BRKDCN-2670 Day2 operations for Datacenter VxLAN EVPN fabrics.pdf

#CiscoLive
Naresh Veesamshety
Technical Leader – Datacenter Switching Marketing
BRKDCN-2670
Day2 operations for Datacenter
VxLAN EVPN fabrics

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BRKDCN-2670

#CiscoLive © 2023 Cisco and/or its affiliates. All rights reserved. Cisco Public
Agenda
• 3 Operational Challenges in Modern Data Centers
• 3 Traditional Workflows in solving the Operational Challenges
• 3 Transformations required to solve the Operational Challenges
• 3 Evolutions in the Cisco Nexus Portfolio
• 3 Operational Use-Cases with Nexus Dashboard Insights
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3 Operational Challenges in
Modern Data Centers

#CiscoLive
Configuration
Complexity!
Troubleshooting Performance
Monitoring
Operational Challenges in VXLAN EVPN Networks
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• Are you sure you haven't misconfigured ?
• Are you sure you did not miss necessary configs ?
Misconfigured/Missed
config
• Is you network configured in the best possible way ?
• Did not know about Cisco recommended best practice configuration!
Best Practices
• Still seeing problems ?
• Have you checked if there was a known bug or a PSIRT !
Hit a bug or PSIRT
Configuration Complexity
1
UNDERLAY OVERLAY
Physical Interfaces NVE/VTEP/Loopback
Routing Protocols MP-BGP/EVPN/Multisite
VLAN/VRFs L2VNI/L3VNI

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• Are you sure you haven't misconfigured ?
• Are you sure you did not miss necessary configs ?
Misconfigured/Missed
config
• Is you network configured in the best possible way ?
• Did not know about Cisco recommended best practice configuration!
Best Practices
• Still seeing problems ?
• Have you checked if there was a known bug or a PSIRT !
Hit a bug or PSIRT
Configuration Complexity
1
You can’t RESOLVE what you don’t KNOW .. You can’t
KNOW what you don’t SEE

#CiscoLive
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Configuration
Complexity!
Monitoring

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• Control plane validation of Overlay and Underlay
• Data plane validation of Overlay and Underlay
• Determine which node dropped the traffic
• Figure out the drop reason
• Attribute the drop reason to network problem
Complete
unreachability
Between servers
(E-W traffic)
Between users
and applications
(N-S traffic)
171.1.1.101 181.2.1.121
192.168.1.10
Troubleshooting
2

#CiscoLive
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Configuration
Complexity!
Monitoring

#CiscoLive
Performance Monitoring - Network
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171.1.1.101 181.2.1.121
Network
Performance
Latency
measurement
Intermittent
microbursts
Congestion
detection
20ms
• How do you prove network innocence ?
• How do you learn about intermittent microbursts in
the network ?
• How do you learn about perennial congestion in the
network ?
3

3 Traditional Workflows in
solving the Operational
Challenges

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Traditional Workflow in solving Operational Challenges
1 • CPU intensive and slow due to Polling/Pull collection.
• Possible loss of data during transport/high CPU/rollovers.
• Multiple OAM sources to get data.
Data Collection

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2 • Per-node/Per-data-model data stacks.
• Unstructured data built for human readability.
• Hard to operationalize.
Data Consumption

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3
• No tool chain that can understand all the disparate data.
• Collected data has no common format.
• Output of one node needs to be correlated manually to the input
of the next node.
Data Correlation

3 Transformations required to
solve the Operational
Challenges

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Model Driven Telemetry
• PUSH model – Reduce CPU request/response overhead.
• Data-model driven consistent formats (yang/openconfig)
• Extend to hardware telemetry.
• Large data compressed by GPB encoding.
• Minimize transmission delay of data on wire.
• Ability to stream high-resolution data.
• Single OAM defining data to collect
Model
Driven
Telemetry
SNMP
Screen
Scraping
ICMP
Syslogs
Transform Data Collection Model
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Single data lake architecture
• Multiple sources/nodes stream to a single data lake.
• Uniform structure makes data ready for consumption.
• Reduce data sprawling and loss of data.
Pub/Sub data consumption architecture
• Allows for the distribution of various functions of an application into micro-
services.
• A service can subscribe to only data that it needs.
• Provides the flexibility to offload resource-intensive computations from
switches to application services.
Transform Data Consumption
2

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Correlation Algorithms
• Structured data can help ‘Machine Learning’ algorithms replace manual
correlation.
• Multiple services can work in tandem to provide deeper insights.
• Identify cause and effects to reduce MTTR.
• Insights can derive actionable resolutions.
Transform Data Correlation
3

3 evolutions in the Cisco
Nexus Portfolio

#CiscoLive
NX-OS Model Driven Telemetry
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Capability: Stream
telemetry data for both
control and data plane.
Data Source: Supports
Yang, DME and NX-API
model.
Flexibility: Support both
sample-based and event-
based telemetry.
Encoding: Support for
GPB, GPB-compact, JSON
and XML encoding.
Transport Protocol:
Support for UDP, HTTP
and gRPC transport.
Enhanced Data Collection Model
1

#CiscoLive
Analogy of a similar evolution!
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Data Collection =
Data Processing =
Data Visibility =

#CiscoLive
Analogy of a similar evolution!
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Data Collection =
Data Processing =
Data Visibility =
All we needed was one
powerful PLATFORM that
could help us do it all!

#CiscoLive
Nexus Dashboard to the rescue!
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Private Cloud Public Cloud
Fabric Discovery
Orchestrator
Insights
Data Broker Fabric Controller
SAN Controller
NDFC
3rd Party apps
Simple to Consume ; Simple to Automate
2

#CiscoLive
Nexus Dashboard to the rescue!
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Private Cloud Public Cloud
Fabric Discovery
Orchestrator
Insights
Data Broker Fabric Controller
SAN Controller
NDFC
3rd Party apps
Simple to Consume ; Simple to Automate
2

#CiscoLive
Nexus Dashboard Insights to the rescue!
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Configs
Intent
Infra
state
Intent
Configs
Infra
state
Data
enrichment
Complex
correlation
Baselines
Modeling
Nexus
Dashboard
Insights
Software and
hardware
telemetry
{REST:API}
Syslog
Email
Kafka
Nexus Dashboard
VCenter
AppDynamics DNS Server

#CiscoLive
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Configs
Intent
Infra
state
Intent
Configs
Infra
state
Data
enrichment
Complex
correlation
Baselines
Modeling
Nexus
Dashboard
Insights
Software and
hardware
telemetry
{REST:API}
Syslog
Email
Kafka
Nexus Dashboard
VCenter
Cisco Digitized Metadata

#CiscoLive
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Configs
Intent
Infra
state
Intent
Configs
Infra
state
Data
enrichment
Complex
correlation
Baselines
Modeling
Nexus
Dashboard
Insights
Software and
hardware
telemetry
{REST:API}
Syslog
Email
Kafka
Nexus Dashboard
VCenter
Simple to Correlate
3

#CiscoLive
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Configs
Intent
Infra
state
Intent
Configs
Infra
state
Data
enrichment
Complex
correlation
Baselines
Modeling
Nexus
Dashboard
Insights
Software and
hardware
telemetry
{REST:API}
Syslog
Email
Kafka
Nexus Dashboard
VCenter
Simple to Correlate
3

#CiscoLive
Nexus Dashboard Insights – Feature set
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Assurance and
Compliance
Advisory and
Maintenance
Visibility and
Troubleshooting
PSIRT notification Field notices
Upgrade assist Advisories
TAC assist Email notifications
One View Flow analytics
Connectivity analysis Microburst detection
AppDynamics integration Capacity planning
Endpoint analytics Topology view
Delta analysis
compliance
Configuration assurance
Explorer

3 Operational Challenge Use-
Cases with Nexus Dashboard
Insights

Configuration Assurance and
Information Awareness
1

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Simplifying : Information you need
NXOS Version: 9.3(5)
How Am I
supposed to
know
PSIRT Alert Known Bug Best practices
L1 L2 L3 L4
S1 S2
BL2
BL1
ToR1
1

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Simplifying : Information you need
How Am I
supposed to
know
Cisco Digitized Metadata NDI
• Best practices updates
• PSIRTs, FNs, EOS/EOL
• Software release notifications
• Digitized signatures of known
defects
L1 L2 L3 L4
S1 S2
BL2
BL1
ToR1
1

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Simplifying : Making aware of what you need
L1 L2 L3 L4
S1 S2
BL2
BL1
ToR1
Get
Notified !
1

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PSIRT Fixed Bug Fixed Best Practice
Compliant
Cisco Recommends :
Upgrade 9.3(5) -> 9.3(10)
L1 L2 L3 L4
S1 S2
BL2
BL1
ToR1
1

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Assurance of Intent
L1 L2 L3 L4
S1
S2
BL2
BL1
ToR1
Host A
1

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Assurance of Intent
L1 L2 L3 L4
S1 S2
BL2
BL1
ToR1
1

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Assurance of Intent
1

Improve: Troubleshooting
Efficiency
2

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Network is all setup!
WAN
50.1.55.1
181.1.1.101
L1 L2 L3 L4
ToR1
S1 S2
BGW1 BGW2
Remote Edge
DC

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WAN
50.1.55.1
181.1.1.101
L1 L2 L3 L4
ToR1
S1 S2
BGW1 BGW2
Remote Edge
DC
50.1.55.0
50.1.55.0
Network is all setup! A good day !

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WAN
50.1.55.1
181.1.1.101
L1 L2 L3 L4
ToR1
S1 S2
BGW1 BGW2
Remote Edge
DC
50.1.55.0
50.1.55.0
Bad things happen!

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Simplify : Troubleshooting
WAN
50.1.55.1
181.1.1.101
L1 L2 L3 L4
ToR1
S1 S2
BGW1 BGW2
Remote Edge
DC
50.1.55.0
50.1.55.0
But now I know what happened!!
2

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Session ID
But now I know what happened!!

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E-W traffic is fine .. Life is good!
131.101.14.162
131.101.1.102

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We can’t talk!
131.101.14.162
131.101.1.102

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Hosts complained - We can’t talk!
131.101.14.162
131.101.1.102
Hey Network Guy..My
applications can’t talk. Can you
help ??

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Proactive Drop Detection
131.101.14.162
131.101.1.102

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Proactive Drop Detection
131.101.14.162
131.101.1.102
Traffic drops can be
proactively detected
with reasons for drop
2

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On-Demand Troubleshooting
131.101.14.162
131.101.1.102
2

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131.101.14.162
131.101.1.102
Simplify: On-Demand Troubleshooting
2

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2

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Fix it and reassure J
2

Simplify : Network
Performance Monitoring
3

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Measuring Network Performance: The Paradox !
Hey Network Guy..My
application became too slow. Is
it the network ??
131.102.13.129: 443 131.104.2.101: 49152

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131.104.2.101: 49152
131.102.13.129:443
How do we Measure Network Latency ?

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131.104.2.101: 49152
131.102.13.129:443
Simplify: Measuring Network Latency
Be notified of an outlier!
Ah.. I can now measure
network performance of
the application traffic
3

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172.16.113.118
172.16.112.98
Microburst detection when it happens!

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172.16.113.118
172.16.112.98
Microbursts happen!

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172.16.113.118
172.16.112.98
Microburst detected!
3

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Simplifying : Microburst detection
3

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Disjoint Network
Entities
SNMP
polling/Logs/CLI
Outputs
Siloed Data Stacks
Manual
Interpretations
Broken Insights/No
Correlation
No End-to-End
Perspective
Network/Nodes
Unification
Streaming
Telemetry
Network unified
workflow
Machine Driven
Correlation
Actionnable Insights
End-to-End Network
Visibility
Operation possibilities in the Data Center
1

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BRKDCN-2670 Day2 operations for Datacenter VxLAN EVPN fabrics.pdf

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