Bull Sas

Le Commissariat a L'energie Atomique Et Aux Energies Alternatives

Commissariat a L'energie Atomique Et Aux Energies Alternatives

Pierre Seroul

Kaoutar Sghiouer

Jean-François Roy

Guillaume Porcher

Emeric Dynomant

Stéphane Pralet

Maxence Menager

HÉLÈNE Schricke

Jean-Francois BerrÉ

Jacques Martin

HervÉ Barancourt

A method for determining at least one machine involved in an anomaly detected in a computing infrastructure comprising a plurality M of machines. This determination is performed by associating with each machine a state vector constructed using metrics and logs. Each vector is also associated with a time window. The monitoring of these vectors makes it possible to detect an anomaly that is then explained via an explanation method that makes it possible to assign an involvement indicator to each machine. The use of this indicator makes it possible to determine the machine most involved.

Method and device for determining at least one machine involved in an anomaly detected in a complex computing infrastructure

A method for scheduling computing tasks on a supercomputer including offline reinforcement learning (OFRL) of a scheduler on a database (LDB). The database includes at least one execution history (HIST) that includes the state (LHPCS) of a learning supercomputer at several moments (T, T−1); the actions (LACT) related to the scheduling of learning tasks on the learning supercomputer at those moments (T); and a reward (REW) related to each task. The method also includes the use of the scheduler trained on the computing tasks to be scheduled.

Method for scheduling a set of computing tasks in a supercomputer

A method of representing a distributed computing system, the distributed computing system comprising a plurality of processing devices connected together according to a predefined topology. The method comprising receiving at least one piece of data from an activity log file relating to at least one processing device among the plurality of processing devices, receiving at least one metric relating to at least one processing device among the plurality of processing devices, receiving at least the predefined topology of the distributed computing system, constructing a graph representative of a distributed computing system operation, the graph comprising the data item extracted from the received log file, the received metric, and the received topology, and embedding at least one part of the graph to obtain at least one state vector representing the at least one part of the embedded graph.

Method for representing a distributed computing system by graph embedding

The invention relates to a device and a method () for determining a technical incident risk value in an infrastructure (), said method comprising:

Method and device for determining a technical incident risk value in a computing infrastructure from performance indicator values

A method for determining an estimated duration before a technical incident in a computing infrastructure, executed by a computing device. The computing device including a data processing module, a storage module that stores in memory at least one correlation base between performance indicators, wherein the correlation base includes values of duration before becoming anomalous between correlated performance indicators, and a collection module. The method includes receiving performance indicator values, identifying anomalous performance indicators, identifying first and other at-risk indicators. The method includes determining an estimated duration before a technical incident including a calculation, from the anomalous indicators and at-risk indicators, a shorter path leading to a risk of technical incident, and a calculation of an estimated duration before a technical incident. The estimated duration is calculated from the values of duration before becoming anomalous between correlated performance indicators for each of the performance indicators constituting the shortest path calculated.

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Sinard, France

Pierre Seroul