Comparison of genetic representation schemes for scheduling soft real-time parallel applications

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. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Yoginder S. Dandass, Amit C. Bugde
• Keywords: Execution Time Requirements, GECCO 2006, Genetic Representation Schemes, Optimization, Soft Real-time Parallel