Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
IEEE Transactions on Parallel and Distributed Systems
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
A comparison of list schedules for parallel processing systems
Communications of the ACM
Real-Time Systems
Hypertool: A Programming Aid for Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
Strength and Weaknesses of Genetic List Scheduling for Heterogeneous Systems
ACSD '01 Proceedings of the Second International Conference on Application of Concurrency to System Design
Probabilistic performance guarantee for real-time tasks with varying computation times
RTAS '95 Proceedings of the Real-Time Technology and Applications Symposium
Statistical Rate Monotonic Scheduling
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Analysis, evaluation, and comparison of algorithms for scheduling task graphs on parallel processors
ISPAN '96 Proceedings of the 1996 International Symposium on Parallel Architectures, Algorithms and Networks
Stochastic Scheduling of a Meta-task in Heterogeneous Distributed Computing
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
Probabilistic analysis and scheduling of critical soft real-time systems
Probabilistic analysis and scheduling of critical soft real-time systems
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This paper presents a hybrid technique that combines List Scheduling (LS) with Genetic Algorithms (GA) for constructing non-preemptive schedules for soft real-time parallel applications represented as directed acyclic graphs (DAGs). The execution time requirements of the applications' tasks are assumed to be stochastic and are represented as probability distribution functions. The performance in terms of schedule lengths for three different genetic representation schemes are evaluated and compared for a number of different DAGs.The approaches presented here produce shorter schedules than HLFET, a popular LS approach for all of the sample problems. Of the three genetic representation schemes investigated, PosCT, the technique that allows the GA to learn which tasks to delay in order to allow other tasks to complete produced the shortest schedules for a majority of the sample DAGs.