Survivability of Large Scale Networks and Design Research

1. 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@psu.edu

2. CYBER DESIGN NET(CD-NET) Agent based Design Network

3. Retrieval Agent Design Repository Design Agent 1 Design Agent 2 Retrieval Agent Customization Agent Coordinator Agent Design Repository Retrieval Agent Design Agent n Functional Requirements Customer Needs New Product Product Analysis 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

4. 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)

5. 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

6. Military Logistics (UltraLog) • Secureagainst cyber attack • Robustagainst damage • Scalableto 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)

7. UltraLog Challenges (PSU) • Situation Identification • Performance Estimation • Adaptive Control • Hierarchical Control • Robustness • Infrastructure level • Application level • Network Survivability • Security

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

9. 62% 6 7 5 52% 10 59% 100% 1 8 62% 64% 11 13 4 TAO 95% 9 2 12 64% 14 16 17 15 Situation Identification • Objective: Estimate global stress environments at TAO • Methodologies: Time series analysis (Chaos), Machine learning

10. N3 500 100 500 LP Heuristic A6 A7 A5 N1 N2 1000 A1 A8 N4 500 A10 A11 A3 A4 200 A9 A2 CPU 100 A12 N5 300 500 A13 A15 A16 A14 Adaptive Control • Objective: Build distributed adaptive control policy for the stress environment • Control facilities: Resource allocation, Alternative algorithms

11. Stress Environment Sensor Design Continuous Modeling Sensor Sensor Sensor Mathematical Programming Agent 1 Agent 2 Agent 3 Periodic Auctioning Decentralized Coordination Auction Adaptive Control Methodologies: Model predictive control, Auction

12. 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

13. 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.

14. 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.

15. 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

16. Mathematical Formulation

17. workload stress Z(t) ~ finite state,CTMC d1 CPU B d2 CPU stress B d1, d2 : CPU time allocation l1, l2 : algorithm control Workload Agent 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)

18. Survivability: Topological perspective • Objective: Survivability of large-scale network • Methodology: Complex networks theory

19. Cyber Design Network (CD-NET) • Challenges: • Securityagainst cyber attacks, hackers • Robustnessagainst damage (infrastructure and application) • Scalability to growth and load of the network

20. 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