Distributed Scheduling of Tasks with Deadlines and Resource Requirements
IEEE Transactions on Computers
Application of Real-Time Monitoring to Scheduling Tasks with Random Execution Times
IEEE Transactions on Software Engineering
Mechanisms for detecting and handling timing errors
Communications of the ACM
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Guest Editorial: A Review of Worst-Case Execution-TimeAnalysis
Real-Time Systems - Special issue on worst-case execution-time analysis
WCET Analysis of Superscalar Processors Using SimulationWith Coloured Petri Nets
Real-Time Systems - Special issue on worst-case execution-time analysis
Software Engineering: A Practitioner's Approach
Software Engineering: A Practitioner's Approach
Probabilistic performance guarantee for real-time tasks with varying computation times
RTAS '95 Proceedings of the Real-Time Technology and Applications Symposium
A Dynamic Real-time Benchmark for Assessment of QoS and Resource Management Technology
RTAS '99 Proceedings of the Fifth IEEE Real-Time Technology and Applications Symposium
RTSS '96 Proceedings of the 17th IEEE Real-Time Systems Symposium
Integrating Multimedia Applications in Hard Real-Time Systems
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Statistical Rate Monotonic Scheduling
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
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The dynamic distributed real-time applications run on clusters with varying execution time, so re-allocation of resources is critical to meet the applications's deadline. In this paper we present two adaptive recourse management techniques for dynamic real-time applications by employing the prediction of responses of real-time tasks that operate in time sharing environment and run-time analysis of scheduling policies. Prediction of response time for resource reallocation is accomplished by historical profiling of applications' resource usage to estimate resource requirements on the target machine and a probabilistic approach is applied for calculating the queuing delay that a process will experience on distributed hosts. Results show that as compared to statistical and worst-case approaches, our technique uses system resource more efficiently.