In this paper, we consider a scalable distributed-memory architecture for which we propose a problem representation that assigns real-time tasks on the processing units of the architecture to maximize deadline compliance rate. Based on the selected problem representation, we derive an algorithm that dynamically schedules real-time tasks on the processors of the distributed architecture. The algorithm uses a formula to generate the adequate scheduling time so that deadline loss due to scheduling overhead is minimized while deadline compliance rate is being maximized. The technique we propose proved to be correct in the sense that the delivered solutions are not obsolete, i.e., the assigned tasks to working processors are guaranteed to meet their deadlines once executed. The correctness criterion is obtained based on our technique to control the scheduling time. To evaluate the performance of the algorithms that we propose, we provide a number of experiments through a simulation study. We also propose an implementation of our algorithms in the context of scheduling real-time transactions on an Intel-Paragon distributed-memory multiprocessor. The results of the conducted experiments show interesting performance trade-offs among the candidate algorithms.
- Distributed real-time systems
- Performance evaluation
- Scheduling and resource allocation
ASJC Scopus subject areas
- Theoretical Computer Science
- Computational Theory and Mathematics