Imagine that this is the biggest supercomputer in the world...
...and this is how you control it
How We Use The Internet Now
Like drinking the sea through a
straw
Combinatorics• The IoT creates an impossible task: Finding,
collecting and analyzing data in real time from a large number of devices
• There are n! ways to combine n devices.
IoT Device Growth• It is estimated that there were 12B devices
shipped in 2013 and that there will be at least 40B devices in 2025
Growth of Computing Power
• Moore’s law states that computing power increases in speed by a factor of 2 every 2 years
Combinatorics Beats Moore’s Law
What This Means• Even with huge data centers and Moore’s
Law, analytics can’t locate, gather, and analyze the volume of data that’s coming
Fog Computing• Fog Computing pushes computing out to the
edge of the Internet, such as in cars that analyze what’s happening around them
Fog Lifter: Compute Locally, Analyze Globally
• Organizes local, dynamic, distributed computing
• Designed for intermittent connectivity
• Processes data locally and makes results available globally
• Data that reaches data centers will be processed multiple times (vertically distributed analytics)
Fog Lifter Platform• Functional Relational Programming
• F-code compiler and evaluator
• Relational rules and constraint checker
• P2P architecture at the Edge
• Work Flow Description
• Data Registry
• Security and Privacy
F-Code Is Portable Code For Fog Computing
• A type of p-code: Functional code that can be executed on any platform, like Java or Python
• Why functional code? It enables parallel processing in the Fog
• Each expression can be independently evaluated with no change in result
F-Code Uses Combinators• S, K, I, B, C, Y
• S f g x –> f x (g x) // distributes expression x into expressions f an g
• K f g –> f // selects f from f g expression
• I x –> x // Identity
• B f g x –> f (g x) // re-distribute evaluation
• C f g x –> f x g // re-order evaluation
• Y x –> x (Y x) // recursion
F-code Compiler• Can compile any pure functional language
program into F-code
• Programs are compiled to combinator expressions
• Expressions can be distributed across devices and results safely recombined
Y (B (S (C B ? (= 0)) 1) (B (S *) (C B (C - 1))))
Relational Programming
• Integrating data from many sources requires careful coding
• Functional Relational Programming (FRP) uses relational algebra to constrain unintended complexity of functions
• Reduces chance of errors
• FRP already in use in large scale analytics
Peer-to-Peer Connectivity
• Supports dynamic environment since edge devices come and go
• Devices share data and computation
• Results can be part of larger computation
Tex
t
E2E1 E3
E4E5
Work Flow Design• Maps data flow and computation across the
Internet in order to leverage parallel processing
• Data centers will analyze results of edge computing rather transferring terabytes of data
Enterprise Data Workflows with Cascading O’Reilly (2013)
Data Registry• Provides semantic description of the data
• Also contains data dictionary
• Provides information about computed results and optionally raw data
• Conforms to relational model
Security and Privacy• Data and results must be
secure from hacking by building in heavy encryption
• Control of data must reside with owner of the data or basic trust is missing
• Permission must be an act of commission, not omission
When Is Fog Lifter Most Useful?
• When analyzing high volume of data from many different sources
• When local result is needed quickly from surrounding environment
• When there is intermittent or low-bandwidth connectivity
• When the same computations are used for multiple purposes
Example: Smart Traffic
Car
Car Car Car
Car
Car
Car
Car
Car
Cars plot route from interactive
algorithm
SmartRoad
SmartRoad
SmartRoad
SmartRoad
Roads trackcar flow
Traffic controlintegrates routes
and flow
City planners design infrastructure
changes
Car
Example: Local Smart Grid
Aggregates data to predict power demand based on conditions such as weather, current demand, sources, and past behavior. This allows development of local power coop with dynamic load balancing using local storage and interfacing with smart grid.
Smart House
PV eCar Controls
Smart House
PV eCar Controls
Smart House
PV eCar Controls
Smart Grid
Example: Shopping
SmartPhone
SmartPhone
SmartPhone
SmartPhone
SmartPhone
Shopping Mall
Store1
Store2
Store3
Store4
• Picture processing is distributed among phones• Stores send images of similar products• Results and locations are displayed• Stores track product queries, improving inventory control
Example: Home Healthcare
• Integrate health factors over time•Generate health
metric•Upload results of
analysis to health record•Alert user and MD
of health problems
Heart Rate
Glucose
VascularHealth
BloodPressure
Exercise
Thera-peutics
Example: FarmingVertical Aggregation
Farm Field Sensorseg salinization
Farm Equipmenteg tractor
Data Harvesterseg aerostats
Farm Data Center
Farm Coop Farm 1 Farm 2 Farm 3 Farm 4
Region Crop Insurance Markets EquipmentSuppliers/Hire
Local Distribution/CSAs
Example: Farming Horizontal Aggregation
Water Usage Patterns
Weather/Field Dynamics
Pest Dynamics Yield Projections
Water Use Planning Ag Market AnalysisInsurance
CompaniesNGOs
Fog Lifter Summary
• For Lifter changes the Fog from a collection of devices to a dynamic computing system
• FRP provides a common language with error control
• Work flow design maps computation using locations described by Registry
• Security and Privacy controls increases safety and confidence of users
The Sea Comes To ShoreFog Lifter allows the Internet to become part of all data centers
Fog Lifter• The first components of Fog Lifter will be
available in 2015
• For more information, contact Bill Worzel at [email protected] or call 734-276-9333
™