Portable RK: A Portable Resource Kernel for Guaranteed and Enforced Timing Behavior
RTAS '99 Proceedings of the Fifth IEEE Real-Time Technology and Applications Symposium
Value vs. deadline scheduling in overload conditions
RTSS '95 Proceedings of the 16th IEEE Real-Time Systems Symposium
A resource allocation model for QoS management
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
Elastic Task Model for Adaptive Rate Control
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
On the Scheduling of Mixed-Criticality Real-Time Task Sets
RTSS '09 Proceedings of the 2009 30th IEEE Real-Time Systems Symposium
Towards the Design of Certifiable Mixed-criticality Systems
RTAS '10 Proceedings of the 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium
Resource Allocation in Distributed Mixed-Criticality Cyber-Physical Systems
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
An incremental approach to scheduling during overloads in real-time systems
RTSS'10 Proceedings of the 21st IEEE conference on Real-time systems symposium
Response-Time Analysis for Mixed Criticality Systems
RTSS '11 Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium
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Large variations in the execution times of algorithms characterize many cyber-physical systems (CPS). For example, variations arise in the case of visual object-tracking tasks, whose execution times depend on the contents of the current field of view of the camera. In this paper, we study such a scenario in a small Unmanned Aerial Vehicle (UAV) system with a camera that must detect objects in a variety of conditions ranging from the simple to the complex. Given resource, weight and size constraints, such cyber-physical systems do not have the resources to satisfy the hard-real-time requirements of safe flight along with the need to process highly variable workloads at the highest quality and resolution levels. Hence, tradeoffs have to be made in real-time across multiple levels of criticality of running tasks and their operating points. Specifically, the utility derived from tracking an increasing number of objects may saturate when the mission software can no longer perform the required processing on each individual object. In this paper, we evaluate a new approach called ZS-QRAM (Zero-Slack QoS-based Resource Allocation Model) that maximizes the UAV system utility by explicitly taking into account the diminishing returns on tracking an increasing number of objects. We perform a detailed evaluation of our approach on our UAV system to clearly demonstrate its benefits.