Implementation Challenges in Real-Time Middleware for Distributed Autonomous Systems Prof. Vincenzo Liberatore Research supported in part by NSF CCR , Department of Commerce TOP , NASA NNC04AA12A, a Lockheed grant, an ABB contract, and an OhioICE training grant.

Motivation Sustainable presence on planetary surface Human-robotic missions Human-robotic missions E.g., construction, maintenance E.g., construction, maintenanceConsequences Higher performance Higher performance Earth tele-operation inappropriate for construction Multiple assets Multiple assets Communication and coordination Autonomous Distributed System

Potential Scenario (Teleops) Tele-operations Robots, rovers Robots, rovers Pressurized vehicles Pressurized vehiclesRequirements Single- or multi-hop Single- or multi-hop End-point adaptation to network non- determinism End-point adaptation to network non- determinism Quality-of-Service Quality-of-Service System and control metrics System and control metrics Lander (Later Habitat) Surface Terminal 4-6 Humans on EVA Autonomous Robot Lunar Relay Teleoperated Robot Pressurized Vehicle Repeater

Talk Overview Bandwidth allocation Play-back buffer Quality-of-Service (QoS) DRE implementation Conclusions

Bandwidth Allocation Objectives: Stability of control systems Stability of control systems Efficiency & fairness Efficiency & fairness Fully distributed, asynchronous, & scalable Fully distributed, asynchronous, & scalable Dynamic & self reconfigurable Dynamic & self reconfigurable

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

Distributed Implementation Two independent algorithms End-systems (plants) algorithm End-systems (plants) algorithm Router algorithm (later on) Router algorithm (later on) NCS PlantNCS Controller Router pp p

NCS-AQM Control Loop tftf q(t) f(q(t)) q`=Σr(t) - C p(t) tbtb NCS PlantQueue Model Plant P(s) = Queue Controller G(s)

Queue Controller G(s) Proportional (P) Controller G P (s) = k p G P (s) = k p Proportional-Integral (PI) Controller G PI (s) = k p + k i /s G PI (s) = k p + k i /s q(s) G(s) P(s) q0q0 + _ eu

Determination of k p and k i Stability region in the k i –k p plane Stabilizes the NCS-AQM closed-loop system for delays less or equal d Stabilizes the NCS-AQM closed-loop system for delays less or equal d Analysis of quasi-polynomials, f(s,e s )

Simulations & Results 50 NCS Plants: [Branicky et al. 2002] [Zhang et al. 2001]

Simulations & Results (cont.) PI ¤ P¤P¤

Talk Overview Bandwidth allocation Play-back buffer Quality-of-Service (QoS) DRE implementation Conclusions

Information Flow Flow Sensor data Sensor data Remote controller Remote controller Control packets Control packets Timely delivery Stability Stability Safety Safety Performance Performance

Main Ideas Predictable application time If control applied early, plant is not in the state for which the control was meant 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 If control applied for too long, plant no longer in desired state Keep plant simple Low space requirements Low space requirements Integrate Playback, Sampling, and Control

Algorithm Send regular control Playback time Playback time Late playback okay Expiration Expiration Piggyback contingency control

Plant output Open LoopPlay-back

Packet losses Figure 8

Talk Overview Bandwidth allocation Play-back buffer Quality-of-Service (QoS) DRE implementation Conclusions

Network Quality-of-Service (QoS) Support real-time distributed applications Voice, video Voice, video Networked control Networked controlGuarantees Network metrics Network metricsBandwidthDelays Delay jitter Loss rates End-point metrics End-point metrics Tracking in networked control Example Packet priorities Packet priorities Current support in Internet Significant research and development Significant research and development None of the above: best-effort None of the above: best-effort

QoS and Space Networks Examples Human-robotic missions necessitate real-time communication Human-robotic missions necessitate real-time communication QoS no longer only for commercial satellite network QoS no longer only for commercial satellite network Fully Distributed QoS [IWQoS 2004] Local mechanisms to protect from global congestion risks Local mechanisms to protect from global congestion risks Addition to planned QoS Addition to planned QoS Autonomously adaptable to QoS requirements with no human supervision Autonomously adaptable to QoS requirements with no human supervision Protects from error in networks configuration Protects from error in networks configuration Suitable for Distributed Autonomous systems Suitable for Distributed Autonomous systems Higher performance Higher performance On the flight On the flight

The following videos were made possible by NASA funds provided by GRC under Contract NNC05CB20C Videos: Tele-Operation, Cross-Traffic and Distributed QoS Note: video not included in SMC-IT proceedings

Distributed QoS Definition Local mechanisms to protect from global risk Local mechanisms to protect from global risk Deployment and benefits Addition to planned QoS Addition to planned QoS Autonomously adaptable to QoS requirements with no human supervision Autonomously adaptable to QoS requirements with no human supervision Protects from error in networks configuration Protects from error in networks configuration Suitable for Distributed Autonomous systems Suitable for Distributed Autonomous systems Higher performance Higher performance On the flight On the flight

YAQS YAQS Yet Another Queuing Strategy Yet Another Queuing StrategyObjectives Distributed QoS Distributed QoS At the router At the router Queuing strategy Queuing strategy Protect from aggressive flows Protect from aggressive flows Short queuing delays Work in progress

YAQS: overview Two queues Fast queue Fast queue Objective: low queuing delays Slow queue Slow queue Deque: WRR Deque: WRREnque If flow exceeds maximum allowed fast occupancy, enque in slow queue, otherwise in fast queue If flow exceeds maximum allowed fast occupancy, enque in slow queue, otherwise in fast queue

Demonstration Enque Two flows in the fast queue Two flows in the fast queue Red flow bottleneck Red flow fills the buffer until packet drop-out Red flow exceeds target occupancy Red flow exceeds target occupancy Next packet in slow queue Next packet in slow queueDeque Simulate generalized processor sharing Simulate generalized processor sharing Fast queue Slow queue

Issues Incremental, but Plenty of potential pitfalls Plenty of potential pitfalls Inter-related and conflicting issues Inter-related and conflicting issues Fast queuing delays Classification Scalability and precision Scalability and precisionAlso Fairness between fast and slow queue Fairness between fast and slow queue Packet re-ordering Packet re-ordering Can affect sending rates Flow goodput Flow goodput

Trace 6 – Fast Delays

Talk Overview Bandwidth allocation Play-back buffer Quality-of-Service (QoS) DRE implementation Conclusions

Middleware implementation Sophisticated commercial DRE Issues Embedded devices with limited memory, computation, power Embedded devices with limited memory, computation, power Support for real-time protocols Support for real-time protocols Support for network QoS Support for network QoS Incorporate research contributions Incorporate research contributions E.g., bandwidth allocation, buffers On-going work

Talk Overview Bandwidth allocation Play-back buffer Quality-of-Service (QoS) DRE implementation Conclusions

Conclusions Sustainable presence on planetary surface Human-robotic missions Human-robotic missions E.g. construction, maintenance E.g. construction, maintenanceNeeds Higher performance Higher performance Multiple assets Multiple assetsImplications Network research Network research Distributed QoS Middleware research Middleware research Resource allocation Buffers Embedded implementation Middleware research and development fits between Networks Networks Intelligent systems Intelligent systems