Abstract: The disclosed technology teaches configuring and reconfiguring an application running on a system, receiving a test configuration file with performance evaluation criteria and bounds for configuration dimensions defining a configuration hyperrectangle. The technology includes instantiating a reference instance and a test instance, subject to similar operating stressors and automatically testing alternative configurations within the configuration hyperrectangle, configuring and reconfiguring components of the test instance in the test cycles at configuration points within the configuration hyperrectangle, and applying a test stimulus to both instances for a dynamically determined cycle time.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.