Abstract: A system implements techniques for using traffic volume associated with a service in order to proactively manage the capacity of a resource that supports the service. More specifically, the traffic volume is used as an indicator to determine, or project, a change in capacity that may be needed to avoid the under-provisioning of the resource. Alternatively, the traffic volume is used as an indicator to determine, or project, a change in capacity that may be preferred by a tenant that operates the service to avoid the over-provisioning of the resource.

Abstract: A system implements techniques for efficiently determining that an update deployed by a foundational service has caused a regression based on an aggregate health determination associated with tenant services and/or cloud resource provider services that depend upon the foundational service. The deployment of the update is initiated by an entity (e.g., an engineering team) tasked with operating and/or managing the foundational service. Accordingly, the system described herein can generate and provide a communication, to the foundational service (e.g., entity), indicating that a regression has likely been caused by the update and/or instructing the foundational service to halt the deployment of the update.

Abstract: The techniques described herein automatically correlate the health of cloud resources to a broader health determination for an entity executing within, or supported by, a distributed computing environment. In contrast to the typical manual analysis that is required to make a broader health determination for a specific entity, the techniques generate and use a standard health model that can be applied, or scaled, to detect unhealthy scenarios across a variety of different entities with different owners (e.g., different tenants and/or different cloud resource providers). Furthermore, to meet varying owner perspectives on health, the techniques include a layer on top of the standard health model that enables an owner to provide input that customizes the standard health model for their own entity.

Abstract: The techniques described herein automatically correlate the health of cloud resources to a broader health determination for an entity executing within, or supported by, a distributed computing environment. In contrast to the typical manual analysis that is required to make a broader health determination for a specific entity, the techniques generate and use a standard health model that can be applied, or scaled, to detect unhealthy scenarios across a variety of different entities with different owners (e.g., different tenants and/or different cloud resource providers). Furthermore, to meet varying owner perspectives on health, the techniques include a layer on top of the standard health model that enables an owner to provide input that customizes the standard health model for their own entity.

Abstract: The techniques described herein automatically correlate the health of cloud resources to a broader health determination for an entity executing within, or supported by, a distributed computing environment. In contrast to the typical manual analysis that is required to make a broader health determination for a specific entity, the techniques generate and use a standard health model that can be applied, or scaled, to detect unhealthy scenarios across a variety of different entities with different owners (e.g., different tenants and/or different cloud resource providers). Furthermore, to meet varying owner perspectives on health, the techniques include a layer on top of the standard health model that enables an owner to provide input that customizes the standard health model for their own entity.

Abstract: The techniques described herein automatically correlate the health of cloud resources to a broader health determination for an entity executing within, or supported by, a distributed computing environment. In contrast to the typical manual analysis that is required to make a broader health determination for a specific entity, the techniques generate and use a standard health model that can be applied, or scaled, to detect unhealthy scenarios across a variety of different entities with different owners (e.g., different tenants and/or different cloud resource providers). Furthermore, to meet varying owner perspectives on health, the techniques include a layer on top of the standard health model that enables an owner to provide input that customizes the standard health model for their own entity.

Abstract: The present disclosure relates to methods and systems for monitoring release pipelines. The methods and systems calculate health metrics of release pipelines using telemetry data from the release pipelines. The methods and systems determine whether the release pipelines are healthy based on the health metrics. The methods and systems automatically trigger one or more actions in response to determining that the release pipelines are unhealthy and continue to monitor the release pipelines in response to determining that the release pipelines are healthy.

Abstract: The present disclosure relates to methods and systems for monitoring release pipelines. The methods and systems calculate health metrics of release pipelines using telemetry data from the release pipelines. The methods and systems determine whether the release pipelines are healthy based on the health metrics. The methods and systems automatically trigger one or more actions in response to determining that the release pipelines are unhealthy and continue to monitor the release pipelines in response to determining that the release pipelines are healthy.

Abstract: A system and method enabling automated data cleansing and scrubbing at the attribute level is disclosed. A consolidated view may be provided of the scrubbed data or narratives that gets promoted to a final copy and the data or narratives received from multiple sources on a single user interface.

Abstract: A system and method enabling automated data cleansing and scrubbing at the attribute level is disclosed. A consolidated view may be provided of the scrubbed data or narratives that gets promoted to a final copy and the data or narratives received from multiple sources on a single user interface.

Abstract: A method includes transmitting a configuration policy from a policy service to one or more managed computing devices. The configuration policy includes values of each of a plurality of configuration parameters and identifies a subset of configuration parameters to be reported to the policy service. The method also includes receiving report data at the policy service from at least one of the managed computing devices. The report data includes results of processing the configuration policy.

Abstract: A method of inventorying software and/or hardware. The method may be practiced for example in a network computing environment including at least one client and at least one server. The method includes requesting a rule file from the server. The rule file is received from the server. Information is gathered about the software and/or hardware at the client based on rules in the rule file. The results of gathering information about the software and/or hardware at the client is sent to the server.

Abstract: Managing software states using policy rules in a policy document in a distributed network. A policy document is generated and the policy document includes a set of policy rules defining one or more desired configurations of one or more pieces of software on the target devices. Target information is associated with the policy document, and the target information corresponds to a selected set of the target devices and includes information relating to characteristics of the software of the selected set of target devices. The set of policy rules included in the policy document is compared with other policy rules for the software. The policy document is modified as a function of the comparison and conflict resolution preferences. The modified policy document is provided for deployment to the selected set of the target devices.

Abstract: Applying policy rules to software installed on a device. A group of devices to receive policy rules for the software is identified. The devices belonging to the specified group is identified. A set of policy rules assigned to the devices in the specified group are identified. The policy rules assigned to the devices are aggregated into one or more policy documents. The one or more policy documents are received. The received policy documents are stored in a data store associated with the device. The set of policy rules specified by the received policy documents are applied to the software. A feedback is provided to the policy authority in response to the applying, and the feedback is indicative of whether the set of policy rules is applied to the software.

Abstract: A method of inventorying software and/or hardware. The method may be practiced for example in a network computing environment including at least one client and at least one server. The method includes requesting a rule file from the server. The rule file is received from the server. Information is gathered about the software and/or hardware at the client based on rules in the rule file. The results of gathering information about the software and/or hardware at the client is sent to the server.