Title: Survivability of Large Scale Networks and Design Research
1Survivability 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
- skumara_at_psu.edu
2CYBER DESIGN NET(CD-NET)
Agent based Design Network
3Agent based Design
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
4Logistics 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)
5Sensor Networks
NSF SST Self-Supporting Wireless Sensor
Networks for In-Process and In-Service Integrity
Monitoring Using High Energy-Harvesting 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
6Military Logistics (UltraLog)
- Secureagainst cyber attack
- Robustagainst 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)
7UltraLog Challenges (PSU)
- Situation Identification
- Performance Estimation
- Adaptive Control
- Hierarchical Control
- Robustness
- Infrastructure level
- Application level
- Network Survivability
- Security
8Methodologies
- Chaos based time series analysis, Machine
learning - Digital sensors
- Model predictive control
- Auction mechanisms
- Mathematical optimal control
- Queueing theory
- Complex networks theory
9Situation Identification
- Objective Estimate global stress environments at
TAO - Methodologies Time series analysis (Chaos),
Machine learning
10Adaptive Control
- Objective Build distributed adaptive control
policy for the stress environment - Control facilities Resource allocation,
Alternative algorithms
11Adaptive Control
Methodologies Model predictive control, Auction
12DMAS 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
13Specification 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.
14Control 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.
15Designing 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
16Mathematical Formulation
17Load Control Problem for Agent Systems
- Optimal resource control to optimize long run
performance. - Piecewise deterministic Markov process for
dynamic environment (workload and CPU
availability)
18Survivability Topological perspective
- Objective Survivability of large-scale network
- Methodology Complex networks theory
19Cyber Design Network (CD-NET)
- Challenges
- Securityagainst cyber attacks, hackers
- Robustnessagainst damage (infrastructure and
application) - Scalability to growth and load of the network
20Distributed 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