An Instance-based Learning Approach for Predicting Execution Times of Parallel Applications
نویسندگان
چکیده
A new approach for predicting execution times of parallel applications is presented. The main goal is to improve decision making in parallel systems, providing the system scheduler with knowledge about parallel applications. A model is produced searching for similar applications on based experience, allowing effortless knowledge updating when new information occurs. Workload traces from three computing centers are used to evaluate the model. The model achieves prediction errors on mean application execution times between 38% and 57%. Obtained results are compared with previous work; using a trace-driven simulator it is showed how the model can improve scheduling decisions on parallel systems.
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