A Network Virtual Machine for
Real-Time Coordination Services
Professor Jack Stankovic, PI
Department of Computer Science
University of Virginia
June 2001
Outline
• Overview
– Problem/Goal
– Research Team/Team Coordination
•
•
•
•
•
Specific Problems/Key Issues
Research Approach
Success
Schedule and Milestones
Deliverables
Sensor/Actuator Clouds
Resource management, team formation,
real-time, mobility, power
Heterogeneous Sensors/Actuators/CPUs
Smart Dust
• battlefield awareness (more later)
• earthquake response
• tracking movements of animals
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.
The Team
Lockheed
Martin
Applications
Req.
Virginia
FC
Aggregate
Control
Team
Coord.
Wireless
Data
Discovery
MMDP
CMU
RT
Illinois
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
Primary Responsibilities
•
•
•
•
•
Applications and Transition - Adams
Data Discovery - Son
Team Coordination - Sha and Abdelzaher
Aggregate Control - Stankovic, Tao, Krogh
Wireless - Kumar
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.
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 selforganize 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
A Network Virtual Machine for Real-Time
Coordination Services
Integrated Theory
Multi-Mode Markov
Decision Processes
(chooses modes)
Set of Adaptive
Controllers 1
with Elastic RT
Scheduling
Set of Adaptive
Controllers N
with Elastic RT
Scheduling
Robust Feedback
Control and Real-Time
Scheduling Theory
Combined to design
each set of controllers
Middleware Architecture
Notional NEST Application:
Distributed Surveillance Network
• 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
• Nodes collect, filter,
and route battlefield
information to client.
–
–
–
–
Visible and IR imagery
Seismic and acoustic
RF
Chemical
Distributed Surveillance Network
• Node communication range (a) 
2x node sensor range (b)
• Each node capable
of sensing and
b
relaying data to
Enemy
neighbors
Activity
Node 1
• Network learns
patterns, recognizes
anomalies, and routes
information to
appropriate clients
a
Node 2
Node 3
Distributed Surveillance Network
• Typical Operational Situation (OPSIT)
– 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
AAA
node neighbors to postulate
increased vehicular traffic at
Decoy
AAA
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.
University of Virginia, University of Illinois, CMU, Lockheed Martin NE&SS-Akron
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
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
FC-EDF scheduler
Completed Tasks
EDF
Scheduler
MR(t)
MRs
PID
Controller
FC-EDF
U
QoS Controller
Admission
Controller
CPU
EDF
Sched
Adjust
QoS
Accepted Tasks
Admit
Reject
Design and Evaluation of a feedback control EDF scheduling algorithm,
IEEE RTSS’99
Submitted Tasks
Performance Specs
Transient Response
Transient response of a second order system
y(t)
t
2
FC-EDF
scheduler
Completed Tasks
U(t)
MRs
EDF
Scheduler
MR(t)
CPU
PID Controller Um
U
U
Us
EDF
Sched
PID Controller
QoS Controller
Min
Adjust
QoS
Accepted Tasks
u
FC-EDF2
Admission
Controller
Admit
Reject
Submitted Tasks
Network Architectures Classical
Hierarchical
Neighborhood
15
13
10
2
12
13
14
15
6
7
8
9
10
1
2
3
4
5
14
9
1
11
3
11
4
5
12
6
7
8
Distributed Control System
Architecture
* Move into network
for HCLOSE
* Added functionality
for NCLOSE
DFCS
LFCS
P-5
min
slr_setpoint
PID-4
P-2
min
PID-1
ctrl_signal
Node-MR
RCSL
Actuator
slr_ctrl
P-3
AC
Actuator
SLR
System
CPU_Util
MR
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
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
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
multi-mode policies
Multi-Mode MDPs
Strategies
P1
action
ak
resource
allocation
policy
Pn
ak
m̂ k
state
estimation
mode
estimation
two-level MDP
model
NEST
Components action
switching
rule
X̂ k
NEST Virtual Machine
observations
Sensor/
Actuator
Interactions
mode
MDP
mode mk
state
xk
state
MDP
ENVIRONMENT
multi-mode MDP
resource allocation strategy
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
Summary - Aggregate Control
Integrated Theory
Multi-Mode Markov
Decision Processes
(chooses modes)
Set of Adaptive
Controllers with
Elastic RT
Scheduling
Robust Feedback
Control and Real-Time
Scheduling Theory
Combined to design
each set of controllers
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
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
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
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
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.
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
Schedule and Milestones
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
A Network Virtual Machine for Real-Time
Coordination Services
Network Virtual Machine (hides complexity of
physical environment - battlefield awareness)
Resource management, team formation,
real-time, mobility, power
New Ideas
• Integration of real-time computing theory, multi-mode
MDP, and feedback control theory
• Composable and scalable micro-protocols that can selforganize distributed devices into collaborative teams to
achieve aggregate goals
• Scaling of wireless networks and protocols for capacity
enhancement
• Protocols for dynamic environmental data discovery
Heterogeneous Sensors/Actuators/CPUs
Schedule
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
16 Months
Year
•behavior spec. language
•self-organizing teams protocol
•QoS aggregate control
•demo
•protocols for self-organizing nodes
2
•robust an adaptive controllers
•demo
Year 3
John A. Stankovic (stankovic@cs.virginia.edu), University of Virginia
University of Illinois, CMU, Lockheed Martin
•integrated theory
•NEST middleware
•demo