Sense-and-Respond Systems and Play-Back Buffers Vincenzo Liberatore Division of Computer Science Research supported in part by NSF CCR , Department of Commerce TOP , NASA NNC04AA12A, and an OhioICE training grant.

V. LiberatoreControl Playback2 Sense-and-Respond Computing in the physical world Components –Sensors, actuators –Controllers –Networks

V. LiberatoreControl Playback3 Sense-and-Respond Enables –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

V. LiberatoreControl Playback4 Information Flow Flow –Sensor data –Remote controller –Control packets Timely delivery –Stability –Safety –Performance

V. LiberatoreControl Playback5 Autonomy S&R and real-time –Autonomy Hide networked RT Hard to build a fully reliable system –Tele-operation Network non-determinism is serious problem S&R –Reduce time constants –Especially important for unexpected occurrences [NLN02] Tele-operationAutonomy S&R

V. LiberatoreControl Playback6 Playback Buffers [Infocom 2006] Main objective –Smooth out network non-determinism Related to –Multimedia buffers –TCP RTO

V. LiberatoreControl Playback7 Multimedia Play-Back Sequence number time Packet generation Play-back Packet arrival

V. LiberatoreControl Playback8 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

V. LiberatoreControl Playback9 Playback Buffers [Infocom 2006] Play-back buffers –Main objective –Smooths out network non-determinism 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 –Large time-out cost

V. LiberatoreControl Playback10 Algorithm

V. LiberatoreControl Playback11 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

V. LiberatoreControl Playback12 Algorithm Send regular control –Playback time Late playback okay –Expiration Piggyback contingency control

V. LiberatoreControl Playback13 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! plant controller Playback delay X X

V. LiberatoreControl Playback14 Countermand control Scenario –Packet i+1 overtakes packet I –  i+1 <<  i –Likely caused by delay spike New signal countermands previous one plant controller Playback delay ii  i+1

V. LiberatoreControl Playback15 Playback Delays (I) Modular component Compute playback delay  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  time

V. LiberatoreControl Playback16 Playback Delays (II) Aggressively attempt to decrease T Add upper bound on playback delay  –Avoid dropping deadlock packets –Bound  ≤ T+RTT Caps  and T Must estimate lower-bound on RTT –Use symmetric of peak-hopper –Add negative variability estimate to compensate for short-term memory

V. LiberatoreControl Playback17 Playback Delays (III) Calculate current RTT variability ifthen Positive variability coefficient Negative variability coefficient Update min RTT estimate Age min RTT estimate Calculate 

V. LiberatoreControl Playback18 Playback Delays (IV) ifthen else Attempt to avoid quashed packets Decrease sampling period

V. LiberatoreControl Playback19 Control Pipes Bandwidth and delays –  is playback delay –T is sampling period 1/T proportional to bandwidth Control pipe –T«  –Multiple in-flight packets Pipe depth –Bound by constraint  ≤ T+RTT –Keep pipe predictable

V. LiberatoreControl Playback20 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

V. LiberatoreControl Playback21 Evaluation

V. LiberatoreControl Playback22 Network Model Simulated network Losses: Gilbert model Delays –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

V. LiberatoreControl Playback23 Plant Scalar linear plant –Plant state x(t) –Input u(t) (control) –Output y(t) –Disturbances v(t), w(t) Akin to white noise Deadbeat controller –Aggressive

V. LiberatoreControl Playback24 Metrics –Root-mean square output –Output: 99-percentile Comparison –Open-loop plant u(t)=0 –Proportional controller (no buffer) –Proportional controller with constant delays

V. LiberatoreControl Playback25 Plant output Open LoopPlay-back

V. LiberatoreControl Playback26 Packet losses Figure 8

V. LiberatoreControl Playback27 Sampling period Imperfection of the control pipe Root-mean-square error  ≤T+RTT

V. LiberatoreControl Playback28 Other Research in Sense-and-Respond

V. LiberatoreControl Playback29 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 self- reconfigurable

V. LiberatoreControl Playback30 Problem Formulation Define a utility fn U(r) that is –Monotonically increasing –Strictly concave –Defined for r ≥ r min Optimization formulation

V. LiberatoreControl Playback31 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)

V. LiberatoreControl Playback32 Conclusions (II) Playback delay  –Adapts to network conditions Sampling period T –Avoids imperfection of control pipe Simulations and emulations –Low variability around set point –Robust