Fault-tolerant computing: theory and techniques; vol. 1
Fault-tolerant computing: theory and techniques; vol. 1
A fault-tolerant scheduling problem
IEEE Transactions on Software Engineering
The Deferrable Server Algorithm for Enhanced Aperiodic Responsiveness in Hard Real-Time Environments
IEEE Transactions on Computers
Fault-Tolerance Through Scheduling of Aperiodic Tasks in Hard Real-Time Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems
A Fault-Tolerant Dynamic Scheduling Algorithm for Multiprocessor Real-Time Systems and Its Analysis
IEEE Transactions on Parallel and Distributed Systems
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Modern Control Engineering
Resource Management in Real-Time Systems and Networks
Resource Management in Real-Time Systems and Networks
Adaptive Workload Management through Elastic Scheduling
Real-Time Systems
Elastic Task Model for Adaptive Rate Control
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Design and Evaluation of a Feedback Control EDF Scheduling Algorithm
RTSS '99 Proceedings of the 20th IEEE Real-Time Systems Symposium
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
ISADS '01 Proceedings of the Fifth International Symposium on Autonomous Decentralized Systems
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In this paper, we propose a feedback-based combined scheduling algorithm with fault tolerance for applications that have both periodic tasks and aperiodic tasks in real-time uniprocessor systems Each periodic task is assumed to have a primary copy and a backup copy By using the rate monotonic scheduling and deferrable server algorithm, we create two servers, one for serving aperiodic tasks and the other for executing backup copies of periodic tasks The goal is to maximize the schedulability of aperiodic tasks while keeping the recovery rate of periodic tasks close to 100% Our algorithm uses feedback control technique to balance the CPU allocation between the backup server and the aperiodic server Our simulation studies show that the algorithm can adapt the parameters of the servers to recover the failed periodic tasks.