Lightbend, Inc.

Paypal, Inc.

Opsclarity Inc.

Ebay, Incorporated

Alan Honkwan Ngai

Amit Sasturkar

Omer Emre Velipasaoglu

Jean-Christophe Martin

Jian Xu

Rami El-Charif

Connie Wong Yang

Vishal Surana

Abhinav Amrut Vora

Timothy Gordon Kimmet

Jiang Xu

Oliver Kempe

Aiyesha Lowe Ma

Narayanan Arunachalam

Taiping Yu

Fei Li

Xin Li

Xu Chu Jiang

Arun Kejariwal

Uday K Chettiar

Cheng Xu

Craig David Upson

A provisioning machine may receive a request that an application be executed while distributed according to a distribution constraint among various devices. The provisioning machine may access a topological model that represents multiple devices configured as a single cloud-based application server and defines a first group of devices that have the same redundancy status (e.g., active or backup). In addition, the topological model may define a second group of devices that have the same functional role (e.g., executing a particular component of the application). A device may be a member of both the first group and the second group. The provisioning machine may determine a size of the first group according to the distribution constraint. Based on the determined size of the first group, the provisioning machine may configure (e.g., provision) the first group of devices as a subset of the multiple devices of the server.

Provisioning of devices

The technology disclosed relates to organizing network performance metrics into historical anomaly dependency data. In particular, it relates to calculating cascading failure relationships between correlated anomalies detected in a network. It also relates to illustrating to a network administrator causes of system failure by laying out the graph to show a progression over time of the cascading failures and identify root causes of the cascading failures. It also relates to ranking anomalies and anomaly clusters in the network based on attributes of the resources exhibiting anomalous performances and attributes of the anomalous performances. It further relates to depicting evolution of resource failures across a network by visually coding impacted resources and adjusting the visual coding over time and allowing replay over time to visualize propagation of anomalous performances among the impacted resource.

Ranking network anomalies in an anomaly cluster

The technology disclosed relates to differential analysis of sets of time series pairs. In particular, it relates to building estimators of magnitude of difference between two time series. After the basic estimators are built, they are combined into ensemble estimators using linear or nonlinear prediction models to improve their accuracy. In one application, the ensemble is used for estimating the magnitudes of difference over sets of metric pairs observed from distributed applications and systems running over a computer network. The metric pairs are then ranked in decreasing order of magnitude of difference to guide an operator in prioritizing his root cause analysis of faults, thereby reducing the time-to-resolution of problems.

Resource and metric ranking by differential analysis

The technology disclosed relates to maintaining up to date software version data in a network. In particular, it relates to accessing a network topology that records node data and connection data including processes running on numerous hosts grouped into local services on the hosts, the local services running on multiple hosts grouped into service clusters and sub-clusters of service clusters, and network connections used by the service clusters to connect the hosts grouped into service connections. It further relates to collecting current software version information for the processes, updating the network topology with the current software version for particular process running on a particular host when it differs from a stored software version in the network topology, reassigning the particular host to a sub-cluster within the service cluster according to the current software version, and monitoring the updated sub-cluster within the service cluster.

Systems and methods of monitoring a network topology

The technology disclosed relates to sub-clustering within service clusters in real-time. In particular, it relates to accessing a network topology that records node data and connection data including processes running on numerous hosts grouped into local services on the hosts, the local services running on multiple hosts grouped into service clusters and sub-clusters of service clusters, and network connections used by the service clusters to connect the hosts grouped into service connections, wherein the node data includes software versions of the processes and process data with configuration files and clustering the multiple hosts with the service clusters into the sub-clusters based at least in part on the software versions.

Systems and methods of constructing a network topology

Organizing network performance metrics into historical anomaly dependency data

A query language used to traverse a path in a graph-based model is provided. The system comprises a parsing module to parse a query comprising query phrases. The respective query phrases composed of an object-type identifier and a filter where the object-type identifier corresponds to respective repositories and the filter is used to identify at least a portion of the objects associated with the object-type in the respective repositories. A planning module identifies the respective repositories corresponding to the object-type identifier within each of the one or more query phrases and generates an execution plan describing data to be retrieved according to the respective repositories and results of a preceding query phrase within the query. A translation module translates the respective query phrases into respective translated queries that are readable by the respective repositories and including the filter. An execution module implements the execution plan using the translated queries.

Query language to traverse a path in a graph

Growing community of inventors

Santa Clara, CA, United States of America

Alan Honkwan Ngai