fog in support of emerging iot applications · rodolfo milito –fog computing conference/expo...

Fog in Support of Emerging IoT Applications

Rodolfo Milito, PhD

Cisco Chief Technology and Architecture Office

Fog Computing Conference and Expo, November 2014, San José, CA

2 Rodolfo Milito – Fog Computing Conference/Expo – November 2014 – San José CA

Agenda

o Fog is a vehicle for IoT

• We examine two IoT spaces: consumer & enterprise • Not all sensors are born equal

• Neither are the associated use cases and apps

o Enterprise-based IoT is not a trivial extension of consumer-IoT • A Canonical example: Smart Traffic Light Systems

• Windmill Farm

• Smart & Connected Communities (S+CC)

• HealthCare

• When seconds matter: Earthquake detection


Whenever possible, Cloud is the answer

Cloud Computing efficiencies are unbeatable - Economies of scale (CAPEX, OPEX) - Elasticity - …

Then, why Fog Computing?

Fog Computing complements & interplays with the Cloud to support apps that require low/predictable latency, rapid mobility, and are widely distributed geographically


Closer examination of the Internet of Things required


IoT Explosive Growth

Source: Cisco IBSG projections, UN Economic & Social Affairs http://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf

6.307

6.721 6.894 7.347 7.83

0

10

20

30

40

50

2003 2008 2010 2015 2020

Bill

ions

of

Dev

ices

World Population

50 Billion SmartObjects Rapid adoption rate of digital infrastructure

5 x faster than electricity & telephony

“~6 things online” per person

Cross-over Point


IoT Billions & billions of sensors

But not all sensors are born equal


Sensors: from wimpy to powerful

power

size

data rate

Distributed Temperature Sensor (DTS) Distributed Acoustic Sensor (DAS) O&G, seismic monitoring, vehicular traffic monitoring

Size Power Data rate [Mbps]

mote In iinches Runs on AA bateries, or solar

DAS Up to 50 Km 600

Other dimensions: cost, processing power, memory

motes

DAS, DTS


A Simple & Successful Model (in its own space) Compelling simplicity, but “Make things as simple as possible, not simpler”

endpoint devices


Limitations of the model

• Ignores feedback decision loops (fdl) o Some fdl operate in very slow time scales o Vehicular traffic patterns, power usage, etc. that feed long-term planning decisions

o Some fdl require near-real time decisions o Customer behavior in retail

o Some fdl demand real-time physical actuation o Elevator control in response to emergency seismic warning, collision prevention systems,

smart grid controllers

• No account for Sensors and actuators organized as systems


Required for feedback

decision loops

Near and real-time control loops require

proximity to the source

Geo-distributed

systems interact E-W

Missing Arrows


Organizing Principles

• Architectural Decisions are constrained and guided by

o Physical Laws

o Intrinsic characteristics of system under consideration

o Technological innovations

o Economic considerations

o Business & human drivers

Strong interplay

Case in point: Centralization vs. Distribution Mainframes Laptops Cloud

Ouroboros virtuous circle Innovation enables apps Apps drive innovation


Assisted living, Smart Homes

IoE, Arguably the Largest Human Deployed System after the Internet

Smart Phones Apps

Industrial/enterprise based

Use Cases

Fresh space

Built on pre-existing systems

SG, S+CC, SCV, Manufacturing

Greenfield Processes and methodologies developed over decades,

centuries, millennia


Assisted living, Smart Homes

IoE, Arguably the Largest Human Deployed System after the Internet

Smart Phones Apps

Industrial/enterprise based

Use Cases

Greenfield Processes and methodologies developed over decades,

centuries, millennia

User-based IoE Industrial-based IoE


Need to incorporate an Organic understanding of IoT Verticals

Consumer-based IoT models do not carry well into industrial-based IoT


IoT Disruption: Beyond Connectivity

Today’s Dominant Endpoints

Dominant Endpoints in 2025

Industrial Automation

Healthcare

Intelligent Buildings

Precision Agriculture Transportation and Connected Vehicles

A person behind every device Devices organized as systems

Smart Grid


o System-wide view rather than individual endpoint view required – e.g. Smart Traffic Light System (STLS), will talk about it

o Machine != Human behavior – M2M chatter

o The Internet meets the physical world

• Deterministic/predictable latency – closed-loops

• New (physical) dimension of threats

o Support for rapid mobility – Connected Vehicle (CV), Connected Rail (CR)

o Managing Large-scale Geo-distributed Systems – pipelines, Smart Grid (SG)

o From consumer-oriented to enterprise operational technologies

• Ecology of domain-expert partners needed

Key Points regarding Industrial-based IoT (I)


o Orchestration and Policy Creation • Networking

• Resource Orchestration

• Orchestration and Control Laws of Physical Devices

o Security • (-) Extended attack surface, new vectors

• (-) Potential for physical damage (Connected Car, insulin pump, etc.)

• (+) Limited set of interactions built-in behavioral detector

o Data Management & Analytics

• Massive number of geo-distributed sources

• Move processing to the data

• Real Time (RT) or NRT analytics

• Data-in-Motion

• Interplay with the Cloud

Key Points regarding Industrial-based IoT (II)

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Smart Traffic Light System


Canonical Example: Smart Traffic Light System (STLS)

o Traffic Light at Intersection • Goal: accident prevention

- Detects pedestrians/cyclist crossing

- Measures distance & speed of approaching vehicles

- Collision likely?

- Issues alarms to approaching vehicles, changes from Green to Red, takes photos & Issues ticket

o STLS as a system • Goal: facilitate traffic flow throughout city/region

- Traffic congestion maps route recommendations (no loops!)

- Cycle coordination of individual intersections green waves

- Faster clearing of traffic accidents (automated dispatch, rerouting, …)

• Goal: Safety

- Green wave for emergency vehicles, emergency evacuation routes

• Goal: Security

- Coordinated surveillance cameras (Amber alerts, etc.)


• Local subsystem (traffic light, sensors, actuators) - Reaction time < 10 msec

- Compute/storage capabilities; ruggedized small form factor box

• Global system - Wide geo-distribution

- Middleware orchestration

- Multiplicity of agencies running the system (must coordinate control policies)

• Interaction with Cloud/DC - Efficient traffic management demands

Data base of historical records

Near real time data on utilities work, street repairs, garbage collection

Requirements Derived from STLS

Enter Fog COMPUTING Mobility

Geo-distribution

Low/predictable latency

Multi-agent orchestration

Semi-autonomy

Big Data & Analytics Support for Service Exchange

RT Analytics at the Edge

Interplay with Cloud

x x x x x x x x


Requirements Derived from STLS • Local subsystem (traffic light, sensors, actuators)

- Reaction time < 10 msec

- Compute/storage capability

- Ruggedized & small form factor box

• Global system

- Wide geo-distribution

- Middleware orchestration

- Multiplicity of agencies running the system (must coordinate control policies)

• Interaction with Cloud/DC

- Efficient traffic management demands large data base of historical records Enter Fog COMPUTING


Windmill Farm


A Perspective on Size

Credit: Pao and Johnson



Region1: power < losses shut-off

Region 2: normal operating Condition

Region 3: shut-off for safety



Control of an Individual Turbine


Characteristics of the Windmill Farm System o Tight interaction between sensors and actuators in closed-loop control loops (local controllers in the turbine)

o Wide geographical deployment of semi-autonomous modules (turbines) that required coordination (prevention of wind starvation of turbines in the rear)

o Strong interplay with the Cloud Real-time analytics at the edge

Batch analytics in the Cloud

Business models in the Cloud

o Multiplicity of time scales Long-term planning (BI, Cloud)

Daily negotiation with Independent System Operators (ISOs) (bidding, commit)

Hourly renegotiations to adjust commitments to operating conditions

5 min interval optimization (farm level)

Local real time control of individual turbines


weather data

weather records

daily weather forecast

negotiation with ISO

farm optimization turbines output hourly forecast

Adj

ustm

ent/r

eneg

otia

tion

CLOUD

FOG


Smart & Connected Communities (S+CC)


A City as a Living Organism

transportation

STLS

emergency services

smart buildings

utility services

smart grid

police

hospitals

fire department

environmental monitoring

parks irrigation

recycling and garbage collection

Multiplicity of agents Interactions & dependencies Missed opportunities for lack of sharing


Consolidation of Silos into a Coherent Infrastructure

First phase:

Reduce of CAPEX, OPEX, and cluttering Second phase:

Enable controlled sharing of information between agencies Third phase:

Facilitate service creation – government agencies, providers of services, citizens


Closing Message

Fog is the vehicle for enterprise/industrial-based IoT

IoT/IoE is disruptive

Yes, the explosion in connectivity is an issue, but note With IoT the Internet meets the physical world (mind the actuators) There are two complementary IoT app spaces: consumer and enterprise In enterprise-based IoT endpoints are organized and managed as integrated system A pure Cloud play does not cut it in many use cases, including the ones outlined

Thank you.

fog in support of emerging iot applications · rodolfo milito –fog computing conference/expo...

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