D est Port: 80
AVC ?
?Source IP: 1.1.1.2
M AC: B8:8D:12:36:15:22
M AC: 60:F4:45:78:96:9F

TopologyTopology
Location
Device
Analytics
Engine
D est IP: 2.2.2.2
D est Port: 80
D est IP: 3.2.2.2
D est Port: 80
Topology
DDI
Netflow
AVC
Location
Device
ISEISE
DDI
NetFlow
AVC
Source IP: 1.1.1.2
M AC: B8:8D:12:36:15:22
M AC: 60:F4:45:78:96:9F

M AC: 60:F4:45:78:96:9FLocation
Device
Analytics
Engine
D est IP: 2.2.2.2
D est Port: 80
D est IP: 3.2.2.2
D est Port: 80
Location
Building 24 1st Floor
TopologyTopologyTopology
DDI
Netflow
AVC
ISEISE
DDI
NetFlow
AVC
Source IP: 1.1.1.2
M AC: B8:8D:12:36:15:22

M AC: 60:F4:45:78:96:9F
Device
Analytics
Engine
D est IP: 2.2.2.2
D est Port: 80
D est IP: 3.2.2.2
D est Port: 80
Building 24 1st Floor
Device
No Layer 2 QoS
marking for Webex
LocationLocation
TopologyTopologyTopology
DDI
Netflow
AVC
ISEISE
DDI
NetFlow
AVC
Source IP: 1.1.1.2
M AC: B8:8D:12:36:15:22

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
• Context
• Learning
• DNA Assurance
• Summary
Agenda
S E C U R I T Y
CONTEXT
L E A R N ING
I N T E NT

What is Machine Learning?
• Machine learning is an application of artificial intelligence (AI) that provides systems the ability to
automatically learn and improve from experience without being explicitly programmed to do so
• The process of learning begins with observations of data, and looking for patternswithin the data so as
to make increasingly better correlations, inferences and predictions
• The primary aim is to allow these systems to learn automatically without human intervention or
assistance and adjust actions accordingly

Reasons for Data Patterns
• Coincidence
• Correlation
• Causation
Statistics 101:
Correlationdoes not necessary mean Causation

Machine Learning Algorithms
build their models using
hundreds of inputs
APs
WAN
Local WLCs
Network Services DCOffice Site
ISE
DHCP
Mobile Clients
CUCM
RF & EDCA
behavioral
metrics,..
Queuing, Dropping, WRED
behavioral metrics…
Device type, OS release,
behavioral metrics, ...
WAN & core
network metrics ..
Application metrics, user
feedback, failure rate, ...
... and more

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
22

Design Thinking (DT) Case Study: GE MRI
• Best technology of its time
• most accurate
• most comprehensive
• safest
• Terrible initial user-
experience, especially with
children
• 80% ofpediatric patients had to
be sedated when undergoing
MRI scans
• Doctor quote: “The worst part of
my day is when I have to give a
child an MRI.”

DT Case Study: GE MRI
• Getting intimately familiar with end-users (specifically
pediatric patients) led to the insight:
• These sick children miss out on a lot of adventures
• Revamped user-experience (no change to tech)  results:
• <5% of pediatric patients had to be sedated when undergoing MRI
scans
• Doctor quote: “The BEST part of my day is when I have to give a
child an MRI and I get to dress up like a pirate!”
2424

Design Thinking “Sweetspot”
25

Design Thinking Core Phases
26

28

29

32

38

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
39

DNA Assurance
4040

Collect relevant metrics
Self-Healing Network Architectural Requirements
#1: Instrumentation
EM
App
Servers
Sensors
4343

Categorize metrics by degrees of relevance
#2: On-Device Analytics
4444

Upload critical metrics off the device to collector(s)
(optimally via model-based streaming-telemetry)
#3: Telemetry
EM
Collector
4545

Provision long-term storage, retrieval and representationof network metrics and events
#4: Scalable Storage
4646

Identify anomalies and trends
#5: Analytics Engine
4747

Analyze all data points and permutations for cognitive and predictive analytics
#6: Machine Learning
4848

Identify root cause of issues by contextually correlating data
#7: Guided Troubleshooting
EM
Analytics
Engine
4949

#8: Self-Remediation
Present actionable insights to the operator
Solicit input to remediate the root cause
Present a self-remediation option
EM
Analytics
EngineEM
Network
Controller
Do you want to take the
recommended action?
Yes No
Do you want to take the
recommended action?
Yes NoAlwaysAlways
5050

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
51

DNA Software Capabilities
Cloud Service Management
Automation Analytics
Virtualization
DNA-Ready Physical andVirtual infrastructure
Security
Cisco DNA Architecture
5252

DNA Center
A single pane-of-glass for Design, Provision, Policy and Assurance
DNA CenterAppliance
EM
NDP
NDP:
Network Data Platform
(Analytics Engine)
EM
NCP
NCP
Network Controller Platform
(Network Controller)
Automation Analytics
5353

DNA Assurance
Everything as a Sensor
150+ Actionable Insights
Client | Applications | Wireless | Switching | Routing
Network Telemetry
Contextual Data
Complex Event
Processing
Correlated Insights
Guided
Remediation
IPAM
CMX
AppD
IPSLA
SNMP
OID
Telnet
DNS
MIB
Ping
CLI
DHCP
Wireless
AAA
Syslog
Router
Netflow
Traceroute
Metadata
extraction
Complex
correlation
Steam
Processing
001110101100110
1010110010
00101101
0110100
1101101
00101101
10101100110
101011000110011
Clients Baseline
Application Network
54

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
55

Client Health Page
5656

Client Health Details
5757

Client 360
5858

Client Onboarding
Network Coverage
& Capacity
Network Device
Monitoring
Application
Performance Sensor
Association failures
Authentication failures
IP address failure
Client Exclusion
Excessive on-boarding time
Excessive authentication time
Excessive IP addressing time
AAA, DHCP reachability
Coverage hole
AP License Utilization
Client Capacity
Radio Utilization
Availability
Crash, AP Join Failure
High Availability
CPU, Memory utilization
Flapping AP, Hung Radio
Pow er supply failures
Throughput analysis
Roaming pattern analysis
Sticky client
Slow roaming
Excessive roaming
RF, Roaming pattern
Dual band clients prefer 2.4GHz
Excessive interference
Client Experience
Web: HTTP & HTTPS
Email: POP3, IMAP, Outlook
Web Access
File Transfer: FTP & TFTP
Terminal: Telnet & SSHv2
Wireless Specific Correlated Insights
Total Insights: 100+ issues in DNA-C 1.2
5959

Client 360—Issues
60

Client 360—Onboarding Details
6161

Client 360—RF Details
6262

DNA-C Assurance Apple Insights
Device Profile
Client shares these details
1. iPhone 7, iPad Pro
2. iOS 11
Support per device-
group Policies and
Analytics
1 Wi-Fi Analytics
1. BSSID
2. RSSI
3. Channel #
Insights into the clients
view of the network
2 Assurance
Error code for why did it
previously disconnected
Provide clarity into the
reliability of connectivity
3
6363

Client 360—iOS Analytics
6464

Wireless Sensors Proactively Assess
Performance
Test your network anywhere at any time
R1
Dedicated Sensor AP1800 Flexible Radio
Sensors act as
clients
Access point
 On-Boarding Tests
• 802.11 Association
• 802.11 Authentication & Key Exchange
• IP Addressing DHCP (IPv4)
 Network tests
• DNS (IPv4)
• RADIUS (IPv4)
• First Hop Router/Default gateway (IPv4)
• Intranet Host
• External Host (IPv4)
 Application tests
• Email: POP3, IMAP, Outlook Web Access (IPv4)
• File Transfer: FTP (IPv4)
• Web: HTTP & HTTPS (IPv4)
Flexible Radio Assignment Algorithm intelligently
identifies excessive radios and seamlessly converts
those into Sensor mode without client impact
6565

Run Network Tests
See What Works and Where
6666

Run Network Tests
Hover over a Test to See Results
6767

Filtering the View
Select Only the Tests That Matter to You Now
6868

Sensor Test How-To
1a. Create a New Test, Choose Location and Run Interval
6969

Sensor Test How-To
1b. Choose the SSIDs to test
7070

Sensor Test How-To
2. Select the Tests to Run
71

Sensor Test How-To
3. Select the Sensors to Use
72

Sensor Test How-To
Test Runs at Intervals
73

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
74

Network Health
75

Network Health—Site View
76

Network Health—Topology View
77

Network Health—By Device Roles
78

Network Health—Device 360 (Part 1 of 3)
79

80

81

Network Issues and Troubleshooting Example
82

Path Trace—Part 1 of 4

Path Trace—Part 2 of 4 (Device Details and Stats)

Path Trace—Part 3 of 4 (Interface Details and Stats)

Path Trace—Part 4 of 4 (QoS and ACL Stats)

PathTrace—How Does it Work?

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
88

Client 360—Application Experience
89

Client 360—Application Experience (cont)
90

Calculating Jitter and Loss for RTP Apps
RTP Header Format
https://tools.ietf.org/html/rfc3550#section-5.1
Gaps in
subsequent RTP
packet sequence
numbers identifies
lost packets
Jitter is calculated
by comparing the
timestamps of RTP
packets with
subsequent
sequence
numbers
91

Calculating Latency and Loss for TCP Apps
Client Server
X
SYN
SYN-ACK
ACK 6
Request 1
ACK
DATA 4
DATA 3
DATA 5
DATA 3
Request 1 (Cont)
X
DATA 4
DATA 1
Request 2
DATA 6
DATA 2
ACK 3
ACK
SND
CND
Request
Response
Retransmission
RT
Response Time
(RT)
t(First response pkt)
– t(Last request pkt)
Network Delay
(ND)
ND = ( CND + SND ) /2
Application
Delay (AD)
AD = RT – SND
ART
SND = Server Network Delay
CND = Client Network Delay
Packet Loss Loss ≈ Retransmissions
(95%+ accuracy)
Application Response Time (ART)
92

Application
Integration
Data Center
Application Servers
Remote
User
Medium Branch
Small Branch
Large Branch
Campus
1.2.8 Release App Integration: Other Integration Opportunities:
• S4B • WebEx
• Spark
• CUCM
• MS O365
SaaS Apps
EM
DNAC
(NCP+ NDP)
93

Why Integrate DNAC with Collab Apps?
• the network doesn’t see everything
• network measurements are mid-stream to the flows
• loss, latency and jitter may all be induced downstream from where network-measurements are
made
App performance
measurements
made here
Loss, latency and/or jitter
induced here is not
measured/reported

Why Integrate DNAC with Cisco Collab Apps?
• the network can measure QoS, but not QoE
• Video codecs can react to network congestion by lowering frame-rates and/or resolution levels
• Packet flows may look perfect from the network QoS perspective, even during periods when the user-
experience may be significantly degraded
QoS
QoE

Client 360—Application Experience (1.2.8 S4B)
96

97

98
S4B MOS Scores

S4B Audio
Media Quality
Metrics
99

S4B Video
Media Quality
Metrics
100

S4B Application
Sharing Quality
Metrics
101

Intelligent Capture: Real-time Client and App
Insights to enhance DNA Wireless Assurance
• Enhance Sticky client
issue analysis with
Real-time location
• Identify coverage holes
with pinpoint accuracy
(<3 mtr. accuracy)
• 24x7 monitoring of Wi-
Fi and non-Wi-Fi
interference using
Intelligent Capture
radio
• 24x7 wIPS forensics to
prevent over the air
attacks using Intelligent
Capture radio
• Real-time client RF view
• In-service packet
captures using
Intelligent Capture
analytics
• Monitor Client
Onboarding real-time
• Real-time App
performance insights
Client and App
Real-time Forensics
Hyperlocation – Client
Pin-pointing
Total Secure
Coverage Monitoring
• Onboarding Tests
• Network Services Tests
• App Connectivity Tests
• App Experience Tests
Active Sensor
Testing
102

1 On-Demand RF Scanner
Key Issues addressed
Poor RF Zones
RF design optimization
Sticky client analysis with real-time location
Key Industries Healthcare, Distribution, Logistics, Retail and Higher Ed.
VoIP performance and audio issues
Situation
• A large logistics company uses Wi-Fi operated Auto-guided vehicles (AGV)
in its logistics warehouses. Due to high ceilings and large moving metal
structures, these warehouse have dynamic RF scenarios
• Due to little human involvement sometimes these machines don’t take
optimal paths due to temporal coverage hole
Solution
• IT staff uses the On-Demand RF scanner tool to validate AGV’s RF
coverage during the live operation and detect coverage holes in a highly
dynamic RF environment
• This helps IT staff immensely in RF design, planning and optimization
Live RF Network || Pause 11:50:32 Record
Session
Red Spot: Coverage Hole
KPI List Graph*
SNR
RSSI
MCS
Throughput
Packet retry
Green Spot: Voice-quality Throughput
Orange Spot: Browsing-quality Throughput
Red Spot: Coverage Hole
Troubleshooting Use Cases using Intelligent Capture

2 Automated Packet Captures
On-boarding and roaming failures
VOIP Performance issues
Sticky client analysis with real-time
location
Key Industries All segments
Situation
• A Financial services giant decided to adopt to BYOD to encourage people
to use its Virtual Desktop Infrastructure for a certain critical applications. In
the first week, John discovered longer Onboarding time from mobile
device compare to laptop and received complaints from several end-users
Solution
• IT staff turned on the Auto PCAP to capture onboarding and roaming
failure anomalies for the BYOD device at a specific site. This allowed IT
staff to capture de-authentication packets that are typically observed when
the client has driver issues
• IT staff also had access to detailed PCAP analysis that enabled them to
identify the root cause behind onboarding and roaming failures
Visual Packet Trace Analyzer (<5 sec)
Real-time Anomalies with Auto PCAPs (<5 sec)

Real-time Client Location Tracking (<5 sec)
3 VIP Service Assurance
Visual Packet Trace Analyzer (<5 sec)
On-boarding and roaming failures w/ PCAP
Remote Wireless troubleshooting through
Full PCAP
Sticky client analysis with real-time location
Key Industries Healthcare, Distribution, Logistics, Retail, Higher Ed, MSPs
Service Level Assurance for critical
apps/users
Situation
• A large healthcare deployed 5000 vital sign monitor for every in-patient.
These vital sign allow doctors to monitor critical parameters on a real-time
basis
• Wi-Fi connection of vital sign monitor starts to get disrupted on an
intermitted basis during the middle of the day and if not addressed it can
be life threateningSolution
• IT staff uses Live Troubleshooting tool to perform detailed forensics on
both the client state and the location
• Using packet trace analyzer, the IT staff is able to visualize frequent
roaming and re-authentication failures from the device along with lower
RSSI than anticipated in particular location

Intelligent Capture—Wireless OnBoarding Analysis
106

Intelligent Capture—Application Analysis
107

Intelligent Capture—Application Analysis (cont)
108

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
109

Kairos
Cloud
Cloud Based
1 click deployment
`
Strong Anonymization
All Data sent to the cloud is anonymize
and fed in the ML algorithms to
improve experience of other
customers
Support of Wireless/Wired/IoT
Extensible to all networking gears
Anonymous
Anonymous
Anonymous
Anonymous
On-going Cross Learning
Building of behavioral models using
anonymized data set covering a broad
range of deployments models
Customer B
Customer A
Customer D
Customer C
Cognitive & Predictive Analytics with Machine Learning
Collect Network Data in the cloud, Pipeline of AI
(Machine Learning) Algorithms to address uses cases
Cognitive analytics & Predictive analytics sis, long term
analysis models, optional close loop control
(e.g. network to end device)
DNA Analytics – Kairos Architecture

Cisco PaaS
NDP platformDNA Controller
DNA Center Assurance UI
Machine Learning
Stack
Graphical Models
Deep Learning
Time Series Models
NLP/NLG
Public Cloud
Orchestrator
Trained Models
Multi-Customer
Database
Prediction Pipelines
APIs
Batch Pipelines
Training Data
Models
ETL Pipelines
Collectors
Public Broker Feature Constructors
DNA Analytics – Kairos Cloud Architecture
Network ServicesDC
WA
N
Office Site
DH
CP
CM
X
Customer Network
Network Control Points
Metrics, Events,
Config, Notifications
Protocols & APIs (SNMP, JSON, NetFlow, pxGrid, CLI, ...)
Data Collection from various sources (SNMP, JSON-based
protocol, Logs, CLI, …) – Agnostic to data source & platform
Anonymization of sensitive
and/or personal data (if any)
Batch pipelines that run at regular
time intervals to continuously train
models on multi-customer data.Extraction of data from various sources,
Computation of use case specific variables and
Conversion to a unique data model (KID format)

Cisco PaaS
Machine Learning
Stack
Graphical Models
Deep Learning
Time Series Models
NLP/NLG
Public Cloud
Orchestrator
Trained Models
Multi-Customer
Database
APIs
Batch Pipelines
Training Data
Models
ETL Pipelines
Collectors
DNA Analytics – Kairos Cloud Architecture
Network ServicesDC
WA
N
Office Site
DH
CP
CM
X
Customer Network
Metrics, Events,
On-premise Kairos UI,
fully integrated in DNA
Center, and serving
data computed in the
cloud locally, de-
anonymization.
Prediction pipelines that apply
ML models trained using multi-
customer batch data on
single-customer live data to
produce use case predictions.
On-premise Orchestrator
responsible for authenticating and
relaying control instructions from the
cloud (e.g. Fusion)

Cisco PaaS
Machine Learning
Stack
Graphical Models
Deep Learning
Time Series Models
NLP/NLG
Public Cloud
Orchestrator
Trained Models
Multi-Customer
Database
APIs
Batch Pipelines
Training Data
Models
ETL Pipelines
Collectors
DNA Analytics – Kairos On-Prem Architecture
Dynamic Parameter Tuning (DPT)
Network ServicesDC
WA
N
Office Site
DH
CP
CM
X
Customer Network
Metrics, Events,
• AD 100% on premise but
sophisticatedmodels computed in
the cloud and pushed on-premise
• No data sent to the cloud

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Why do we need Machine Learning?
• Anomaly detection
• Dynamic network performanceat different times and on different network conditions
• Different expected performanceon different SSIDs and/or locations for the same
customer
• Different expected performancefor different customers
• Static thresholds (even if configurable) would likely raise many false positives or miss
relevant events
• Root cause analysis
• Automatic selection of relevant KPIs explaining an issue
• Cross-correlation across multipledevices
• Long-term trending
• Automatically identifying trends and behavior changes on network entities/locations
114

Radio Throughput – Bad RF conditions
Category Real-time Anomaly Detection
Context University
Findings Throughput drops when interference
increases as well as 100% of clients
have low RSSI and SNR
Root
Cause
Coverage issue.
Actions Review the RF design to provide
better coverage in this área.

Onboarding Time rate due to DHCP
Context University / local user SSID (Open)
Findings Onboarding time spiking around 7 AM, in
relation with some slow DHCP time.
Note: Issue raised on onboarding time taking around
3 seconds (because the model predicted less than
2 seconds to be the normal/expected onboarding
time).
Root Cause The WLC serves dorms areas and the peak is
happening when people wake up in the
morning (higher peak load), but then both the
client count and issue ends as soon as people
move out of the dorms.
Actions Verification of DHCP performance under peak
times.

Onboarding failure – AAA backend maintenance
Context University / eduroam
Findings Spike of AAA Auth failures and
increased AAA time.
Root
Cause
The customer is confident that this is
due to an eduroam night maintenace
window.
Actions None, as this issue was expected

Smart Dashboards – Channel change count
Category Smart Dashboards
Context University
Findings Two 5 GHz radios that usually had
less than 20 channel changes per
week, suddenly have >50
Actions The following week those APs went
back to their usual behavior.

Agenda
• Intent
• Context
• Learning
• DNA Assurance
• Summary
119

DNA Assurance—Summary
• Most networking departments today are bogged down by operations spending
the majority of their time manually configuring and troubleshooting their
networks
• Enabling an intent-based closed-loop architecture, including automation and
analytics, significantly frees up IT time and resources to drive innovation
• DNA Assurance provides actionable 150+ insights for:
• Clients
• Network Devices, and
• Applications
• Machine Learning provides even deeper insights

DNAC 1.2 Platform:
Scale and Hardware specification
• Centralized deployment, cloud tethered
• 1 RU Small form factor
• 2 x 10Gbps Data links
• Built in Network Telemetry collection(FNF,
SNMP, Syslog)
• Built in Contextual connectors (ISE/PxGrid,
IPAM, Location)
• HA (3 Node, Automation), RBAC, Backup/Restore,
Scheduler, APIs
• 64-bit x86 Processors
• Solid State Disks in RAID10
• Hardware MRAID Controller
• Dual PSU
Scale:Single Node
5,000  4K APs + 1K Network Devices
25,000  Clients/Hosts
121

CAT2K / CAT3K / CAT4K Switches CAT9K / CAT6K / N7K Switches ASR / ISR / CSRv Routers
CA T2 K R ecommended OS M inimum OS
C2960-L IOS 15.2(2)E7 IOS 15.2(1)E1
C2960-P IOS 15.2(2)E7 IOS 15.2(1)E1
C2960-C IOS 15.2(2)E8 IOS 15.2(1)E1
C2960-CPD IOS 15.2(2)E8 IOS 15.2(1)E1
C2960-X Stack IOS 15.2(2)E6 IOS ≥ 12.1
C2960-XR IOS 15.2(2)E6 IOS ≥ 12.1
C2960-XR Stack IOS 15.2(2)E6 IOS ≥ 12.1
C2960-CX IOS 15.2(4)E3 IOS ≥ 12.1
C3560-CX IOS 15.2(6)E All Versions
C3650 (Copper) IOS-XE 16.6.1 All Versions
C3650-Stack IOS-XE 16.6.1 All Versions
C3850(Copper/Fiber) IOS-XE 16.6.1 All Versions
C3850-Stack (Copper/Fiber) IOS-XE 16.6.1 All Versions
C4500-X IOS-XE 3.10E All Versions
C4500-E (SUP 7E|7LE|8LE) IOS-XE 3.10E All Versions
C4507R+E (SUP 7E|7LE|8LE) IOS-XE 3.10E All Versions
C4503E (Sup 8E|9E) IOS-XE 3.10E All Versions
C4506E (Sup 8E|9E) IOS-XE 3.10E All Versions
C4507R+E (Sup 8E|9E) IOS-XE 3.10E All Versions
C4510R+E (Sup 8E|9E) IOS-XE 3.10E All Versions
C AT9K Re commended OS M i nimumOS
C9300 IOS-XE 16.6.2 IOS-XE 16.6.1
C9300 Stack IOS-XE 16.6.2 IOS-XE 16.6.1
C9400-LC-48UX IOS-XE 16.6.2 IOS-XE 16.6.1
C9400-LC-24XS IOS-XE 16.6.2 IOS-XE 16.6.1
C9400 (Sup1XL) IOS-XE 16.6.2 IOS-XE 16.6.1
C9400 (Sup1E) IOS-XE 16.6.2 IOS-XE 16.6.1
C9500 IOS-XE 16.6.2 IOS-XE 16.6.1
C9500 Stack IOS-XE 16.6.2 IOS-XE 16.6.1
IS R 4K R ecommended OS M inimum OS
ISR 4431 WIP WIP
ISR 4221 WIP WIP
ISR 4351 WIP WIP
ISR 4451-X WIP WIP
C6503E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
C6504E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
C6506E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
C6509E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
C6513E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
C6807-XL (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
C6840-X (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
C6880-X (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2
A S R 1K R ecommended OS M inimum OS
ASR 1001-X WIP ≥ 15.2(2)S, ≥ 15.3(1)S1
ASR 1002-X WIP ≥ 15.2(2)S, ≥ 15.3(1)S1
ASR 1006-X WIP ≥ 15.2(2)S, ≥ 15.3(1)S1
ASR 1009-X (RP2|RP3) WIP WIP
ASR 1001-HX WIP WIP
ASR 1002-HX WIP WIP
V ir tual Router R ecommended OS M inimum OS
CSRv (Virtual) WIP WIP
IS R 1K ( Selected PIDs Only )
R ecommend
ed O S
M inimum
O S
C1112-8P + (LTEEA) IOS-XE 16.7.1 IOS-XE 16.6.1
C1113-8P + (M,LTE*,WE,WA,WZ,MWE) IOS-XE 16.7.1 IOS-XE 16.6.1
C1114-8P + (LTEEA) IOS-XE 16.7.1 IOS-XE 16.6.1
C1115-8P + (PM, LTEEA,PMLTEEA) IOS-XE 16.7.1 IOS-XE 16.6.1
Wireless Controllers / APs
DNAC 1.2 Supported Network Platforms
122

Appendix—
DNA Assurance
in Action

Client Health Page
124124

Client Health Details
125125

DNA Assurance—Client 360 View

Apple iOS Analytics Insights
127

CLUS18—Monday June 11
128

DNA Assurance—Network Device 360 View

Cisco Connect Toronto 2018 DNA assurance

Cisco Connect Toronto 2018 DNA assurance

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