Cloud Computing and Intelligent Systems: Two Fields at a Crossroads

Infinite Dimensions Solving Tomorrow’s Problems TodayCSCI'14
Cloud Computing and
Intelligent Systems
Two Fields at a Crossroads
Dr. Jeffrey Wallace
The 2014 International Conference on Computational
Science and Computational Intelligence (CSCI'14)

Cloud Computing and Intelligent
Systems
• Context
• Technical Challenges
• Examples
– Unmanned Systems Control
• Anti-ship Missile Defense
• Battlefield Extraction of Wounded
• Technology Enablers
– System/Component Integration
– Algorithm/Environment Integration
• Summary 2

Context
• Unique Systems Engineering and Development
Capability
– A distillation of over $2B in government R&D
– Addresses both people and technology
• Partnered with the Office of the Secretary of
Defense, the Joint Chiefs of Staff, the 4 services,
Academia, and others
• Solved world-class/grand challenge problems on
F-35, CVN-21, healthcare IT, etc.
• Systems Integrator for the Continuous
Transformation Environment
3

Infinite Dimensions Solving Tomorrow’s Problems Today
Continuous Transformation
Environment
“A Cloud Computing
Range”

CTE at a Glance
• CTE Objectives
– Prototype development and experimentation: Innovation in a
collaborative environment by industry, government, and
academia in an open systems collaborative environment
– Integration, verification, test, and release of Commercial-Off-The-
Shelf (COTS) products for government use
– Rapidly prove operational utility of high technology solutions
– Open systems and standards compliance evaluation,
documentation, and capabilities matrix
• Solve GAO identified big integrator problem1
– Organizing principle: “Give innovation a chance.”
• Consortium of large and small technology companies
and facilities partners
5
1 Government Accounting Office-09-326SP, http://www.gao.gov/new.items/d09326sp.pdf

CTE Network
6
 Quality Technology Services (QTS) and Verizon (VZW) are the current facilities
 OCONUS Sites are VZW
 CONUS VZW: Engelwood, CO; Culpeper, VA, Miami, FL
 Remaining CONUS Sites are QTS, in particular the 1.3M sq. ft. Richmond, VA s

Main CTE Experimentation Lab Location
Richmond- High Density Multi-Data Center
Campus
500,000 Sq Ft of Planned Raised Floor
Multiple Distinct Data Center Buildings
(Current Basis of Design)
1
3
2 Office Space
1.3 Million Sq Ft Campus

Technical Challenges
• Rapidly create complex, realistic, and scalable
networks of systems and component inter-
relationships
• Distribution of autonomous controls and monitors
• Implementation of complex webs of cause and effect
• Dynamic alteration of the component execution
structure
– Adaptation and evolution of the system
• Ability to handle billions of active processes in real-
time
– Harness power of sequential, distributed and/or parallel
processing – optimizing the use of any
compute/network/storage configuration
– Smartphones to supercomputers
8

Unmanned Systems Control
October 27, 2008 USIC Conference, San Diego,
CA
9

Hypersonic Anti-Ship Missile
Challenges
• Critical Battlespace defense gap
• Interactions happening faster than humans
can react
• Current Command and Control (C2) is not
real-time, performance limited, and the
only source of information
• Greatly affects real-time response for
kinetic engagement capabilities
10

Anti-Ship Missile
Defense
 Increase Response time for Fleet
 Simplify Installation, Maintenance, &
Operation
SBX
DDG
e.g. DF-21
UAS
Weapon
USV
Anti-
Ship
Missile
Space
Sensor

12
Sea Base
Perimeter
ASW Threat
Identification
Perimeter
SUW Threat
Monitoring
Perimeter
SUW Threat
Identification
Perimeter
18 nmi
ASMD Threat
Perimeter

13
Sea Base
Perimeter
ASW Threat
Identification
Perimeter
SUW Threat
Monitoring
Perimeter
SUW Threat
Identification
Perimeter
ASMD Threat Perimeter

Cloud of Projectiles
Each USV’s Gun Creates a Cloud
14

New Fleet Capabilities
• Automated Fleet Systems
– Adaptive & composable
– Knowledge built into systems
– Emphasizes speed and flexibility
• Ability to fuse new information, knowledge,
and structures rapidly
– Mesh networks and systems
– Example: Sensor net that automatically refocuses
based on accurate real time fused information
• C2 and fire control moves from Fleet
platforms to Network
15

Inter-system Interoperability and Interaction with Personnel “On
Scene” Example: Nightingale II
16
Autonomous transit from starting point,
to pick-up point, to medical unit
4
A. Call for
MedEvac
received at
Nightingale
Control
B. Best UAV is
chosen
automatically
C. Route is
autonomously
planned &
uploaded
D. UAV is
launched
automatically
1
2
Autonomous collision &
obstacle avoidance
Similar process for: Logistics, Combat Rescue, & Special Ops
No Fly
Zone
C2
No Fly
Zone
A
B
C
D
1
Autonomous
Clear-Zone
landing
3
5
UGV+BEAR
deploys
BEAR
deploys
6 BEAR
recovers &
Medic treats
7
UAS/UGV/BEAR
system rejoins and
goes to destination

Nightingale II
• Autonomous:
– VTOL UAV
– High-mobility UGV(s)
– Asset allocation and mission planning
• Interoperability / coordination with existing
– Dismounted ground personnel
– Air operations
– Ground operations
– Artillery & strike operations
– Political & no-fly boundaries
– High-data-rate non-LOS communications
October 27, 2008USIC Conference, San Diego, CA 1
7

Nightingale II
Challenges
• Autonomous VTOL UAV
– Autonomous obstacle avoidance
• Wires, antennae, etc.
– Sensor with sufficient resolution & range for
vehicle maneuverability limits
– Day, night, weather, dust/sand/dirt (“brown
out”)
– Autonomous collision avoidance
(other aircraft)
• Small UAVs, birds, etc.
– Sensor with sufficient resolution & range for
vehicle maneuverability limits
– Day, night, weather, dust/sand/dirt (“brown
out”)
October 27, 2008 USIC Conference, San Diego, CA 1
8

Nightingale II
Challenges
• Autonomous VTOL UAV (cont)
– Autonomous LZ identification
• LZ size, geometry, roughness, slope
• Ingress/egress flight-path
– Obstacles
– Moving ground personnel, vehicles
– Exposure to enemy
» Lines of fire
» Exposure time
• Coordination with UGV mobility constraints
– Navigable path between LZ and casualty
October 27, 2008 USIC Conference, San Diego, CA 1
9

2
0
Nightingale II
Challenges
• Rough-terrain UGV(s)
– Mobility
• Tracks, wheels, legs
• Water crossing
• Mud, sand, snow, ice
• Interiors (stairs, doors, elevators, etc.)
– Autonomous capabilities
• Beyond Grand Challenge, Urban Challenge
– Casualty extraction
• Careful casualty handling
• Does the UGV need a UGV?
October 27, 2008 USIC Conference, San Diego, CA

System/Component Integration
• Most well-known methods and technology
originate in “business IT”…
• Optimizing execution and efficiency and
enabling computation at scale is typically
the province of “high performance
computing”
• Real-time execution and synchronization
are addressed by several communities,
e.g. robotics
21

Ease of Development/Integration/Interoperability
Implement/Support Any Architecture
Application
Connectors
Message
Brokers
Enterprise
Resource Managers
Application
Servers
Component
Brokers

Data
Source 1
Application 1
Compute
Process 1
Compute
Process M
Data
Source 2
Data
Source N
Application X
WAIT_FOR (DS1 semaphore, wait time1)
WAIT_FOR (DSP semaphore, wait timep)
Typical Complex Application-
System

The DNA of Complex Systems
• Look at nature to understand
complex systems
• Internal Processes
• External Processes
• Internal Events
• External Events
• Intermix of all four is required
–Implementing in a scalable manner is key

Internal Processes
Analogy: The Heart Beat
• Atria pump blood to
ventricles, which
contract
• Nonstop contractions
are driven by the heart's
electrical system
Internal Process: Synchronous or Asynchronous –
Intrincsic Capabilities

External Processes
Analogy: Pacemaker
• External process monitors
and interacts with an
object (i.e., a pacemaker
monitors the heart’s
rhythm)
• The electric current makes
the heart beat within a
certain range
External Process: Synchronous or Asynchronous –
Monitor and Control

Internal Events
Analogy: Heart Attack
• Internal occurrence without
pre-established time scale
• Certain factors cause the
occurrence. Blood flow is
restricted, or the nerve
system, which controls the
heart, malfunctions
Internal Occurrence: Irregular Time Scale – Intrinsic
Capabilities

External Events
Analogy: Defibrillation
• External event changes a
passive object’s state (i.e., a
defibrillator is used for
resuscitation)
• External electrical shock is
applied to the heart
• Foundational representation
method
External Occurrence: Irregular Time Scale – Monitor and
Control

FE FE
FE FE
FE
FE
FE
FE
FE FE
FE
FE FE FE FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
FE FE
User Application
Interface Services
Hardware Device
Interface Services
3D Visualization
Interface Services
Web Interface
Interface Services
Communication
Speed
Slow
Medium
Fast
Configurable Computational Mesh
Polymorphic Computing Architecture (PCA)
FE = Functional Element
29

Component Repository
Composability Automation
CASE Tool Environment
User Defined
IT System
Interface
User Defined
Hardware
Interface
Web Services
API
(JNI, SOAP, OWL, etc.)
Shared
Memory
IP JTRS
Reflective
Memory
Security State Saving Core Programming
Distributed
Object Mgmt
Std App Dev
Interface
Synchronization
Management
Event Management
Services
Knowledge
Representation
Integration Meta-Data
Data
Translation
Communication Services (Unicast, Multicast, Broadcast)
Common Application Services
Intelligent Application Services
System Execution Services
Service Decomposition
CompressionEncryption
BLOSLink-16 Others
30

API Example
Turret/Fire Control
Slew Elevate
Fire When Slew and Elevate are Complete
Process Firing Commands (and Queuing Them)
31

Example: Turret Fire Command
void Turret::fire()
{
P_VAR
P_BEGIN(2)
// Wait until the turret movement is completed
WAIT_FOR(1, slewComplete, -1);
WAIT_FOR(2, elevateComplete, -1);
// Fire the weapon, this would activate the real gun
Fire_M256();
RB_cout << "Flash, Boom, Bang, Echo" << endl;
fireComplete = 1;
P_END
}
32

Algorithm/Environment
Integration
• Must be able to integrate arbitrary types of
models (e.g. rule-based, network-based,
fuzzy, combinations, etc.)
• A system must account for functionally
disparate phenomena, in order to
represent “intelligence” more effectively,
for example:
– Recognition of patterns
– Adapting new solutions or strategies
– Rule following

Adaptive, Dynamic, Knowledge-based
Execution/Control
Conceptual Graphs
Computational Ontology Framework
ConceptsConcepts ActorsActorsRelationshipsRelationships
CAT STAT LOCSIT MATCAT STAT LOCSIT MATMAT
A Cat sits on a matA Cat sits on a mat Une Chat assis sur
une matte
Une Chat assis sur
une matte

Basic CG Formation Rules
Concept 1
Relationship 1
Relationship 1
Concept 2
Concepts or Other Actors
Actor 1
Actors or Relationships
Actors or Relationships
Concept 1
Concept
Concept or other Actors
Concept 1
Relationship 1
Actor 1
INVALID

Extensions
• A CG execution engine with abilities to:
– Embed in a hardware controller or a software
program
– Associate the software and hardware components as
indicated by the CG structure
– Control, execute, and integrate the entire system
• Customization of concepts, relationships, and
actors by the integrator, providing capability to:
– Accommodate new hardware, or software
components, without returning to original developers
for new versions of graph systems

Extensions
• A mechanism to control the CG execution
engine
– Each node maintains a truth value to
managed graph execution
– A concept from belief network theory
• Collectively, the extensions embodied in
the system enable intelligent automation,
execution, and control of complex
hardware and software assemblies

Summary of Extensions
• A collection of user-defined concept nodes
• A collection of user-defined relationship nodes
• A collection of user-defined actor nodes
• A unique ID numbering scheme to identify every node, regardless of
type
• A description of the connected nodes, and route of the connection
• A list of references to input concept nodes (those with no incoming
arcs)
– A valid CG must contain at least one input concept node
• A list of references to the output concept nodes (those with no
outgoing arcs)
– A valid CG must contain at least one output concept node
• A data structure that records the truth value of each node

Paradigm of Execution
1. The system is started
a. Each component in the system is initialized
b. Synchronization relationships are established
c. Inputs are read and loaded into system components
2. The CG associated with the system is initialized and parsed
a. Each system component is associated with a CG element using the unique ID
tag (concept, relationship, actor)
b. Each component is registered in the correct collection mechanism for each type,
using the unique ID tag
3. The system begins operating, with various evolution methods. For
example:
a. A user inputs information, which changes the state of the system (either event-
based, process-based, or simple update loop)
b. Regular update cycles occur for various system aspects
4. The system operation mechanism executes, and an Execution Cycle
operation for the CG is activated
5. Each time a system operation mechanism is activated, step 4 is repeated

CG Execution
Dividend: 9.0
Divisor: 4.0
Number: 144.0
Quotient: 2.0
Remainder: 1.0
SquareRoot: 12.0
Sum: 14.0
divide plus
sqrt
Dividend: 9
Divisor: 4
Number: 144
Quotient: 2
Remainder: 1
SquareRoot: 12
Sum: *s
divide plusplus
sqrt
IF ?r = 0IF ?r = 0
T
TT
T
TT
TT
T
F

Example of a Path of Route
in a CG
runs
Is a
generating
for
Responsible
for
of Producing
A
Machine
An
Interpreter
Algorithm
An
Interpretation
An Agent
A Graph A Behavior

Summary
• Representation of complex webs of
synchronized causes and effects is central
to the implementation of complex systems
• Computation, correlation of simultaneously
evolving systems and interrelated
phenomena
• Ability to control an activity based on a
web of logic, and start another in response
to dynamic conditions
• Achievement of scalability without loss of
capability 42

Backup
43

Software Development Framework: JEE
• Minimizes amount of code, people, skill-level,
development, test, integration, and time
required
• Standardizes processes, templates, and
documentation
• Works with all modern languages and
standard Application Programming Interfaces
(APIs)
• Does not compromise any other software
vendor’s intellectual property – typically a key
cost, schedule, and performance driver
• Can be taught to all development team
members in an extremely short period of time
(< 1 to 2 days)
11

Example: Building Automation
Management System
Level 1
Information
management
system
Level 2
Information processing
And supervisory system
Level 3
Information processing/
Automation system
Level 4
Process field

Components in a BAMS
fan
Air conditioning
PC
Heat energy
sanitary
lighting
Waste-
managementelectricalacoustics
video
safety
elevator
Emergency power
supply
transformer
heating
Building
700

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