Ca, Inc.

Victor Muntés-Mulero

David Sanchez Charles

Smrati Gupta

Marc Solé Simó

Serguei Mankovskii

Erhan Giral

Marta Arias

Giorgio Stampa

Steven Lloyd Greenspan

Thomas Howard Ferrin

An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.

Efficient machine learning for network optimization

A multivariate clustering-based anomaly detector can generate an event for consumption by an APM manager that indicates detection of an anomaly based on multivariate clustering analysis after topology-based feature selection. The anomaly detector accumulates time-series data across a series of time instants to form a multivariate time-series data slice or multivariate data slice. The anomaly detector then performs multivariate clustering analysis with the multivariate data slice. The anomaly detector determines whether a multivariate data slice is within a cluster of multivariate data slices. If the multivariate data slice is within the cluster and the cluster is a known anomaly cluster, then the anomaly detector generates an anomaly detection event indicating detection of the known anomaly. The anomaly detector can also determine that a multivariate data slice is within an unknown cluster and generate an event indicating detection of an unknown anomaly.

Multivariate clustering-based anomaly detection

A multivariate path-based anomaly detection and prediction service ('anomaly detector') can generate a prediction event for consumption by the APM manager that indicates a likelihood of an anomaly occurring based on path analysis of multivariate values after topology-based feature selection. To predict that a set of metrics will travel to a cluster that represents anomalous application behavior, the anomaly detector analyzes a set of multivariate date slices that are not within a cluster to determine whether dimensionally reduced representations of the set of multivariate data slices fit a path as described by a function.

Multivariate path-based anomaly prediction

A digital currency driven channel assignment technique is disclosed. Each AP in a distributed network uses a channel selection manager and a distributed ledger to select channels according to a channel assignment and a digital currency associated with the distributed ledger. The digital currency incentivizes APs to make sacrifices in their channel selection for the benefit of the overall network while punishing APs that select channels selfishly and cause bandwidth interference.

Distributed ledger for wireless channel assignment

A non-intrusive system which monitors a data center by detecting electromagnetic waves alleviates a resource burden on components and is inexpensive to deploy and scale within a data center. The system detects waves using omnidirectional antennas positioned throughout the data center, thus alleviating the need to physically attach directional antennas to components. The system performs a learning phase wherein representations of detected waves are mapped to occurrence of events within the data center. Once the learning phase is complete, operation of existing network monitoring tools, such as agents and probes, may cease, and the system may begin monitoring for events based on the detected waves. The system may also analyze wave data prior to the occurrence of events to identify event prediction indicators, e.g. distinctive wave values or patterns, which may be used to predict the occurrence of an event.

Data center monitoring based on electromagnetic wave detection

As a network increases in size and complexity, it becomes increasingly difficult to monitor and record relationships between components in the network. The lack of knowledge regarding component relationships can make it difficult to adequately and timely perform analysis of network issues or conditions. As a result, automated generation of a context graph that displays relationships among both hardware and software components in a network can help keep pace with a growing network and improve network analysis. The context graph may be generated based, for example, on event data (alternately referred to as event indications) generated by network components and/or event monitoring agents and network topology information. Additionally, the context graph may be augmented to display inter-component relationships based on multi-event correlations. The context graph can be used to assist in troubleshooting network issues or performing root cause analysis.

Context graph generation

A first event notification is received. The first event notification is associated with a first event. Criteria is determined based, at least in part, on the first event notification. A first component is identified based, at least in part, on the criteria a component graph. An operational status associated with the first component is determined. It is determined not to generate a second event notification based, at least in part, on the operational status.

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Sant Feliu de Llobregat, Spain

Victor Muntés-Mulero