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Fog Lifter Summary from CES

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Describes an architecture for doing distributed computing in the Internet of Things (IoT) and integrates it with the larger Internet.

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? Fog Lifter Bill Worzel CEO, Fog Lifter Advisor, Kwaai Oak billw@fog-lifter.com bill@kwaaioak.com
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 C> f x (g x) // distributes expression x into expressions f an g ? K f g C> f // selects f from f g expression ? I x C> x // Identity ? B f g x C> f (g x) // re-distribute evaluation ? C f g x C> f x g // re-order evaluation ? Y x C> 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 ?ow 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 DataWork?ows 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 Traf?c Car Car Car Car CarCarCar CarCar Cars plot route from interactive algorithm Smart Road Smart Road Smart Road Smart Road Roads track car ?ow Traf?c control integrates routes and ?ow 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 Smart Phone Smart Phone Smart Phone Smart Phone Smart Phone 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 Vascular Health Blood Pressure Exercise Thera- peutics
Example: Farming Vertical Aggregation Farm Field Sensors eg salinization Farm Equipment eg tractor Data Harvesters eg aerostats Farm Data Center Farm Coop Farm 1 Farm 2 Farm 3 Farm 4 Region Crop Insurance Markets Equipment Suppliers/Hire Local Distribution/ CSAs
Example: Farming Horizontal Aggregation Water Usage Patterns Weather/Field Dynamics Pest Dynamics Yield Projections Water Use Planning Ag Market Analysis Insurance Companies NGOs
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 ?ow design maps computation using locations described by Registry ? Security and Privacy controls increases safety and con?dence of users
The Sea Comes To Shore Fog Lifter allows the Internet to become part of all data centers
Fog Lifter ? The ?rst components of Fog Lifter will be available in 2015 ? For more information, contact Bill Worzel at billw@fog-lifter.com or call 734-276-9333 ?

More Related Content

Fog Lifter Summary from CES

  • 1. ? Fog Lifter Bill Worzel CEO, Fog Lifter Advisor, Kwaai Oak billw@fog-lifter.com bill@kwaaioak.com
  • 2. Imagine that this is the biggest supercomputer in the world...
  • 3. ...and this is how you control it
  • 4. How We Use The Internet Now Like drinking the sea through a straw
  • 5. 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.
  • 6. 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
  • 7. Growth of Computing Power ? Moore¨s law states that computing power increases in speed by a factor of 2 every 2 years
  • 9. 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
  • 10. Fog Computing ? Fog Computing pushes computing out to the edge of the Internet, such as in cars that analyze what¨s happening around them
  • 11. 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)
  • 12. 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
  • 13. 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
  • 14. F-Code Uses Combinators ? S, K, I, B, C,Y ? S f g x C> f x (g x) // distributes expression x into expressions f an g ? K f g C> f // selects f from f g expression ? I x C> x // Identity ? B f g x C> f (g x) // re-distribute evaluation ? C f g x C> f x g // re-order evaluation ? Y x C> x (Y x) // recursion
  • 15. 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))))
  • 16. 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
  • 17. 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
  • 18. Work Flow Design ? Maps data ?ow 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 DataWork?ows with Cascading O¨Reilly (2013)
  • 19. 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
  • 20. 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
  • 21. 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
  • 22. Example: Smart Traf?c Car Car Car Car CarCarCar CarCar Cars plot route from interactive algorithm Smart Road Smart Road Smart Road Smart Road Roads track car ?ow Traf?c control integrates routes and ?ow City planners design infrastructure changes Car
  • 23. 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
  • 24. Example: Shopping Smart Phone Smart Phone Smart Phone Smart Phone Smart Phone 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
  • 25. 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 Vascular Health Blood Pressure Exercise Thera- peutics
  • 26. Example: Farming Vertical Aggregation Farm Field Sensors eg salinization Farm Equipment eg tractor Data Harvesters eg aerostats Farm Data Center Farm Coop Farm 1 Farm 2 Farm 3 Farm 4 Region Crop Insurance Markets Equipment Suppliers/Hire Local Distribution/ CSAs
  • 27. Example: Farming Horizontal Aggregation Water Usage Patterns Weather/Field Dynamics Pest Dynamics Yield Projections Water Use Planning Ag Market Analysis Insurance Companies NGOs
  • 28. 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 ?ow design maps computation using locations described by Registry ? Security and Privacy controls increases safety and con?dence of users
  • 29. The Sea Comes To Shore Fog Lifter allows the Internet to become part of all data centers
  • 30. Fog Lifter ? The ?rst components of Fog Lifter will be available in 2015 ? For more information, contact Bill Worzel at billw@fog-lifter.com or call 734-276-9333 ?