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8/3/2019 Nest Kickoff Short

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A Network Virtual Machine forReal-Time Coordination Services

Professor Jack Stankovic, PI

Department of Computer ScienceUniversity of Virginia

June 2001


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Outline

Overview Problem/Goal

Research Team/Team Coordination

Specific Problems/Key Issues

Research Approach Success

Schedule and Milestones

Deliverables


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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


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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.


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The Team

Lockheed

Martin Virginia

CMU Illinois

ApplicationsReq.

Aggregate

Control

MMDP

RT

FC

Team

Coord.

Data

Discovery

Wireless


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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


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Primary Responsibilities

Applications and Transition - Adams

Data Discovery - Son

Team Coordination - Sha and Abdelzaher

Aggregate Control - Stankovic, Tao, Krogh

Wireless - Kumar


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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.


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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


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Integrated Theory

Multi-Mode MarkovDecision Processes

(chooses modes)

Robust FeedbackControl and Real-TimeScheduling TheoryCombined to designeach set of controllers

Set of AdaptiveControllers 1with Elastic RT

Scheduling

Set of AdaptiveControllers Nwith Elastic RT

Scheduling

Middleware Architecture

A Network Virtual Machine for Real-Time

Coordination Services


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Large, heterogeneous network of unattendedsensor/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 battlefieldinformation to client. Visible and IR imagery Seismic and acoustic RF

Chemical


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Node communication range (a)

2x node sensor range (b)

Distributed Surveillance Network

ab

Each node capable

of sensing and

relaying data toneighbors

Network learns

patterns, recognizes

anomalies, and routes

information to

appropriate clients

Node 1

Node 2

Node 3

EnemyActivity


13/35University of Virginia, University of Illinois, CMU, Lockheed Martin NE&SS-Akron

Typical Operational Situation (OPSIT)

AAA

AAA

Decoy

Distributed Surveillance Network

Network deployed from high altitude to assess enemy air defensesprior to strike.

Network identifies potential enemy AAA sites, communicates

locations to command structure.

Network associates tracks from

node neighbors to postulateincreased 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.


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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


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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


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FC-EDF scheduler

PID

ControllerQoS Controller

Admission

Controller

EDF

SchedulerCPU

FC-EDF

Accepted Tasks

Submitted Tasks

MRs

MR(t)

Completed Tasks

U AdjustQoS

Admit

Reject

EDF

Sched

Design and Evaluation of a feedback control EDF scheduling algorithm,IEEE RTSS99


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Performance SpecsTransient Response

t

y(t)

Transient response of a second order system


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FC-EDF2 scheduler

PID Controller

QoS Controller

Admission

Controller

EDF

Scheduler

CPU

FC-EDF2

Accepted Tasks

Submitted Tasks

MRs

MR(t)

Completed Tasks

U

PID ControllerUs

Min

U(t)

Um

Uu

Adjust

QoS

Admit

Reject

EDFSched


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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


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Distributed Control System

Architecture

SystemRCSL

Actuator

ACActuator

PID-1

PID-4

Node-MR

SLR

CPU_Util

MRctrl_signal

slr_ctrl

slr_setpoint

* Move into networkfor HCLOSE

* Added functionalityfor NCLOSE

P-2

P-3

min

P-5

min

DFCS LFCS


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Network Architectures -

Non-classical

Clouds of sensors/actuators/cpus

network architecture dynamically changing (fast)

subject to high error ratenew resources entering and leaving

due to mobility, faults, .

Power/mobility/communication/computation/secu

rity tradeoffs


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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


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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


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action

ak

Multi-Mode MDPs

StrategiesP1

Pn

ENVIRONMENT

NEST Virtual Machine

NEST

Components

Sensor/

Actuator

Interactionsmode

estimation

switching

rule

state

estimation

two-level MDP

modelmode

MDP

state

MDP

mode mk

state

xk

action

ak

observations

k

X

km

multi-mode policies

resource

allocation

policy

multi-mode MDP

resource allocation strategy


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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


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Summary - Aggregate Control

Integrated Theory

Multi-Mode MarkovDecision Processes

(chooses modes)

Robust FeedbackControl and Real-TimeScheduling TheoryCombined to designeach set of controllers

Set of Adaptive

Controllers with

Elastic RTScheduling


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Team Formation

For each major task, a reference model for an ideal team isdefined (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 andcommunication resources. Allocate resource to maximize

total payoff.

Challenge fundamental assumptions, e.g., in consensus

algorithms


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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


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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


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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


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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.


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Tasks

1: Application Req.

2: Behavioral Spec Lang

3: Mapping to System

Level Parameters

4: Architecture For DataDiscovery

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


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Schedule and Milestones


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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


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A Network Virtual Machine for Real-Time

Coordination ServicesNew 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

Heterogeneous Sensors/Actuators/CPUs

Resource management, team formation,real-time, mobility, power

Network Virtual Machine (hides complexity ofphysical environment - battlefield awareness)

Schedule

16 Months

Year 2

Year 3

behavior spec. language

self-organizing teams protocolQoS aggregate controldemo

protocols for self-organizing nodes

robust an adaptive controllers

demo

integrated theory

NEST middleware

demo

Documents

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