Scheduling Tasks with Resource Requirements in Hard Real-Time Systems
IEEE Transactions on Software Engineering
Preemptive Scheduling of Real-Time Tasks on Multiprocessor Systems
Journal of the ACM (JACM)
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Template-Based Real-Time Dwell Scheduling with Energy Constraint
RTAS '03 Proceedings of the The 9th IEEE Real-Time and Embedded Technology and Applications Symposium
A resource allocation model for QoS management
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
Practical Solutions for QoS-Based Resource Allocation
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Scalable Resource Allocation for Multi-Processor QoS Optimization
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Radar Dwell Scheduling Considering Physical Characteristics of Phased Array Antenna
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Integrated Resource Management and Scheduling with Multi-Resource Constraints
RTSS '04 Proceedings of the 25th IEEE International Real-Time Systems Symposium
Finite-Horizon Scheduling of Radar Dwells with Online Template Construction
RTSS '04 Proceedings of the 25th IEEE International Real-Time Systems Symposium
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Tasks in many real-time applications can be scheduled by variations of rate monotonic or earliest deadline first algorithms. When this is possible, it is satisfying to have formal analysis and performance bounds underlying the use of these algorithms. However, in many applications the simultaneous set of constraints that must be satisfied makes these traditional solutions unsuitable. Practical solutions for these more complicated applications are important. In this paper we develop a novel integrated scheduling and allocation heuristic for a dual face phased array radar system. The realistic features of the radar system that must be simultaneously addressed include timeliness (worst case execution time, period, deadline), semantic importance, and physical constraints such as beam selection and frequency harmonics. The heuristic function we develop provides a very flexible way to incorporate these requirements into one single equation. Since scheduling high semantic importance tasks is paramount, we use the highest semantic importance tasks' success ratio as the major performance metric. Based on simulation results, we show that our static heuristic algorithm can schedule more than 91% of the highest semantic importance tasks at high frequency conflict degree even at heavy workloads. The result is 50% better than EDF and 31% better than an importance (IMP) based static priority scheduling algorithm where IMP is similar to various current approaches. For the online scheduling algorithm, our heuristic algorithm is 30% better than EDF and 20% better than IMP in terms of highest semantic importance tasks' success ratio at heavy workloads.