Survivability of Large Scale Networks and Design Research

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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
• skumara_at_psu.edu

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CYBER DESIGN NET(CD-NET)
Agent based Design Network
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Agent 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
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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)
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Sensor 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
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Military 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)
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UltraLog Challenges (PSU)

• Situation Identification
• Performance Estimation
• Adaptive Control
• Hierarchical Control
• Robustness
• Infrastructure level
• Application level
• Network Survivability
• Security

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Methodologies

• Chaos based time series analysis, Machine
  learning
• Digital sensors
• Model predictive control
• Auction mechanisms
• Mathematical optimal control
• Queueing theory
• Complex networks theory

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Situation Identification

• Objective Estimate global stress environments at
  TAO
• Methodologies Time series analysis (Chaos),
  Machine learning

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Adaptive Control

• Objective Build distributed adaptive control
  policy for the stress environment
• Control facilities Resource allocation,
  Alternative algorithms

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Adaptive Control
Methodologies Model predictive control, Auction
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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

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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.

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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.

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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
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Mathematical Formulation
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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)

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Survivability Topological perspective

• Objective Survivability of large-scale network
• Methodology Complex networks theory

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Cyber Design Network (CD-NET)

• Challenges
• Securityagainst cyber attacks, hackers
• Robustnessagainst damage (infrastructure and
  application)
• Scalability to growth and load of the network

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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