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This paper describes how neural networks and support vector machines can be used to detect errors in a large scale distributed system, specifically the ATLAS Trigger and Data AcQuisition (TDAQ) system. By collecting, analysing and preprocessing some of the data available in the system it is possible to recognize and/or predict error situations arising in the system. This can be done without detailed...
This paper describes the new dynamic recovery mechanisms in the ATLAS Trigger and DataAcQuisition (TDAQ) system. The purpose of the new recovery mechanism is to minimize the impact certain errors and failures have on the system. The new recovery mechanisms are capable of analyzing and recovering from a variety of errors, both software and hardware, without stopping the data-gathering operations. An...
This paper describes the new dynamic recovery mechanisms in the ATLAS Trigger and Data AcQuisition (TDAQ) system. The purpose is to minimize the impact certain errors and failures have on the system. The new recovery mechanisms are capable of analysing and recovering from a variety of errors, both software and hardware, without stopping the data gathering operations. It incorporates an expert system...
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