Post on 19-Jun-2020
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
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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
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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
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Closer examination of the Internet of Things required
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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
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IoT Billions & billions of sensors
But not all sensors are born equal
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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
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A Simple & Successful Model (in its own space) Compelling simplicity, but “Make things as simple as possible, not simpler”
endpoint devices
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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
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Required for feedback
decision loops
Near and real-time control loops require
proximity to the source
Geo-distributed
systems interact E-W
Missing Arrows
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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
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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
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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
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Need to incorporate an Organic understanding of IoT Verticals
Consumer-based IoT models do not carry well into industrial-based IoT
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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
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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)
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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
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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.)
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• 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
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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
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Windmill Farm
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A Perspective on Size
Credit: Pao and Johnson
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Credit: Pao and Johnson
Region1: power < losses shut-off
Region 2: normal operating Condition
Region 3: shut-off for safety
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Credit: Pao and Johnson
Control of an Individual Turbine
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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
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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
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Smart & Connected Communities (S+CC)
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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
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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
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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.