edgeprocessing-allseenalliance 4x3 template 24sept2014...erp, crm, and other enterprise apps...

13 October 2015 AllSeen Alliance 1

IoT Edge Processing

JEFF KIBLER (@jrkibler)

VP Tech Services, Infobright

Evolution of edge computing analytics and long-term data retention


1. IoT Premise and Challenges

2. Exposing Opportunities

3. Directions to Consider

4. Moving from Possible to Practical

5. Wrap-up

Agenda

3

IoT Foundations

Premise and Challenges


The Life of the End User

Athletics

Multi-billion dollar

industries where 1%

competitive edge

decides careers.

Infrastructure

Increasing reliance on

alternative energy,

permeable surfaces,

and environmental

metering.

Telemetry

Predicting and

improving health

outcomes.


Premise

Leading Verticals

IoT Presents a Large Market Opportunity

Leading Challenges

• Data Security

• Infrastructure

• Privacy

• Governance

• Industrial Equipment

• Oil/Gas/Energy

• Automotive

• Retail / Restaurants

• Hospitals

• mHealth/teleHealth

• Infrastructure


Premise

IoT Solutions Today are Sexy, Self-Contained

AlCloud Based

Central Repository

SensorsRules/

Workflow

Alerts,

Triggers,

Actions

Data:

NoSQL: Hadoop (Cloudera, Hortoworks, MapR), Cassanndra, MongoDB

Analytic: Sybase/IQ, HP Vertica, Amazon Redshift, Infobright, Pivitol

Standard Relational: Postgres, MySQL, Oracle, Sybase, Microsoft

Cloud: Amazon, Rackspace, Dimension

Data, Joyent, Cisco, EMC, IBM, Microsoft

Rules/Workflow: Apache Storm, Tibco Streambase,

Software AG Apama, Sybase Aleri, Various Coded in

(Java, Python, Ruby on Rails), TempoIQ

Closed Loop

Message-Response System


Key Challenge

Added Complexity

Evolve with Simplicity

Centralized Volumes

• Gigabytes to Terabytes

• Terabytes to Petabytes

• Petabytes to Exabytes

• Data Exploitation Demands

• Edge Processing Demands

• Governance, ownership

• Privacy

8

Deliver an IoT Platform that

contemplates enormous

sophistication and complexity in a

delivery model that is intuitive,

accessible, and affordable.”

9

IoT Foundations

Exposing Opportunities


Gaps Exposing the Opportunity

Data

Major Considerations

• How/where to leverage utility value of data

Edge Processing

• Drivers behind and rationale of edge processing both physical

and/or virtual

Architecture

• Meeting market requirements over time; getting it right today

IoT World Forum Reference Architecture

11

Many will overkill to address the

gaps. The result will be sophisticated

yet hardly elegant solutions.”


Viewpoints: Now and Future

Vendors and Users

Sample Industry ViewpointsCurrent Equipment Vendor

Drivers Current IoT User Drivers

Vendor Assumptions about the

FutureUser Assumptions about the Future

Industrial Equipment

(Lutron Lighting / Glidden

Paints)

Better product, higher margins,

differentiation, stickiness

Higher uptime; easier servicing

when needed; better results

Control of the silo, data, and

devices. Customers will want the

value add of accessing the data

Devices supplied by multiple vendors

will work together and the data can be

leveraged

Oil & Gas

(FMC / Chevron)

Better products; safer products;

proactive servicing

Safety; efficiency; visibility; uptime;

compliance readiness

Gain product insight and control

devices; value added services

Integrated IoT devices; holistic view

from rig level up

Automotive

(Ford / you)

Better product info;

maintainability; increased

margins; more competitive

Ease of use; comfort, safety;

entertainment

Increasingly autonomous;

changing models; compliance

Fully integrated experience, “car as

device” including data; insurance;

ownership

Retail & Restaurants

(Viking Commercial /

McDonald’s)

Tracking (Beacons); better

equipment maintenance; higher

uptime; customer stickiness

Higher yields per customer; better

operational information; better

uptime; greater sales

Greater level of integration

required;

anonymization requirements

Leverage various IoT silos to create

operational efficiencies and greater

profits

Hospitals

(Lutron Lighting / Mercy Health

St. Louis)

Higher uptime; greater

efficiencies; enhanced supply

chain

Less shrinkage; better compliance;

greater visibility

Silo control with regulatory

oversight; Integrated product

suites

Exceptional level of integration of

patient data and resources; operational

efficiency

mHealth / Telehealth

(New England BioLabs /

McDonald’s)

“must have devices” for

consumers; highly cost effective

monitoring solutions

Health maintenance; physician

accessibility; reduced costs; better

outcomes

Lower cost delivery; shrinking

footprint – becoming invisible;

Lower energy; multi-point;

integration

Greater exposure to data; integration

with home systems; non-intrusive;

lifestyle insights

Smart City

Infrastructure

(Siemens / City of Chicago)

Specific silos

(lighting/rubbish/streets.

Increased efficiency and reduced

cost of service delivery for various

silos

Increasing footprint and product

suite offerings; Mega vendor

based service led engagements

Coordination and orchestration of

holistic data; lower cost and better

service delivery through analytics


Viewpoints: Now and Future

Vendors and Users

Sample Industry ViewpointsCurrent Equipment Vendor

Drivers Current IoT User Drivers

Vendor Assumptions about the

FutureUser Assumptions about the Future

Industrial Equipment

(Lutron Lighting / Glidden

Paints)

Better product, higher margins,

differentiation, stickiness

Higher uptime; easier servicing

when needed; better results

Control of the silo, data, and

devices. Customers will want the

value add of accessing the data

Devices supplied by multiple vendors

will work together and the data can be

leveraged

Oil & Gas

(FMC / Chevron)

Better products; safer products;

proactive servicing

Safety; efficiency; visibility; uptime;

compliance readiness

Gain product insight and control

devices; value added services

Integrated IoT devices; holistic view

from rig level up

Automotive

(Ford / you)

Better product info;

maintainability; increased

margins; more competitive

Ease of use; comfort, safety;

entertainment

Increasingly autonomous;

changing models; compliance

Fully integrated experience, “car as

device” including data; insurance;

ownership

Retail & Restaurants

(Viking Commercial /

McDonald’s)

Tracking (Beacons); better

equipment maintenance; higher

uptime; customer stickiness

Higher yields per customer; better

operational information; better

uptime; greater sales

Greater level of integration

required;

anonymization requirements

Leverage various IoT silos to create

operational efficiencies and greater

profits

Hospitals

(Lutron Lighting / Mercy Health

St. Louis)

Higher uptime; greater

efficiencies; enhanced supply

chain

Less shrinkage; better compliance;

greater visibility

Silo control with regulatory

oversight; Integrated product

suites

Exceptional level of integration of

patient data and resources; operational

efficiency

mHealth / Telehealth

(New England BioLabs /

McDonald’s)

“must have devices” for

consumers; highly cost effective

monitoring solutions

Health maintenance; physician

accessibility; reduced costs; better

outcomes

Lower cost delivery; shrinking

footprint – becoming invisible;

Lower energy; multi-point;

integration

Greater exposure to data; integration

with home systems; non-intrusive;

lifestyle insights

Smart City

Infrastructure

(Siemens / City of Chicago)

Specific silos

(lighting/rubbish/streets.

Increased efficiency and reduced

cost of service delivery for various

silos

Increasing footprint and product

suite offerings; Mega vendor

based service led engagements

Coordination and orchestration of

holistic data; lower cost and better

service delivery through analytics

Connected Products

System of Systems

14

Deliver an IoT Platform that

accommodates evolving user needs

with minimal user requirements.”


Lens of a Vendor

Need

Considerations

• Product that performs and adaptable

Data Characterization

• Framing view of product by dataData Use

• Predict and Evolve ProductConstituencies

• Understand User Segmentation

Ownership

• Retain rights to Data

Stewardship

• Data Access by usersManagement

• Controlling devices in the field

Drivers

• Decreased downtime, increased utilization and visibility, Upsell

Outlook

• Integration into larger system of systems


Lens of a User

Need

Considerations

• Operate efficiently to gain better insight / make better decisions

Data Characterization

• Products and ServicesData Use

• Holistic understanding on data breadth / avoid silos

Constituencies

• Organization or consumer including various silo systems

Ownership

• Own the data

Stewardship

• Determine user permissionManagement

• Product companies manage assets

Drivers

• Cost savings, enhanced outcomes, increased revenue

Outlook

• Move from Silo to greater system

17

IoT Foundations

Directions to Consider


IoT Direction

Pushing Suppliers for more Robust Analytic Stack

AlCloud Based

Central Repository

SensorsRules/

Workflow

Closed Loop

Message-Response SystemEnterprise Apps:

ERP, CRM, and

other enterprise

apps

Possible Specialized Store

Alerts,

Triggers,

Actions

Analytic Workbench: Operational,

Investigative, Predictive Analytics and

Machine Learning


IoT Direction to the Edge

Increase in Edge Processing for filtering and increased capabilities

Cloud Based

Central RepositorySensorsRules/

Workflow

Closed Loop


Enterprise Apps:

ERP, CRM, and

other enterprise

appsPossible Specialized Store

Alerts,

Triggers,

Actions


Investigative, Predictive Analytics and

Machine Learning

Edge Processor

• Apply rules and workflow against that data

• Take action as needed

• Filter and cleanse the data exhaust (increasing payload)

• Store local data for local use

• Enhance security

• Provide governance admin controls

Rules/

Workflow


Edge Processing Assumptions

• Limited or no human resources for maintaining the database or other system capabilities at the edge – must be a hands off operation, with remote monitoring or

control only

• Hardware footprint will be limited

• Not all use cases apply – but many do

– Factories

– Retail/Restaurants

– Homes (but with far less data)

– Buildings

– Many aspects of smart cities

– Hospitals (but only marginally for personal health)

– Cars (Edge on board)

– Other transportation modalities (especially planes,, trains and ships)

– Oil and Gas


Architectural Considerations

Cloud-Based Central

Repository

Cloud-Based Central

Repository

SensorsRules/

Workflow

(& Filtering)

Sensors

Closed Loop


Edge Processor

Sensors

Sensors

External Data

Persisted Store

Publis

h

Analytic

Workbench:

Operational,

Investigative,

Predictive

ERP, CRM, etc.

Various Sensor Devices

First Receiver

Cloud-Based Central

Repository

Rules/

Workflow

Analytic Workbench

Enterprise Apps

Cloud-Based Central

Repository

Rules/

Workflow

Analytic Workbench

Enterprise Apps

Sensors

Sensors

Sensors

Sensors

Vendor Corporate (“Lutron Lighting”)

One of multiple vendor silos

User Remote Site (“McDonald’s/South Boston”)

User Corporate (“McDonald’s Head Office”)

Government (“USDA”)

Subscribe

Cloud-Based Central

Repository

Rules/

Workflow

Analytic Workbench

Enterprise Apps


Local Back End Data Provisioning

SensorsRules/

Workflow

(& Filtering)

Sensors

Closed Loop


Sensors

Sensors

External Data

Persisted Store

Analytic

Workbench:

Operational

Investigative

Predictive

ERP, CRM, etc.

Various Sensor

Devices & Silos

First Receiver

Cloud-Based Central

Repository

Rules/

Workflow

Analytic Workbench

Enterprise Apps

Cloud-Based Central

Repository

Rules/

Workflow

Analytic Workbench

Enterprise Apps

Cloud-Based Central

Repository

Rules/

Workflow

Analytic Workbench

Enterprise Apps

Sensors

Sensors

Sensors

Sensors

Vendor Corporate (“Lutron Lighting”)

McDonald’s

Vendor Corporate (“Honeywell HVAC”)

Vendor Corporate (“Bosch Appliances”)

23

Data beyond a certain scale becomes

impossible to accommodate and use

without vast infrastructure and

excessive administration.”


Small Edge Node Volumes Today

� Most data volumes today and in the

near future are exceptionally low by

some standards (like Telco and

Networking)

� The key will be to provide the

underpinnings to service the full

analytic stack and feed enterprise

applications

Hotel Example

Sensors Deployed: 100,000

Avg. Message Interval: 5 seconds

Exhaust Rate: 100

Avg. Message size: 3kb

Data Retention Period: 30 days

Required Message Flow Capacity: 2.16M Messages/Hr

Required Storage: 2.59 TB

AlCloud Based

Central Repository

SensorsRules/

Workflow

Closed Loop

Message-Response SystemAlerts,

Triggers,

Actions


Investigative, Predictive Analytics

and Machine Learning

Enterprise Apps:

ERP, CRM, and

other enterprise

apps

Possible

Specialized Store


Future Unknown Edge Node Volumes

� The combination of many silos with greater

reach along with the augmentation with

external data will create much higher

volumes over time, especially in certain

user cases

� The ability to practically accommodate

massive amounts of data in the future

will be a critical consideration of IoT

architectures

Sensors Rules/Workflow

(& Filtering)

Sensors

Closed Loop Message-Response System

Edge Processor

Sensors

Sensors

External Data

Persisted Store

Pu

blis

h

Analytic Workbench:

Operational,

Investigative,

Predictive

ERP, CRM, etc.

Various Sensor Devices First Receiver

Cloud-Based Central RepositoryRules/

Workflow

Analytic Workbench

Enterprise Apps


Workflow


Workflow

Sensors

Sensors

Sensors

Sensors

Vendor Corporate

User Corporate

Third Party – as needed

Su

bscri

be Analytic Workbench

Enterprise Apps

Analytic Workbench

Enterprise Apps


Opportunity for Providers and Users

Increased Data Focus

& Analytic Capabilities

Edge & Tier Processing

wherever appropriate

Publish &

Subscribe Architecture

Leveraging the

Utility Value of

IoT Data

27

IoT Foundations

Moving from Possible to Practical


Metadata Leveraged Architecture

� Establish Metadata at the point

of ingestion

� Provide comprehensive query

tools contemplating a variety of

needs

En

dp

oin

t D

evic

es

1st Receiver

Edge Processors

1st Receiver

Edge Processors

1st Receiver

Edge Processors

Mid-Tier

Edge Processors

Mid-Tier

Edge Processors

Includes

Infobright Store

integrated with

Hadoop for

enhancing

analysis of

machine data

Leverages Metadata throughout the architecture


Metadata Leveraged Architecture

En

dp

oin

t D

evic

es

1st Receiver

Edge Processors

1st Receiver

Edge Processors

1st Receiver

Edge Processors

Mid-Tier

Edge Processors

Mid-Tier

Edge Processors

Includes

Infobright/Metadata

Store integrated

with Hadoop for

enhancing analysis

of machine data

Leverages Metadata throughout the architecture

Common Tool Sets, Minimal Administration

Affordable and Accessible


Gap: Leveraging Data and Analytics

Lack of data leverage and more robust analytic stack will become an increasing

impediment

Gap: Edge Processing

Edge processing is cloud based filtering and workflow for exhaust

Gap: Publish/Subscribe Model

Basic monolithic cloud architectures

Opportunity: Data Cleansed, Enriched, and Published

Analytic stack can be established to provide operational, investigative, predictive,

and machine learning.

Opportunity: Edge Processing / First Receiver

Extensible version of workflow and data cleansing for edge deployments

Opportunity: Event-driven Architecture

Flexible pub/sub architecture for adaptability in demands with constituencies in a

simple, secure, and accessible fashion

Gaps and Opportunities


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edgeprocessing-allseenalliance 4x3 template 24sept2014...erp, crm, and other enterprise apps...

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