Survivability of Large Scale Networks and Design Research

Soundar R.T. KumaraDistinguished Professor of Industrial and

Manufacturing EngineeringThe Pennsylvania State University

University Park, PA 16802skumara@psu.edu

NSF-EXCITED WorkshopFebruary 28, 2005

CYBER DESIGN NET(CD-NET)

PSU

VT

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BU

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UMSU

Idaho

Univ. Agent

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Repository / Digital Libraries

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NIST

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MIT

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UMSU

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CustomizationAgent

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Agent based Design Network

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

CustomizationAgent

DesignAgent 1

CoordinatorAgent

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DesignAgent n

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Customer Needs

Functional Requirements

Design Repository

NewProduct

Design Repository

Product Analysis

DesignAgent 2

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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)

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

Military Logistics (UltraLog)

•Secureagainst cyber attack

•Robustagainst damage

•Scalable to wartime data loads

UltraLog: Extremely survivable net-centric logistics information systems for the modern

battlefieldDARPA - 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

Objective: Estimate global stress environments at TAO

Methodologies: Time series analysis (Chaos), Machine learning

1

2

TAO 4

8

9

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11

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5 7

13

14 15

6

16 17

100%

95%

52%

62%

62%

59%

64%

64%

Adaptive Control

N1

A1

A2

A3 A4

A8

A9

A10

A12

A5A7

A11

A13 A14

A6

A15 A16

N2

N4

N3

N5

1000

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500 500100

100

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CPU

LP

Heuristic

Objective: Build distributed adaptive control policy for the stress environment

Control facilities: Resource allocation, Alternative algorithms

Adaptive Control

Stress Environment

Auction

Agent 1Sensor

Agent 2Sensor

Agent 3Sensor

Sensor Design

Mathematical Programming

Decentralized Coordination

Continuous Modeling

Periodic Auctioning

Methodologies: Model predictive control, Auction

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

Hierarchical Agent Society Satisfying Constraints with Minimum Total Infrastructure Set-up Cost

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.

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)

CPU

Workload Agent

workload stress

d1

d2

)(,1 tZr

)(,2 tZr

1l

2l

)(tZDB

B

Z(t) ~ finite state, CTMC

CPU stress

))(( th x

))((2 tc l

)(1 tx

)(2 tx

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)

Challenges:

•Securityagainst cyber attacks, hackers

•Robustnessagainst damage (infrastructure and application)

•Scalability to growth and load of the network

Scalability

Sec

urity

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ustn

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Scalability

Sec

urity

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ustn

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PSU

VT

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BU

MIT

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UMSU

Idaho

Univ. Agent

RepositoryAgent

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Repository / Digital Libraries

PSU

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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