Cisco Connect Toronto
Canada • 18 October 2018
Global vision.
Local knowledge.

Cisco Kinetic:
Enabling Data Analytics for
the Internet of Things
Robert Barton, Distinguished Systems Engineer
October 18, 2018

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A Quick
Introduction to
Data Analytics

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A Quick Introduction to Data Analytics
Automation for Faster
Results
Reveal
Hidden Patterns
Make Data
Driven Decisions
Focus on
Important Things
• The goal is to uncover correlation and connections in large data sets
• We need to understand how to communicate with customers differently

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Value
Complexity
Descriptive
Analysis
Diagnostic
Analysis
Predictive
Analysis
Prescriptive
Analysis
The Increasing Value of Data Analytics
What is
Happening?
Why did it
Happen?
What is Likely to
Happen?
What Should I do
About it?

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From Big Data
How Do we Get to Business Insights from Data?
Provide Actionable Insight
Reports and Dashboards
Extract Intelligence
Enhance the data
Quality Check the data
Transform the data
Connect the data
To Insightful Decisions

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Big Data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using traditional data processing tools
So, What is Big Data ?(per Wikipedia definition)

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3Vs of Big Data
Big Data refers to distributed computing architectures specifically
aimed at the “3 V’s” of data: Volume, Velocity, and Variety
VELOCITY VARIETYVOLUME

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Unstructured vs. Structured Data
Unstructured data is a poor fit for traditional relational databases
2005 20152010
•More than 90% is unstructured data
•Quantity doubles every 2 years
•Most unstructured data is neither
stored nor analyzed!
10,000
0
GBofData
(INBILLIONS)
STRUCTURED DATA
UNSTRUCTURED DATA
Source: Cloudera

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Batch vs. Real-Time Analytics
Batch: Data at Rest
• Requires Batch-style analytics
• Huge volumes of data on disk
• Structured or semi-structured
• No rush to return the results
• Analysis is typically ”batch style”
Real-Time: Data in Motion
• Requires Real-time/Streaming style
analytics
• Typically processed in memory
• Tremendous velocity, high bandwidth
• Diverse data sources
• Low-latency processing required

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Data Management Systems Need Flexibility
Traditional Data
Warehouse
Big Data Store
Structured Data Unstructured Data Data Streaming at the Edge
Short and/or
Long Term
Make a Decision Today
Real Time
Make a Decision RIGHT NOW
Long Term
Plan for a Business
Decision

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Data
Preparation
Analyze Data
(Machine
Learning)
Connect to Data
Data
Visualization
Normalization of data flows
with context information, etc.
Data is
collected from
sensors
Process the data to derive
appropriate insights
GUI, REST API,
Kafka, Policy Export,
…
How do we Get Business Insights from Data?

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Big Data and The Data Management Lifecycle
Data
Preparation
Analyze The DataConnect Data
Data
Visualization
Cisco Provides Enablement Platforms
Supporting this architecture

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Understanding the Roles
Analytics / BI Software
Packages
Selection of the Right
analytics Tools
SQL Developers
Visual Analytics Tools
Users of Data Mining Tools
Data Acquisition
Data Manipulation
Data Movement
Machine Learning Methods
Connecting Data Insights to
the Business
Data
Scientist
Expertise
Data
Analyst
Expertise
Network Engineers build the platform supporting the business

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Types of Big Data Management Systems
NoSQL (Not Only SQL)
Fast key-value
store/retrieve in real time
Hadoop
Distributed batch,
query, and processing
Massive Parallel
Processing (MPP)
Shared-nothing
Structured and Unstructured
Unstructured Data
Structured Data (SQL)

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Example: Hadoop Cluster in the DC or Cloud
Rack 1 Rack 2 Rack 3 Rack N
Name
Node
Rack
Switch
Data
Nodes
Distribution Switch• Big Data systems (such as
Hadoop) are primarily batch-type
analytics systems and are
centralized in the cloud of DC
• Not well suited for distributed IoT
devices
Name
Node
Name
Node

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Distributed
Analytics and
Data Movement

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How do you Access all the Data ?
• Imagine a mega-field of oil with no
way to extract it from the ground
• Data needs to be extracted before
it can be refined, and then used

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In IoT, the “Things” are Distributed
App
App
App
App
App
App
App
App
AppApp

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The OpenFog Reference Architecture

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• Data is collected and consolidated from various
sources by a “message broker”
• Think of the broker as a “Router” for data
• Aggregates data
• Uses standard protocols for data transmission,
such as MQTT or AMQP (Advanced Message
Queuing Protocol)
• Apache Kafka is well-known distributed
messaging system designed to scale for Big Data
Aggregating and Moving Data Around
Message Brokers

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MQTT
• Clients Subscribe to topics to publish and receive messages
• Broker (MQTT Server) receives subscriptions from client topics
• Broker receives messages from clients and forwards to interested clients
Model
Publisher SubscriberTopic
Message Message
Broker
Subscribes to an MQTT
“topic” e.g. “temperature”

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Cisco Kinetic
Building Blocks

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Cisco Industrial IoT Networking Portfolio
Embedded
Networks
ESS, 5900 ESR
5921 SW ESR
IoT
Gateways
IR809, 829, 819H
Industrial
Security
ISA 3000
Industrial
Wireless
AP1552, AP1572,
IW3702
Industrial
Switching
IE 1K,2K,3K,4K,5K,
CGS
LoRaWAN
Industrial
Routing
ASR 902/903,
CGR 1000, CGR 2000
Edge
Computing
IOx
Fog Director
Management and
Automation
Field Network Director
Industrial Network
Director
Transportation Manufacturing Public SectorEnergy
IXM-LPWA

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IOX + Kinetic
IOX
IOS GOS (Linux)
Hypervisor
IR 8x9 HW
Kinetic

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IOX / Kinetic Network Infrastructure
IR829IR809 IE4000
Compute Module
CGR1240
§ Ethernet
§ Cellular 3G, 4G LTE
§ Wi-Fi
Broad Connectivity
§ HW Accelerated
Encryption
§ IPSec VPN
§ 802.1x
§ Firewall
§ Identity Services
Proven Security
§ Ruggedized for
shock/vibration,
humidity, temperature,
dust
§ DC power supplies
Industrial Grade
§ Centralized control
§ Network
§ Security
§ Fog applications
Policy-based
Management

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IoT Edge | Industrial
Compute Gateway IC3000
• Cisco IOx edge computing framework
• Developer tools for fast onboarding of container and VM
applications
• End-to-End Security
• Anti-tamper proof hardware and Secure bootloader
• Secure Cisco Linux Kernel
• Signed and trusted applications
• Management
• Zero touch deployment of thousands of gateways
• Remote upgrades of firmware and edge applications
• Remote monitoring, diagnostics, and troubleshooting of
gateways and edge applications

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The Cisco
Kinetic Platform

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What Does Kinetic do?

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Intent-Based Network
Cisco Networking
IoT Data Fabric
Cisco Kinetic is the Industry Leading IoT
Analytics Enablement Platform
Gateway Management
Module (GMM)
Edge & Fog
Processing Module (EFM)
Data
Control Module (DCM)

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§ Rapid deployment of gateways at scale
with minimum technical knowledge;
limited training required
§ Gateway management from single
pane of glass
§ Reduce upfront deployment and
ongoing maintenance cost
§ Improve the efficiency of operations
and utilization of assets and operators
§ Real-time status allows issues to be
identified and resolved in timely
manner
Cisco Kinetic – Gateway Management Module
Gateway Management Module
Intent-Based Network
Cisco Networking
Automatic
Onboarding
Configuration
& Management
Location
Tracking
Real-time
Monitoring
OS
Upgrades

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Kinetic Gateway Management Module
Secure connectivity for OT
• Secure connection of IoT Devices
• Simplified GW management at scale
• Secure IoT app lifecycle management
Cisco Kinetic
APPconfig

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LTE Provider 1
Provider 2
Jasper Control
Center
Dual-SIM Mobile
Router (e.g. IR829)
on vehicle
LTE 2LTE 1
Provider 1
Jasper Control
Center
Cisco Kinetic (GMM)
FlexVPN Control
Tunnel
Kinetic GMM is the
FlexVPN Control
Plane
The vehicle
has one active
LTE backhaul
Kinetic GMM Deployment Example
Large Transit Operator in Canada
LTE Provider 2
FlexVPN Data
Tunnel

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Easy On-Boarding with Kinetic Mobile App
Large Transit Operator in Canada

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Intent-Based Network
Cisco Networking
IoT Data Fabric
Edge and Fog Fabric Module (EFM)
Gateway Management
Module (GMM)
Edge & Fog
Processing Module (EFM)
Data
Control Module (DCM)

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Multiple raw
input Streams
from different
sensor types
The DSA message broker
consolidates IoT data and
forwards to cloud
Big Data storage
and deeper
analytics in the
cloud (HDFS,
etc.)
…ABBC
Cisco Kinetic Edge Fog Module (EFM)
DS Links (data connector)
• Data acquisition and protocol translation
• facilitating communication between edge
device and message broker
• MQTT, Modbus,
Note: Model is Based on Distributed Services Architecture (DSA) – http://www.iot-dsa.org
Fog / Edge Node (router or UCS C-Series)

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Real-Time / Streaming Analytics at the Fog Layer
• Performs analytics in real-time data
as it is produced by devices
• Allows near-instant response to
changing conditions
• Aggregate data from many types of
sensors
• Use local storage for time-series
analysis (a data Historian)
Example: Analyze if data is outside
“Bollinger Bands” – a measurement of
volatility (standard deviations)

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Cisco Kinetic Edge and Fog Processing Module (EFM)
Network
Products
Admin/Control
Runtime
System
Monitor
ParStream
(Historian Database)
DSLinks
Dataflow (analytics)
DSA Message Broker
System
Administrator
Development
IoT Gateway/Routers
Dataflow
Editor
Visualization
BI and Data
Visualization
Real-time
Data
Visualization
Acuity DGLux5

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Visual, Object
Oriented Data
Flows that Map
to Process are
the future
Drag and drop data policy
Kinetic EFM (Edge & Fog Module)
Intuitive, visual data policy editor to accelerate IoT projects

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Example: Data Management for Oil Drilling
7,000 Oil Wells x 1TB Data/Day/Well = 7PB Data/Day
Edge
Processing
Data Center
Fog
Processing
300 Platforms
7,000 Oil Wells
40

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Kinetic EFM and End-to-End IoT Analytics
Edge Processing
Temp Link
Flow Link
Flow Link
Temp Link
EFM
Broker
EFM
Broker
Edge Location
(7000)
Processing
Platforms (300)
Fog Processing
Link
Link
Historian
DB (Cisco
ParStream)
Streaming
Analytics
EFM
Broker
Dashboard
Kafka
Broker
Hadoop
Data
Virtualization
Regional (Just 1)
Data Center
Dashboard
Batch Analytics and
Machine Learning

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Intent-Based Network
Cisco Networking
IoT Data Fabric
Data Control Module (DCM)
Gateway Management
Module (GMM)
Edge & Fog
Processing Module (EFM)
Data
Control Module (DCM)

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Data Control Module (DCM)
Kinetic provides:
• Gateway Configuration &
Connectivity management
• Application lifecycle
management
• Fog computing
• Secure data delivery
Edge Fog
Data Center
Hybrid Cloud
App
Cloud partners provide:
• Cloud software services
• Application development tools
• Big data analytics
• Machine learning and
cognitive computing
• Edge execution models
(ML inference, server-less
computing, app frameworks)
BROKER
Devices

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Kinetic Data Control Module
Secure data delivery
Data
Type 2
Data
Type 3
Data
Type 4
Data
Type N
Data
Type 1
Data Control
Data policy
Move the data
Cloud neutral
Integrated with Kinetic IoT GW and fog
application management
Provide data routing policies to easily
move IoT data to cloud applications
across multi-cloud & multi-location
deployments

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Kinetic DCM Demo / Example
M
QTT
Subscription
Raspberry Pi
Temp Sensor
Cisco IR809 Router
MQTT Publisher
Message Broker Data Client
(MQTT Subscriber)
HVAC VAV’s
Smoke alarm
MQTT Publisher
BROKER

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Putting it all Together:
Sensors &
Endpoints Applications
Edge Devices
(IE4k, GW, Compute)
Enterprise
Data Centers
Fog
Compute
Cloud Apps
EFM
Fog & DC EFM adds
additional compute
capabilities, including
normalization, aggregation,
storage and visualization
2
DCM adds secure cloud
connection capabilities
3
GMM enables mass
deployment and
management of
gateways
4
EFM
Gateway
Management
Module (GMM)
EFM extracts data from
sensors, transforms it
and sends it either to
another IoT device,
1
or to a hosted app
EFM
EFM EFM
On-prem or private
cloud apps
EFM
Data Control
Module (DCM)

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It Always Starts with a Operations or Business Problem
Real-time Quality Detection
Preventative Maintenance
Remote Monitoring
Real-time Quality
Detection
Asset Tracking & Management
Personnel Safety
OEE (Overall Equipment
Efficiency) Condition-Based
Maintenance