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Predictive analytics as an essential mechanism for situational awareness at the ATLAS Production Systemстатья
- Авторы: Titov M., Gubin M., Klimentov A., Barreiro Megino F.H., Borodin M., Golubkov D.
- Сборник: Selected Papers of the 26th International Symposium on Nuclear Electronics and Computing (NEC 2017)
- Серия: CEUR Workshop Proceedings (CEUR-WS.org)
- Том: 2023
- Год издания: 2017
- Место издания: CEUR-WS.org
- Первая страница: 61
- Последняя страница: 67
- Аннотация: The workflow management process should be under control of a specific service that is able to forecast the processing time dynamically according to the status of the processing environment and workflow itself, and to react immediately on any abnormal behavior of the execution process. Such situational awareness analytic service would provide the possibility to monitor the execution process, to detect the source of any malfunction, and to optimize the management process. The stated service for the second generation of the ATLAS Production System (ProdSys2, an automated scheduling system) is based on predictive analytics approach. Its primary goal is to estimate the duration of the data processings (in terms of ProdSys2, it is task and chain of tasks) with possibility for later usage in decision making processes. Machine learning ensemble methods are chosen to estimate completion time (i.e., “Time-To-Complete”, TTC) for every (production) task and chain of tasks, and “abnormal” task processing times would warn about possible failure state of the system. This is the primary phase of the service development that also includes the strategy for its precision enhancement. The first implementation of such analytic service is designed around Task TTC Estimator tool and it provides a comprehensive set of options to adjust the analysis process and possibility to extend its functionality.
- Добавил в систему: Титов Михаил Анатольевич