A Network Virtual Machine for Real-Time Coordination Services

Professor Jack Stankovic, PI

Department of Computer Science

University of Virginia

June 2001

OutlineOutline• Overview

– Problem/Goal

– Research Team/Team Coordination

• Specific Problems/Key Issues

• Research Approach

• Success

• Schedule and Milestones

• Deliverables

Sensor/Actuator CloudsSensor/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

GoalGoal

• 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.

The TeamThe Team

LockheedMartin Virginia

CMU Illinois

Applications Req.

AggregateControl

MMDP

RT

FC

TeamCoord.

DataDiscovery

Wireless

The TeamThe 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

Primary ResponsibilitiesPrimary Responsibilities

• Applications and Transition - Adams

• Data Discovery - Son

• Team Coordination - Sha and Abdelzaher

• Aggregate Control - Stankovic, Tao, Krogh

• Wireless - Kumar

Specific Problems/Key IssuesSpecific 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.

Overview of Research ApproachOverview 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

Integrated Theory

Multi-Mode MarkovDecision Processes

(chooses modes)

Robust FeedbackControl and Real-TimeScheduling Theory Combined to designeach set of controllers

Set of Adaptive Controllers 1with Elastic RTScheduling

Set of Adaptive Controllers Nwith Elastic RTScheduling

Middleware Architecture

A Network Virtual Machine for Real-Time Coordination Services

• 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

Notional NEST Application:Distributed Surveillance Network

• Nodes collect, filter, and route battlefield information to client.– Visible and IR imagery– Seismic and acoustic– RF– Chemical

• Node communication range (a) 2x node sensor range (b)

Distributed Surveillance NetworkDistributed Surveillance Network

ab

• 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

University of Virginia, University of Illinois, CMU, Lockheed Martin NE&SS-Akron

• Typical Operational Situation (OPSIT)

AAA

AAA

Decoy

Distributed Surveillance Network 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.

How the Problems ChangeHow 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

Aggregate PerformanceAggregate 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

FC-EDF schedulerFC-EDF scheduler

PID Controller

QoS Controller

Admission Controller

EDFScheduler

CPU

FC-EDF

Accepted Tasks

Submitted Tasks

MRs

MR(t)

Completed Tasks

U AdjustQoS

AdmitReject

EDFSched

Design and Evaluation of a feedback control EDF scheduling algorithm, IEEE RTSS’99

Performance SpecsTransient Response

Performance SpecsTransient Response

t

y(t)

Transient response of a second order system

FC-EDF2 schedulerFC-EDF2 scheduler

PID Controller

QoS Controller

Admission Controller

EDFScheduler

CPU

FC-EDF2

Accepted Tasks

Submitted Tasks

MRs

MR(t)

Completed Tasks

U

PID ControllerUs

Min

U(t)

Um

Uu

AdjustQoS

AdmitReject

EDFSched

Network Architectures - Classical

Network Architectures - Classical

Hierarchical Neighborhood

15

9

13

1

10

11 12

2 3 4 5

76

14

8

15

9

13

1

10 11 12

2 3 4 5 76

14

8

Distributed Control System Architecture

Distributed Control System Architecture

S ystem

RC

SL

Act

ua

tor

A CA ctuator

P ID -1

P ID -4

N ode-M R

S LR

C P U _U til

M Rctrl_s igna l

s lr_ctrl

s lr_setpo in t

* M ove in to ne tw ork fo r H C LO S E* A dded functiona lity fo r N C LO S E

P -2

P -3

m in

P -5

m in

DFCS LFCS

Network Architectures - Non-classical

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/security tradeoffs

Aggregate ControlAggregate Control

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

The Multi-Mode MDP Approach

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

action ak

Multi-Mode MDPs Strategies

Multi-Mode MDPs Strategies

P1

Pn

ENVIRONMENT

NEST Virtual Machine

NEST Components

Sensor/ActuatorInteractions

modeestimation

switchingrule

stateestimation

two-level MDPmodel

modeMDP

stateMDP

mode mk

state xk

action ak

observations

kX̂km̂

multi-mode policies

resourceallocation

policy

multi-mode MDP resource allocation strategy

MMDP Research IssuesMMDP 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

Summary - Aggregate ControlSummary - Aggregate Control

Integrated Theory

Multi-Mode MarkovDecision Processes

(chooses modes)

Robust FeedbackControl and Real-TimeScheduling Theory Combined to designeach set of controllers

Set of AdaptiveControllers withElastic RTScheduling

Team FormationTeam 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

Data DiscoveryData 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

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

ApproachApproach

• 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

SuccessSuccess

• 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.

TasksTasks• 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

Schedule and MilestonesSchedule and Milestones

DeliverablesDeliverables

• 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

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 servicesJohn A. Stankovic (stankovic@cs.virginia.edu), University of VirginiaUniversity 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