1. A Network Virtual Machine for Real-Time Coordination Services Professor Jack Stankovic, PI Department of Computer Science University of Virginia June 2001

2. Outline Overview –Problem/Goal –Research Team/Team Coordination Specific Problems/Key Issues Research Approach Success Schedule and Milestones Deliverables

3. Sensor/Actuator Clouds Heterogeneous Sensors/Actuators/CPUs Resource management, team formation, real-time, mobility, power battlefield awareness (more later) earthquake response tracking movements of animals Smart Dust

4. Goal Create a network virtual machine that is a coordination and control layer (middleware) that –abstracts –controls, and –guarantees aggregate behavior for unreliable and mobile networks of sensors, actuators, and processors.

5. The Team Lockheed Martin Virginia CMUIllinois Applications Req. Aggregate Control MMDP RT FC Team Coord. Data Discovery Wireless

6. The Team University of Virginia –Tarek Abdelzaher, Sang Son, Jack Stankovic (PI), Gang Tao University of Illinois –Lui Sha, P. R. Kumar CMU –Bruce Krogh Lockheed Martin –Dennis Adams

7. Primary Responsibilities Applications and Transition - Adams Data Discovery - Son Team Coordination - Sha and Abdelzaher Aggregate Control - Stankovic, Tao, Krogh Wireless - Kumar

8. Specific Problems/Key Issues Application Requirements Aggregation - system as a whole must meet requirements –individual entities not critical –Real-Time, Power, Mobility, Wireless, Size, Cost, (Security and Privacy) Self-organizing protocols that organize mobile sensor control agents into teams Environment Data Discovery Wireless Communications - capacity man.

9. Overview of Research Approach Application requirements Behavior specification language - listen, move, call-in-fire, call-in-jamming Integration of real-time computing theory, multi-mode MDP, and feedback control theory Composable and scalable micro-protocols that can self- organize distributed devices into collaborative teams to achieve aggregate goals Protocols for dynamic environmental data discovery Scaling of wireless networks and protocols for capacity management and interaction with aggregate control

10. Integrated Theory Multi-Mode Markov Decision Processes (chooses modes) Robust Feedback Control and Real-Time Scheduling Theory Combined to design each set of controllers Set of Adaptive Controllers 1 with Elastic RT Scheduling Set of Adaptive Controllers N with Elastic RT Scheduling Middleware Architecture A Network Virtual Machine for Real-Time Coordination Services

11. Large, heterogeneous network of unattended sensor/communication nodes provides battlefield awareness to military commanders at all echelons. –Unattended ground sensors –Robotic ground vehicles –Micro air vehicles –Miniature aerostats Notional NEST Application: Distributed Surveillance Network Nodes collect, filter, and route battlefield information to client. –Visible and IR imagery –Seismic and acoustic –RF –Chemical

12. Node communication range (a)  2x node sensor range (b) Distributed Surveillance Network a b Each node capable of sensing and relaying data to neighbors Network learns patterns, recognizes anomalies, and routes information to appropriate clients Node 1 Node 2 Node 3 Enemy Activity

13. University of Virginia, University of Illinois, CMU, Lockheed Martin NE&SS-Akron Typical Operational Situation (OPSIT) AAA Decoy Distributed Surveillance Network –Network deployed from high altitude to assess enemy air defenses prior to strike. –Network identifies potential enemy AAA sites, communicates locations to command structure. –Network associates tracks from node neighbors to postulate increased vehicular traffic at specific candidate sites. –Nodes local to candidate sites monitor increased human activity as hostilities increase; decoy AAA sites rejected. –Network routes around failed nodes to distribute targeting and BDA information during and after air strike.

14. How the Problems Change Environment –connect to physical environment (large numbers) –massively parallel interfaces –faulty, highly dynamic, non-deterministic Network –wireless –structure is dynamically changing –sporadic connectivity –new resources entering/leaving –large amounts of redundancy –self-configure/re-configure

15. Aggregate Performance Specify and control emerging behavior to meet system-level requirements –Smart Clouds of sensors/actuators/cpus in battlefield environments Combine FC, MMDP and elastic RT scheduling

16. FC-EDF scheduler PID Controller QoS Controller Admission Controller EDF Scheduler CPU FC-EDF Accepted Tasks Submitted Tasks MR s MR(t) Completed Tasks UU Adjust QoS Admit Reject EDF Sched Design and Evaluation of a feedback control EDF scheduling algorithm, IEEE RTSS’99

17. Performance Specs Transient Response t y(t) Transient response of a second order system

18. FC-EDF 2 scheduler PID Controller QoS Controller Admission Controller EDF Scheduler CPU FC-EDF 2 Accepted Tasks Submitted Tasks MR s MR(t) Completed Tasks UU PID Controller UsUs Min U(t) UmUm UuUu Adjust QoS Admit Reject EDF Sched

19. Network Architectures - Classical HierarchicalNeighborhood

20. Distributed Control System Architecture

21. Network Architectures - Non-classical Clouds of sensors/actuators/cpus –network architecture dynamically changing (fast) –subject to high error rate –new resources entering and leaving due to mobility, faults, …. –Power/mobility/communication/computation/secu rity tradeoffs

22. Aggregate Control Feedback Control Theory –explicit use of real-time –computer system models –transient performance specifications –adaptive/robust control –utilization bounds –elastic control –random algorithms

23. The Multi-Mode MDP Approach NEST applications as Markov decision processes –Discrete-state, discrete-time features –Markovian behavior –Influence of resource allocation decisions Challenges –size and complexity of NEST applications –abrupt and random changes in topology –abrupt and random changes in the environment Multi-mode approach –basic MDP formulation is intractable for NEST –behaviors can be aggregated into modes corresponding to various topologies/components

24. action a k Multi-Mode MDPs Strategies P1P1 PnPn ENVIRONMENT NEST Virtual Machine NEST Components Sensor/ Actuator Interactions mode estimation switching rule state estimation two-level MDP model mode MDP state MDP mode m k state x k action a k observations multi-mode policies resource allocation policy multi-mode MDP resource allocation strategy

25. MMDP Research Issues Modeling –state variables and validation of Markov assumption –action variables and influences on transition probabilities –network and environmental modes –observable states and modes Scalable Strategies –design of mode-matching policies –state and mode aggregation –mode estimation and policy switching Adaptive Strategies –run-time policy improvement Integration –data acquisition and fusion from NEST sensors –with local/global individual mode controllers –implementation via micro-protocols

26. Summary - Aggregate Control Integrated Theory Multi-Mode Markov Decision Processes (chooses modes) Robust Feedback Control and Real-Time Scheduling Theory Combined to design each set of controllers Set of Adaptive Controllers with Elastic RT Scheduling

27. Team Formation For each major task, a reference model for an ideal team is defined (the dream team model) –Roles and members needed (minimal, ideal) –Computational requirements (minimal, idea) –Communication flow (minimal, ideal) Utility functions to be defined, so that we can compute the gain as a function of members, computation and communication resources available. Teams compete for resources: members, computation and communication resources. Allocate resource to maximize total payoff. Challenge fundamental assumptions, e.g., in consensus algorithms

28. Data Discovery Find interesting information in the environment - geographic based –move proper resources to those areas of interest Procedure –identify target data streams and attributes needed –remove noise, outliers, synchornize streams, etc. –data discovery (find patterns of interest) Analogy: data mining on a non-stationary dataset

29. Challenges in Wireless Networks Networks of wireless nodes - Ad Hoc Networks –Spontaneously deployable anywhere –Adaptive to nodes, mobility, volatility Issues –How much traffic can they carry?  Scalability  Performance of protocols for  Power control  Routing  MAC  ….  Clean abstraction for control and surveillance

30. Approach Power control algorithms –for enhancing capacity –for providing power aware routes –for reducing MAC contention Media Access Control –build on SEEDEX protocol –no reservations –new idea of exchanging the seeds of random number Study performance and scaling of routing algorithms Study performance of transport layer protocols

31. Success Application Level (battlefield scenario) : –Find information faster and more accurately via coordination, react quicker and with higher throughput, re-configure when necessary, able to scale Network Virtual Machine for NEST –hide complexity of environment Unified theory of QoS aggregate control Self-configuring team formation protocols under new constraints Etc.

32. Tasks 1: Application Req. 2: Behavioral Spec Lang 3: Mapping to System Level Parameters 4: Architecture For Data Discovery 5: Data Discovery Protocols 6: Micro-Protocols for Team Formation –form teams –timely and coherent info 7: Robust and Adaptive Controllers –decentralized control –MMDP 8: Option years 9: Testbed Development 10: Testing and Demos 11: Reports and Papers 12: Work with OEP

33. Schedule and Milestones

34. Deliverables An API that supports behavioral abstractions Library routines to map behavioral abstractions into system level requirements Architecture design for data discovery Micro-protocols for team formation Aggregate QoS control for first part of scheduling problem (as defined in proposal) Simulation testbed (for first stage) Quarterly reports, final report

35. A Network Virtual Machine for Real-Time Coordination Services New Ideas Integration of real-time computing theory, multi-mode MDP, and feedback control theory Composable and scalable micro-protocols that can self- organize distributed devices into collaborative teams to achieve aggregate goals Scaling of wireless networks and protocols for capacity enhancement Protocols for dynamic environmental data discovery Impact Guaranteed aggregate behavior of NEST systems Control of mobile sensor/actuator/computer networks Large scale distributed team coordination Theory and practice for performance control Survival of essential services John A. Stankovic (stankovic@cs.virginia.edu), University of Virginia University of Illinois, CMU, Lockheed Martin Heterogeneous Sensors/Actuators/CPUs Resource management, team formation, real-time, mobility, power Network Virtual Machine (hides complexity of physical environment - battlefield awareness) Schedule 16 Months Year 2 Year 3 behavior spec. language self-organizing teams protocol QoS aggregate control demo protocols for self-organizing nodes robust an adaptive controllers demo integrated theory NEST middleware demo