thank&youfor&joiningus&today! the&presentationwill ... · lessons learned and...

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1Copyright 2012-2016. SDNCentral LLC. All Rights Reserved February 5, 2016

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


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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


Welcome to SDxCentral DemoFriday™Monitoring your Modern Network:

SignalFx & Cisco IOS XR 6.0

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


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



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


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


Receiving unitTable 3 Table 2 Table 1

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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

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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