Advanced Quality of Service Adaptation in UAV Systems

1. I P B I P B I B B B B B B P B B B B B B B B B B B B B B B I I B B P B P IBBPBBPBBPBBPBBPBBIBBPB B P B P B P B I P B I P P P P P P P B B P P P I I B B B B B B B B B B B B B B B B B B B B B B B B B B B B P P B B B B B B B B B B B B B B B B B B B B B B B B B ...PBBPBBPBBI II I Current Adaptation responses Variations in Mission Requirements • NETWORK RESERVATION • Under excessive Network load - Use IntServ to reserve bandwidth • LOAD BALANCING • Excessive CPU load - Move distributor to more lightly loaded host • Timeliness • Pilot or targeting officer must have an out-of-the-window view of UAV imagery • Quantity • Surveillance officer must record complete UAV imagery for off-line analysis Variations in Operating Conditions • DATA FILTERING • Excessive network or CPU load - Drop selective frames

2. System-wide QoS Application or Domain-specific QoS Common Middleware Service QoS Distribution Middleware QoS Operating System QoS Network QoS Layers of QoS Specification & Adaptation in UAV Systems Candidate Technologies Emerging Alternatives Adaptive feedback loops can run at multiple layers Mission doctrine contracts (TBMD, AAW, CFF) UAV capabilities CEC/SIAP A/V Streaming Service, HiperD RM, Open QoS Testbed RM, RT ARM, DeSiDeRata, QuO, Proteus QuO Gateway RT CORBA (ACE+TAO), Distributed RT Java Linux RK RT Java Hybrid & MultiChannel IntServ & DiffServ

3. Hyper-D Resource Manager Common Middleware Service QoS ACE/TAO RT ORB ACE/TAO RT ORB ACE/TAO RT ORB Distribution Middleware QoS AQoSA AQoSA Current Layers of QoS Specification& Adaptation in Navy UAV Systems Video Source Video Display Video Forwarding Code Application or Domain- specific QoS QoS Adaptive Control QoS Adaptive Control A/V Streaming Service A/V Streaming Service Operating System QoS IntServ/RSVP IntServ/RSVP Network QoS

4. Video Display AQoSA AQoSA AQoSA ACE/TAO RT ORB ACE/TAO RT ORB ACE/TAO RT ORB AdHoc Integration of Components for QoS Adaptation and Control Video Source Video Forwarding Code Distributed Resource Management Coordination Control Path Control Path Data Path Data Path A/V Streaming Service A/V Streaming Service Virtual Information Collection A/V Streaming Service IntServ/RSVP IntServ/RSVP IntServ/RSVP Operating System Operating System Operating System Wireless Network LAN Network

5. UAV/HIPER-D Requirements (Previous Experiment) • Low latency to support interaction (users see images at the same time as the UAV) • Displayed frame rate can be less than 30/second, providing that targets remain clear and no jitter • HIPER-D Resource Manager determines where and when applications run • Management techniques focused on discrete problem and remedy • New experiments will extend these basic ideas: • Individual and composite bottleneck identification and adaptations • End-to-end behavior • Aggregate and Coordinated behavior • Scaling and Redundancy • Varied anomalies and operating conditions • More resources under control/coordination, including “soft” resources • Intercluster coordination and feedback

6. UAV demonstration illustrates some of the software engineering challenges with reusing and adding QoSto current off-the-shelf component software • We used an off-the-shelf video player in the UAV demonstration • Developed for playing MPEG video from a file • Had to convert it to accept input from a stream • Developers of the video player had recognized the need for adaptation to handle changes in QoS • The video player included code to compensate for slow video cards (i.e., falling behind in the video) • Unfortunately, this code is intertwined throughout the functional code (i.e., there is no separation of concerns) • Reusing this code presented some challenges because the QoS code was intertwined and specific to a different use-case • We had to “turn off” the file-specific adaptation in order to use the video player effectively with a video stream • This was difficult because the adaptive code was intertwined throughout the functional code • In contrast, the adaptive code specified separately in the QuO middleware was easy to change

7. QoS Doctrine Measured QoS Expected QoS Interceptor Interceptor Monitors Monitors Resource Management Service Correlate Probes Infer/Adapt Integrate Feedback Loop Piggybacked Measurements Translate Status Monitors Monitors Interceptor Interceptor Resource Resource Resource Applying Reflection to Optimize Multi-level Resource Management Applying reflection as an optimization is even more relevant to middleware than compilers due to dynamism & global resources: • Key System Characteristics • Integrate observing & predicting of current status & delivered QoS to inform the meta-layer • Meta-layer applies reflection to adapt system policies & mechanisms to enhance delivered QoS Client Object Collect ORB endsystem ORB endsystem

8. Combined System-level & Application-level Management Feedback Client Object End-to-End Application-centric Feedback End-to-End Application-centric Feedback Local Resource- centric Feedback Local Resource- centric Feedback End-to-End Network-centric Feedback End-to-End Network-centric Feedback Key Research Challenge:Providing & Organizing QoS Guarantees for Multiple Adaptive Feedback Loops Solution Approach • Multi-level distributed resource management middleware • Support stable QoS at varying granularity & scope levels for integrated, multi-property feedback paths across different locations & time scales • Patterns, protocols, & architectures needed to integrate COTS components

9. System-wide QoS ACE/TAO RT ORB ACE/TAO RT ORB ACE/TAO RT ORB ACE/TAO RT ORB ACE/TAO RT ORB IntServ/RSVP IntServ/RSVP IntServ/RSVP IntServ/RSVP IntServ/RSVP IntServ/RSVP Operating System Operating System Operating System Operating System Operating System Operating System Integrated Adaptive System Concept Application or Domain-specific QoS Common Middleware Services QoS Distribution Middleware QoS Operating System QoS Network QoS