Transcript Integrated Play-Back, Sensing, and Networked Control

Sense-and-Respond Systems
and Play-Back Buffers
Vincenzo Liberatore
Division of Computer Science
Research supported in part by NSF CCR-0329910, Department of Commerce
TOP 39-60-04003, NASA NNC04AA12A, and an OhioICE training grant.
Sense-and-Respond
• Computing in the physical
world
• Components
– Sensors, actuators
– Controllers
– Networks
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Sense-and-Respond
• Enables
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Industrial automation [BL04]
Distributed instrumentation [ACRKNL03]
Unmanned vehicles [LNB03]
Home robotics [NNL02]
Distributed virtual environments [LCCK05]
Power distribution [P05]
Building structure control [SLT05]
• Merge cyber- and physical- worlds
– Networked control and tele-epistemology [G01]
• Sensor networks
– Not necessarily wireless or energy constrained
– One component of sense-actuator networks
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Information Flow
• Flow
– Sensor data
– Remote controller
– Control packets
• Timely delivery
– Stability
– Safety
– Performance
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Playback Buffers [Infocom 2006]
• Main objective
– Smooth out network non-determinism
• Related to
– Multimedia buffers
– TCP RTO
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Multimedia Play-Back
Sequence number
Packet generation
Packet arrival
Play-back
time
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Related Work
• Multimedia buffers
– Important source of inspiration
– Physics versus multimedia quality
– Playback delay computed in advance
• Affects control signal computation
– Round-Trip Times
• TCP RTO
– Another source of inspiration
• Upper bound on RTT
– Large time-out cost
• Conservative estimate
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Algorithm
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Main Ideas
• Predictable application time
– If control applied early, plant is not in the state
for which the control was meant
– If control applied for too long, plant no longer
in desired state
• Keep plant simple
– Low space requirements
• Integrate Playback, Sampling, and Control
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Algorithm
• Send regular control
– Playback time
• Late playback okay
– Expiration
• Piggyback contingency control
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Deadwood packets
• Old
– Received after the expiration time
• Out-of-order
– Later control more appropriate for current plant state
• Would get us into a deadlock
– New packet resets the playback timer
– Keep resetting until no signal applied
– “Quashed” packet
• Discard!
controller
plant
Playback delay
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X
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Countermand control
• Scenario
– Packet i+1 overtakes packet I
– ti+1 << ti
– Likely caused by delay spike
• New signal countermands previous one
controller
plant
Playback delay
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ti+1
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ti
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Playback Delays (I)
• Modular component
• Compute playback delay t and sampling period T
• Use short term peak-hopper [EL04]
– Original peak-hopper for TCP RTO
• Too conservative for networked control
– Aggressively attempt to decrease t
time
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Playback Delays (II)
• Aggressively attempt to decrease T
• Add upper bound on playback delay t
– Avoid dropping deadlock packets
– Bound t ≤ T+RTT
• Caps t and T
• Must estimate lower-bound on RTT
– Use symmetric of peak-hopper
– Add negative variability estimate to
compensate for short-term memory
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Playback Delays (III)
r1  r0

r0
Calculate current RTT variability
B  min{max{2 ,0.9375B},1}
Positive variability coefficient
if   0 then C  max{3C / 4   / 4,1 / 2}
r '  (1  C) min{r1 , r0}
r 'rmin
rmin  rmin  r ' ? rmin 
: r'
16
t  (1  B) max{r1, r0}
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Negative variability
coefficient
Update min RTT estimate
Age min RTT estimate
Calculate t
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Playback Delays (IV)
if
t  T 'rmin
then
T  t  rmin
Attempt to avoid quashed packets
t  T 'rmin
else
T 't  rmin
T T 
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Decrease sampling period
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Control Pipes
• Bandwidth and delays
– t is playback delay
– T is sampling period
• 1/T proportional to bandwidth
• Control pipe
– T«t
– Multiple in-flight packets
• Pipe depth
– Bound by constraint t ≤ T+RTT
– Keep pipe predictable
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Observer
• Estimate future plant state
– Plant sample current state, including local variables
– Keep log of outstanding control packets
• Assumption on packet delivery
– Future packet delivery is uncertain
• Purge from log
– Old packets
– Packet that should be overtaken by new control
• Countermands signals generated when delay spike is
transient
– Out-of-order packets
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Evaluation
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Network Model
• Simulated network
• Losses: Gilbert model
• Delays
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Shifted Gamma distribution
Heavy tail
Low probability of out-of-order delivery
Correlate delays to introduce delay spikes
• Wide-area implementation
• Use RT scheduling whenever possible
• Use otherwise unloaded machines
– RT made little difference
• Host worldwide, heterogeneous conditions
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Plant
• Scalar linear plant
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Plant state x(t)
Input u(t) (control)
Output y(t)
Disturbances v(t), w(t)
 x (t )  ax(t )  bu(t )  v(t )

 y(t )  x(t )  w(t )
• Akin to white noise
• Deadbeat controller
– Aggressive
a e aT
u  ky; k 
b e aT  1
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Metrics
• Metrics
– Root-mean square output m2 
– Output: 99-percentile
y
2
• Comparison
– Open-loop plant u(t)=0
– Proportional controller (no buffer)
– Proportional controller with constant delays
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Plant output
Open Loop
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Packet losses
Figure 8
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Sampling period
t ≤T+RTT
Root-mean-square error
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Imperfection of the
control pipe
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Other Research in
Sense-and-Respond
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Bandwidth Allocation
• Definition
– Multiple sense-and-respond
flows
– Contention for network
bandwidth
• Desiderata
– Stability and performance of
control systems
• Must account for physics
– Efficiency and fairness
– Fully distributed,
asynchronous, and scalable
– Dynamic and selfreconfigurable
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Problem Formulation
• Define a utility fn U(r) that is
– Monotonically increasing
– Strictly concave
– Defined for r ≥ rmin
• Optimization formulation
max  i Ui (ri )
s.t.

iS ( l )
ri  Cl , l  1,..., L
and ri  r min, i
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Conclusions (I)
• Sense-and-Respond
– Merge cyber-world and physical world
– Critically depends on physical time
• Playback buffers integrated with
– Sampling (adaptive T)
– Control (expiration times, performance
metrics)
• Packet losses
– Reverts to open loop plant (contingency
control)
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Conclusions (II)
• Playback delay t
– Adapts to network conditions
• Sampling period T
– Avoids imperfection of control pipe
• Simulations and emulations
– Low variability around set point
– Robust
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