thank&youfor&joiningus&today! the&presentationwill ... · lessons learned and...
TRANSCRIPT
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Thank you for joining us today!The presentation will begin shortly. Thank you for your patience.
1Copyright 2012-2016. SDNCentral LLC. All Rights Reserved February 5, 2016
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2February 5, 2016
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Copyright 2012-2016. SDNCentral LLC. All Rights Reserved
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Matt PalmerCo-Founder
SDxCentral
Adam ZimmanHead of Business
Development
SignalFX
Shelly CadoraPrinciple Engineer
Programmable Networks
Cisco
Copyright 2012-2016. SDNCentral LLC. All Rights Reserved February 5, 2016
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Relevant Industries Who Should Attend? Key Takeaways
• Web Service Providers• Service Providers• Large Scale Datacenter
Operations
• Network Operations• Network Administrators• Network Architects• Cloud Architects• DevOps• Site Reliability Engineering
• Introduction to Cisco IOS XR and Streaming Telemetry
• Monitoring needs to evolve with the modern networks and applications
• Static Thresholds are not enough; building dynamic alerts
Welcome to SDxCentral DemoFriday™Monitoring your Modern Network:
SignalFx & Cisco IOS XR 6.0
Copyright 2012-2016. SDNCentral LLC. All Rights Reserved February 5, 2016
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Today’s Demo:
• IOS-XR 6.0 Streaming Telemetry
• Ease of integration with open source and hosted software
• SignalFX Hosted Monitoring Solution
Copyright 2012-2016. SDNCentral LLC. All Rights Reserved February 5, 2016
Welcome to SDxCentral DemoFriday™Monitoring your Modern Network:
SignalFx & Cisco IOS XR 6.0
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Shelly Cadora
February 2016
Streaming Telemetry
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Data Is Created In the Network, But Isn’t Useful There
sensing &measurement
Where Data Is Created
storage & analysis
Where Data Is Useful
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Getting Data Out of the Network Is Hard
sensing &measurement
Where Data Is Created Where Data Is Useful
syslog
SNMP
CLIstorage & analysis
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Network Monitoring Is A Big Data Problem
New Capabilities New Requirements
• Speed, scale, SDN • Better traffic engineering• Gray failure detection• Fault prediction• Automated remediation
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Identify Primary Concerns, Weigh Trade-offs
sensing &measurement
Where Data Is Created Where Data Is Useful
storage & analysis
Performance Completeness
Encoding Data Models
Strongly typedSelf-describing Event-based
Pub-sub
Introspectible Customizable
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Two Views of the Network’s Job
Where Data Is Created Where Data Is Useful
As much data, as fast as possible
• Store• Model, Transform• Event, Alert
• Stream processing• Filtering• Model, Transform• Alert, Event• Storage• Batch processing
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Initial Goal: Validate the Big Data Proposition
Performance + Encoding“As much data as fast as possible”
Enable a push model
Make data simple to use
Focus on the WAN
IF IP QoS BGP MPLS System IGP
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Lessons Learned and Future Iterations
Model-Driven ManagementReal Operational Models
Vendor Neutral, YANG-based
Dynamic policy subscription
Open, streaming, secure transport/RPC
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Instruction on:• What data to collect• With what cadence• And send to where
Ultra-high level picture
Router Receiving unit
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Ultra-high level picture
Router
Instruction on:• What data to collect• With what cadence• And send to where
Receiving unitTable 3 Table 2 Table 1
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Instruction on:• What data to collect• With what cadence• And send to where
Router Receiving unitTable 3 Table 2
Interface ifInErrors ifOutErrors ifHCOutOctets …
HundredGigabitEthernet0/1/0/2
10 0 123456789 …
Bundle-Ether 42 3 0 234567890 …
… … … … …
Table 1
Ultra-high level picture“I am the interface counters table”
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High Level Streaming Telemetry Architecture
Common namespace
/ interaction
model
TelemetryEngine
GPB Encoder
JSON Encoder
RemoteManagement
StationXR
Feature
XR
PolicyConfig
XRFeature
XRFeature
RemoteManagement
Station
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Some Consumption Models
Logstash
ElasticSearch
Kibana
ST Input Codec
Output Codec
Kafka
Hadoop
Impala
BYO Black Box
SST
Custom Open Source, Customizable
Proprietaryor OS-based
SST
Commercial Stack
ODL
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10.1.0.1
10.0.0.6
10.0.0.2
.5
.17
.13
.1 .9
.18
.10
.14
10.6.0.1 10.3.0.1.25
10.2.0.1
.26
10.7.0.1.29
.30
10.5.0.1.22
10.0.0.X = Loopback
.21
.38
SFO NYC
BOSCVG
ATL MIA
RSVP TE TUNNEL WITH AUTO-BW
CLIENT SERVER
CollectorsTopology
.py
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20© 2015 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 20
Demo
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INFRASTRUCTURE MONITORING FOR THE MODERN STACK
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MODERN APPS ARE FUNDAMENTALLY DIFFERENTIncreasingly complex, operationally unpredictable, rapidly evolving & heavily distributed
S E R V I C E O R I E N T E D E L A S T I C A G I L E
Short sprints with frequent code pushes and continuous
integration
Multiple Distinct, yet Inter-related, Services
combine to Provide Application Functionality
Low cost of VMs/containers + ease of spinning up new capacity
has democratized scale-out architectures
2-week
6/1 Release
6/15 Release
6/29 Release
2-week
2-week
Apps
VM
Checkout Service
VM VM VM
VM VM VM VM
ITPublic/Private Cloud
(w/ Self-Service APIs)
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OP E RAT I ON AL I N TE L L I GE N C E
SignalFlowTM
Streaming & HistoricalAnalytics
Ask, Anticipate & Act in Real-Time Real-time visibility and correlation across the stack
Compare incoming patterns against historical patterns in real-
time
No query language needed
Intelligent & dynamic alerting
Resolution down to 1s
Leverage your existing investments in metrics, events and
logs
Prebuilt integrations and content
S Y S T E M M E T R I C S & E V E N T S
A P P M E T R I C C & E V E N T S
U S E R M E T R I C S & E V E N T S
B U S I N E S S M E T R I C S & E V E N T S
S i g na l Fx B E N E F I T S
THE NEW OPERATIONAL TOOLSETReal-time metrics monitoring for modern applications & architectures
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• THE TREND IS MORE IMPORTANT THAN THE STATIC VALUE
• ANALYTICS TURN MONITORING INTO A PROACTIVE MEASURE
• THE FASTER THE ANALYTICS, THE BETTER YOUR ALERTING
BU ILD ACTIONABLE & TIMELY ALERTSMultidimensionality and speed of analytics are key
USE ALL DIMENSIONS TO ASK, ANSWER AND ALERT IN REAL-TIME:
• LATENCY, BY CUSTOMER, BY REGION, BY DEVICE TYPE, BY OS, ETC…
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AbsoluteReturn absolute
value of a datapoint
LN / Log10Calculate natural or base-10 logarithm
of datapoint
ScaleMultiple each datapoint by a
specified number
CeilingRound values up to
nearest integer
Mean(Average)
Calculate arithmetic mean
Square rootCalculate
positive square root of
datapoint
CountCount # of time
series with values
Mean + StdevCalculate mean
plus user-specified number of standard
deviations
Standard deviationEstimate standard
deviation in a set of datapoints
DeltaCalculate difference between current and
previous value
Minimum /Maximum
Return smallest / largest value in a set
of datapoints
SumAdd up all the
values in a set of datapoints
FloorRound values down to nearest integer
PowerCalculate result of
datapoints raised to specified power (or vice versa)
Top / BottomDisplay subset of time series by count or %
(e.g. top n)
IntegrateMultiple values by
resolution (in seconds) of the chart
Rate of changeDivide delta by no. seconds per time
interval
VarianceEstimate variance
in a set of datapoints
ExcludeFilter time series by
value. Can be used to create SumIf, CountIf
etc.
PercentileCalculate user-
specified percentile.
TimeshiftShow datapoints offset by user-
specified period.
MAT H A S A S E RV I C E
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FULL STACK INTEGRATIONS
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DEMO
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S I G N U P F O R A T R I A L AT :
http://info.signalfx.com/cisco.html
Follow us: @signalfxFollow me: @azimman
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https://www.sdxcentral.com
Questions & Answers
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