Ca, Inc.

Victor Muntes Mulero

Marc Sole Simo

David Sanchez Charles

Jaume Ferrarons Llagostera

Alberto Huelamo Segura

David Solans Noguero

Michal Zasadzinski

Josep Lluis Larriba Pey

Norbert Martinez Bazan

Sergio Gomez Villamor

In a datacenter setting, a summary of differences and similarities between two or more states of the same or similar systems are predicted. Initially, a Long Short-Term Memory (LSTM) neural network is trained with to predict a summary describing the state change between at least two states of the datacenter. Given a set of training data (at least two datacenter states that are annotated with a state change description), the LSTM neural network learns which similarities and differences between the datacenter states correspond to the annotations. Accordingly, given a set of test data comprising at least two states of a datacenter represented by context graphs that indicate a plurality of relationships among a plurality of nodes corresponding to components of a datacenter, the LSTM neural network is able to determine a state change description that summarizes the differences and similarities between the at least two states of the datacenter.

Descriptive datacenter state comparison

Embodiments of the present disclosure relate to a fault tolerant root cause analysis (RCA) system that is able to handle calculation failures during runtime. Calculations (e.g., evaluation of a diagnostic model for a specific component or device) that are performed during the RCA are integrated using different resources of the system under analysis. In order to make a final diagnosis, the resources exchange messages containing calculation inputs and outputs. Calculation problems due to calculation failures in a particular resource can be resolved efficiently which reduces resource utilization and minimizes failure propagation to other parts of the system. Accordingly, the system is able to recover and output a diagnosis even if some of the resources fail or generate problems.

Fault tolerant root cause analysis system

In a datacenter setting, annotations or descriptions of relevant parts or subgraphs corresponding to components in the datacenter are predicted. Given a set of training data (library of subgraphs seen in the past labeled with a textual description explaining why were they considered relevant enough to be placed in the historical database), the recurrent neural network (RNN) learns how to combine the different textual annotations coming from each relevant region into a single annotation that describes the whole system. Accordingly, given a set of input or test data (datacenter state modeled a context graph that is not annotated), the system determines which regions of the input graph are more relevant, and for each of these regions, the RNN predicts an annotation even in a previously unseen or different datacenter infrastructure.

Automatic datacenter state summarization

Incomplete state information for nodes of a datacenter is completed utilizing historical state information. A context graph is received having a plurality of nodes that correspond to components of the datacenter, each node including properties that correspond to the represented component. It is determined that at least one of the properties for a node is incomplete. A context hash is derived from the context graph and compared to a plurality of subgraph hashes. Based upon the comparison, one or more subgraphs are identified that are similar to the context hash. Where a single similar subgraph is identified, the property information for the single subgraph is imputed to the context graph to complete the incomplete information. Where more than one similar subgraph is identified, the property information for the identified similar subgraphs is aggregated and the aggregated property information is imputed to the context graph to complete the incomplete information.

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Barcelona, Spain

Victor Muntes Mulero