Cisco Connect Ottawa 2018 dna assurance shortest path to network innocence

Cisco Connect Ottawa
Canada • 02 October 2018
Global vision.
Local knowledge.

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Cisco Connect

Don Orlik, Product Specialist – Digital Network Architecture
Nathaly Landry, Systems Engineer
The Shortest Path to Network Innocence!
DNA Analytics and Assurance

Cisco Connect delivers education and
inspiration to technology innovators
worldwide.
• What is DNA Center
• Introduction to Context and Machine Learning
• Design Thinking
• DNA Center Assurance: Using DNA Center for Client
Assurance
• DNA Center Assurance: Using DNA Center for Network
Assurance
• DNA Center Assurance: Using DNA Center for Application
Assurance
• DNA Center Assurance: Using Wireless Sensors
• Key Takeaways
AGENDA
Slides
&
Demos

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public
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?

. 1 McKinsey Study of Network Operations for 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

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

The Cost of Troubleshooting
Typical troubleshooting issues for an enterprise network with 800 users (wired and wireless)
Issue/task DNA Center™ Traditional CLI Savings
Occurrences
per week
Hours saved
per week
Days saved
per year
Traceroute Instantaneous 6 minutes 6 minutes 25 2.5 hours 15
Slow onboarding 2 minutes 17 minutes 15 minutes 20 5 hours 30
Device RPA failure Instantaneous 20 minutes 20 minutes 6 2 hours 12
Radio channel analysis 5 minutes 25 minutes 20 minutes 6 2 hours 12
Issue replication 5 minutes 65 minutes 60 minutes 2 2 hours 12
Site visit Not required 180 minutes 180 minutes 0.5 1.5 hours 9
Total: 15 hours 90
What would you do with 90 extra productive days per year?90
*RPA: Routing protocol adjacency failure

What is DNA
Center?

DNA Center: Design, Policy, Provision, Assurance
A better way to manage your network
DNA Center: Design, provision,
automate policy and assure
services from one place
Logical workflow to design,
provision, set policy
Respond to changes faster
Monitor end-to-end
network performance
Predict and act on problems
before they happen
Pinpoint problems faster
Reduce downtime with an
end-to-end view instead of
hop by hop
Manage hardware and
software lifecycles
Keep up to date, meet
compliance and plan for refresh
DN1-HW-APL
Current version 1.2.5

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Decouple Policy from
Network Topology
Industry Best-Practices
and Policy Compliance
Proactive Issue
Identification and
Resolution
Business Intent driven
Network Changes
Simplify Day 0 to Day
N Changes
Monitoring
and Troubleshooting
Fabric Network Automation Assurance
Introducing DNA Center
Policy-Based Network
Covered in the this
session

Decouple Policy from
Network Topology
Industry Best-Practices
and Policy Compliance
Proactive Issue
Identification and
Resolution
Business Intent driven
Network Changes
Simplify Day 0 to Day
N Changes
Monitoring
and Troubleshooting
Fabric Network Automation Assurance
Introducing DNA Center
Policy-Based Network
Covered in the
morning session

Digital Business
SecurityMobile IoT MultiCloud
Network
Intent Context
Security
Learning
Cisco DNA—
Intent-based Networking for the Enterprise

ip access-list extended MM_STREAM-ACL
remark citrix - Citrix
permit tcp any any eq 1494
permit udp any any eq 1494
remark citrix-static - Citrix-Static
permit tcp any any range 2512 2513
permit udp any any range 2512 2513
remark pcoip - PCoIP
remark timbuktu - Timbuktu
remark xwindows - XWindows
remark vnc - VNC
permit udp any any range 5900 5901
exit
ip access-list extended SIGNALING-ACL
remark h323 - H.323
Legacy QoS Policy
Intent-Based
Application Policy

Digital Business
SecurityMobile IoT MultiCloud
Network
Intent Context
Security
Learning
Cisco DNA—
Intent-based Networking for the Enterprise
Pillars
addressed
by DNA
Analytics
and
Assurance

Contextual
Information

The Value of Context
“By all means go and see this play”
—Theatre Critic
(as quoted in an advertisement by the play’s producers)

“If triviality
is what you happen to be wanting,
by all means go and see this play”
—Theatre Critic
The Value of Context

The Challenge of Context:
Transforming Data into Actionable Insights
Data
Insight
Information
Action
Create value at the right
time
Extract meaningful insights
from data
Businessbenefit
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
19

Transforming Data into Actionable Insights
Example
Data
Insight
Information
Action
WebEx has 63 ms of jitter
WebEx Application Health Score is 70%
WebEx is not being marked on the WLAN
Enable Fastlane for WebEx
20

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

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

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

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

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

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

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

Machine
Learning

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 patterns within 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:
Correlation does 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

Design
Thinking

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% of pediatric 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) à 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!”
34

Design Thinking “Sweetspot”

Design Thinking Core Phases

39

42

48

Technical
Requirements

DNA Assurance
50

Collect relevant metrics
Self-Healing Network Architectural Requirements
#1: Instrumentation
EM
App
Servers
Sensors
53

Categorize metrics by degrees of relevance
#2: On-Device Analytics
54

Upload critical metrics off the device to collector(s)
(optimally via model-based streaming-telemetry)
#3: Telemetry
EM
Collector
55

Provision long-term storage, retrieval and representation of network metrics and events
#4: Scalable Storage
56

Identify anomalies and trends
#5: Analytics Engine
57

Analyze all data points and permutations for cognitive and predictive analytics
#6: Machine Learning
58

Identify root cause of issues by contextually correlating data
#7: Guided Troubleshooting
EM
Analytics
Engine
59

#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
60

DNA
Assurance

DNA Software Capabilities
Cloud Service Management
Automation Analytics
Virtualization
DNA-Ready Physical and Virtual infrastructure
Security
Cisco DNA Architecture
62

DNA Center
A window into the Network for Design, Provision, Policy and Assurance
DNA Center Appliance
EM
NDP
NDP:
Network Data Platform
(Analytics Engine)
EM
NCP
NCP
Network Controller Platform
(Network Controller)
Automation Analytics
63

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

Wireless Sensors Proactively Assess Performance
Test your network anywhere at any time
R1
Dedicated Sensor AP1800 Flexible Radio
Sensors act as
clients
Access point
Ø On-Boarding Tests
• 802.11 Association
• 802.11 Authentication & Key Exchange
• IP Addressing DHCP (IPv4)
Ø Network tests
• DNS (IPv4)
• RADIUS (IPv4)
• First Hop Router/Default gateway (IPv4)
• Intranet Host
• External Host (IPv4)
Ø 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
65

Demo Time

Client Health Page
67

Client Health Details
68

Client 360
69

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
Power 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: 66 issues in DNA-C 1.1
70

Client 360—Issues

Client 360—Onboarding Details
72

Client 360—RF Details
73

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
74

Client 360—iOS Analytics
75

Wireless Sensors Proactively Assess Performance
Test your network anywhere at any time
R1
Dedicated Sensor AP1800 Flexible Radio
Sensors act as
clients
Access point
Ø On-Boarding Tests
• 802.11 Association
• 802.11 Authentication & Key Exchange
• IP Addressing DHCP (IPv4)
Ø Network tests
• DNS (IPv4)
• RADIUS (IPv4)
• First Hop Router/Default gateway (IPv4)
• Intranet Host
• External Host (IPv4)
Ø 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
76

Run Network Tests
• See What Works and Where
77

Run Network Tests
• Hover over a Test to See Results
78

Filtering the View
• Select Only the Tests That Matter to You Now
79

Network Health

Network Health—Site View

Network Health—Topology View

Network Health—By Device Roles

Network Health—Device 360 (Part 1 of 3)

Network Issues and Troubleshooting Example

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?

Client 360—Application Experience

Client 360—Application Experience (cont)

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

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)

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

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)
Capture

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
Capture

Intelligent Capture—Wireless OnBoarding Analysis

Intelligent Capture—Application Analysis

Intelligent Capture—Application Analysis (cont)

Network Value Beyond Connectivity
Integrated System
The Network is the Foundation
Programmable Products
A Platform for Open Networking
Seamlessly integrating IT systems and business processes
Visibility Availability Performance
Analytics and AssuranceSecurity Policy and BehaviorAutomation
Value Beyond the Network:
IT Efficiency | Business Intent | Service Assurance
Open Platform
Network Efficiency
Business and
IT Efficiency

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, Networks and
Applications
• Additional ongoing-development initiatives were previewed, including
• Application Dynamics Integration
• Application Integration
• StealthWatch Integration
DNA Assurance—Summary

Cisco Connect Ottawa 2018 dna assurance shortest path to network innocence

Cisco Connect Ottawa 2018 dna assurance shortest path to network innocence

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