Cisco Technology, Inc.

Aparupa Das Gupta

Yathiraj B Udupi

Debojyoti Dutta

Xinyuan Huang

Marc Solanas Tarre

Rishabh Jain

Manoj Sharma

Rahul Ramakrishna

Kai Zhang

Johnu George

Amit Agarwal

Seth Mason

A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.

Intelligent anomaly identification and alerting system based on smart ranking of anomalies

The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.

Cloud resource placement optimization and migration execution in federated clouds

In one embodiment, a device in a network aggregates values for a set of key performance indicators (KPIs) for a system the network to form a plurality of KPI states. The device associates a plurality of observed performance metric values from the system with the KPI states. The device constructs a machine learning-based decision tree. Internal vertices of the decision tree represent conditions for the plurality of observed performance metric values and leaves of the tree represent the KPI states. The device predicts a KPI state by using the machine learning-based decision tree to analyze live performance metric values streamed from the system. The device generates a proactive alert based on the predicted KPI state.

Probabilistic and proactive alerting in streaming data environments

The present disclosure describes a method for virtual machine placement optimization based on generalized organizational scenarios. The method involves defining a variable matrix (wherein each entry of the variable matrix indicate whether a particular virtual machine is to be placed on a particular host server), a first set of variables (wherein each variable of the first set of variables indicate whether a particular host server has at least one virtual machine to be placed thereon), a second set of variables (wherein the second set of variables indicates for all possible pairs of host servers whether two particular host servers both have at least one virtual machine to be placed thereon). The method further involves determining a set of virtual machine to host server allocations by solving a constraints optimization problem over the first set of variables and the second set of variables based on a generalized organizational scenario.

Virtual machine placement optimization with generalized organizational scenarios

A method for assisting evaluation of anomalies in a distributed storage system is disclosed. The method includes a step of monitoring at least one system metric of the distributed storage system. The method further includes steps of maintaining a listing of patterns of the monitored system metric comprising patterns which previously did not result in a failure within one or more nodes of the distributed storage system, and, based on the monitoring, identifying a pattern (i.e., a time series motif) of the monitored system metric as a potential anomaly in the distributed storage system. The method also includes steps of automatically (i.e. without user input) performing a similarity search to determine whether the identified pattern satisfies one or more predefined similarity criteria with at least one pattern of the listing, and, upon positive determination, excepting the identified pattern from being identified as the potential anomaly.

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San Jose, CA, United States of America

Aparupa Das Gupta