Fog Lifter Summary from CES

™Fog LifterBill Worzel

CEO, Fog LifterAdvisor, Kwaai Oak

[email protected] [email protected]

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

™