Transcript Introduction to Wavelets and Wavelet Transforms

Survivability of Large Scale
Networks and Design Research
NSF-EXCITED Workshop
February 28, 2005
Soundar R.T. Kumara
Distinguished Professor of Industrial and Manufacturing
Engineering
The Pennsylvania State University
University Park, PA 16802
[email protected]
CYBER DESIGN NET(CD-NET)
Retrieval
Agent
Retrieval
Agent
Retrieval
Agent
Design
Agent 3
Design
Agent 2
Design
Agent 4
Coordinator
Agent
Design
Agent 1
Design
Agent n
Retrieval
Agent
Customization
Agent
Retrieval
Agent
Company B
Agent
Company A
Agent
Repository
Agent
Design
Agent A
Design
Agent B
Agent based Design Network
Univ. Agent
Idaho
MIT
BU
PSU
SU
UM
NIST
VT
NASA
GT
Web service
Repository / Digital Libraries
Agent based Design
Retrieval
Agent
Design
Agent 1
Design
Agent 2
Customization
Agent
Functional
Requirements
Retrieval
Agent
Coordinator
Agent
Design
Repository
Design
Agent n
Customer
Needs
Design
Repository
Retrieval
Agent
New
Product
Product
Analysis
NSF-ITR : An Information Management Infrastructure for Product Family Planning and Mass Customization,
PI: Timothy W. Simpson (PSU), Co-PIs: Soundar R.T. Kumara (PSU),
S.B. Shooter (Bucknell), J.P. Terpenny (Virginia Tech), R.B. Stone (U. Missouri-Rolla),
August 2003 – July 2006
Logistics Network
Agent Based Logistics
Network
General Motors: Development of Wireless based Automatic Deployment and Load Makeup System
PI: Soundar R. T. Kumara (PSU). (January 2001 – current)
Sensor Networks
NSF SST : Self-Supporting Wireless Sensor Networks for In-Process and In-Service Integrity Monitoring Using High EnergyHarvesting Nonlinear Modeling Principles. PI: Soundar R. T. Kumara (PSU) Collaborators: S. Bukkapatnam (Oklahoma State),
S.G. Kim (MIT) and X. Zhang (UC Berkely) (September 2004 – August 2007);
Marine Corps: Integrated Diagnostics: Soundar Kumara and Barney Grimes
Military Logistics (UltraLog)
•Secure
against cyber
attack
•Robust
against damage
•Scalable
to wartime data
loads
UltraLog: Extremely survivable net-centric logistics
information systems for the modern battlefield
DARPA - ULTRALOG : Chaos, Situation Extraction, and Control: A Novel Integrated Approach to Robust and Scalable Cognitive
Agent Design
PI: Soundar R. T. Kumara (PSU) (Jan. 2001 to July 2005)
UltraLog Challenges (PSU)
 Situation Identification
 Performance Estimation
 Adaptive Control
 Hierarchical Control
 Robustness
 Infrastructure level
 Application level
 Network Survivability
 Security
Methodologies
 Chaos based time series analysis, Machine
learning
 Digital sensors
 Model predictive control
 Auction mechanisms
 Mathematical optimal control
 Queueing theory
 Complex networks theory
Situation Identification
62%
5
6
59%
100%
1
64%
TAO
8
7
52%
10
62%
11
4
13
95%
9
2
12
Objective: Estimate
64%
14
15
16
17
global stress environments at
TAO
Methodologies: Time
series analysis (Chaos),
Machine learning
Adaptive Control
1000
A1
200
N3
500
100
500
A5
A6
A7
N1
LP
Heuristic
Objective: Build
N2
A8
500
A3
A10
A4
A9
A2
100
A12
N5
500
A13
A14
300
A15
A16
N4
A11
CPU
distributed adaptive
control policy for the
stress environment
Control facilities:
Resource allocation,
Alternative algorithms
Adaptive Control
Methodologies: Model predictive control, Auction
Stress Environment
Sensor Design
Continuous Modeling
Sensor
Sensor
Sensor
Agent 1
Agent 2
Agent 3
Periodic Auctioning
Auction
Mathematical
Programming
Decentralized
Coordination
DMAS Implementation: CPE Society
 Military logistics



Command and Control Structure
Distributed, continuous planning and execution
Stressful Environment: Stresses range from heavy computational
loads to infrastructure loss
 Objective: Identify and demonstrate key concepts in the
argument for and concept of “design for survivability
Specification and Performance
Estimation
 Methodology: XML based
distributed specification
(TechSpecs), Queueing
theory based performance
modeling.
 Description:


TechSpecs described agent
attributes, measurement
points and control
parameters.
BCMP network and Whitt
QNA employed to estimate
the end-to-end app-layer
response times and remove
infeasible operating modes.
Control of the DMAS
 Methodology: Application-
Layer control using
queueing theory, and other
learned models.
 Description: Trading off
QOS (plan quality) for
performance (response time)
using estimates gained from
Queueing network models.
Regression models used to
assess the impact of model
prediction on application
utility.
Designing a Network Infrastructure
 Methodology: Optimization
using GA.
 Description: Represent the
entire network of agents as a
math programming model with
constraints on resources with an
objective to minimize the total
set-up costs.
Hierarchical Agent Society Satisfying Constraints with
Minimum Total Infrastructure Set-up Cost
Mathematical Formulation
Load Control Problem for Agent
Systems
 Optimal resource control to optimize long run
performance.
 Piecewise deterministic Markov process for dynamic
environment (workload and CPU availability)
workload stress
r1, Z ( t )
Z(t) ~ finite state, CTMC
h( x(t ))
d1
x1 (t )
B
r2, Z ( t )
x 2 (t )
B
CPU
l1
DZ (t )
d2
l2
c2 (l(t ))
Workload
CPU stress
Agent
d1, d2 : CPU time allocation
l1, l2 : algorithm control
Survivability: Topological perspective
 Objective: Survivability of large-scale network
 Methodology: Complex networks theory
Cyber Design Network (CD-NET)
Company B
Agent
Company A
Agent
Challenges:
Design
Agent B
Univ. Agent
Security
•Robustness
against damage
(infrastructure
and application)
Design
Agent A
Robustness
•Security
against cyber
attacks, hackers
Repository
Agent
Idaho
MIT
BU
PSU
SU
UM
NIST
VT
NASA
•Scalability to
growth and load
of the network
GT
Web service
Repository / Digital Libraries
Scalability
Distributed Large Scale Networks
Research- Lessons Learnt and their
usefulness to CD-NET
 Distributed Agents – Agent definitions,
communication and platform are critical
 Agent Composition to solve a problem is feasible
through TechSpecs (meta-data) and dynamic
service discovery
 Ontologies are the foundation for TechSpecs
 Infrastructure Survivability – Optimization
approaches
 Application Survivability – Through CAS analysis