Abstract: Systems and methods for deploying dependent updates include mechanisms for requiring that dependency information be provided for each update so that dependent updates may be identified. Update dependencies are tracked so that dependent updates are not deployed until parent updates have been completed. Deployment sequencing is implemented on top of existing asynchronous deployment policies so that asynchronous workflow remains intact and unaltered. The asynchronous workflow is upgraded to a synchronous (i.e., sequential) workflow for updates having dependencies to ensure that updates having dependencies are applied in the correct order.

Abstract: A data processing system is implemented for detecting changes to infrastructure components, and extracting metadata associated with the changes. The data processing system also implements grouping the changes based on the metadata, ranking the groups of changes based on past incidents of service outages, and displaying the ranked groups of changes to a user.

Abstract: A data processing system implements scalable, secure, and reliable targeted release (TR) deployments of updates in cloud-based service. The TR deployment framework is provided that solves the technical problem of facilitating deployment of updates to targeted release customers. TR customers are associated with pre-deployment requirements (PDRs) associated with accreditation and/or validation tasks that must be completed before the update may be deployed to the customer base of the TR customers. A subset of the TR customer base is provided with segregated access to complete these accreditation tasks and/or validation tasks before the update is provided to the remainder of the userbase of the TR customer. This approach ensures that the industry standards and/or customer requirements are met before the update is deployed to the entire userbase of the TR customer.

Abstract: A data processing system implements managing the deployment of updates to a cloud-based service by deploying an update to one or more components of a cloud-based service according to a deployment plan. The deployment plan defines a plurality of stages in which the update is deployed to a subset of the components associated with a different subset of users of a userbase. The system implements receiving signal data that includes information regarding the performance of the update at each stage of the deployment plan; analyzing the signal data to determine whether one or more trigger conditions of halt and recovery rules associated with the update have been satisfied; and automatically halting deployment of the update to the one or more components of the cloud-based service responsive to at least one of the trigger conditions associated with the halt and recovery rules associated with the update having been satisfied.

Abstract: Systems and methods for creating and deploying jobs in a cloud-based service include receiving a job definition defining job attributes pertaining to a job to be deployed in a cloud-based service. The job definition is processed to determine whether the job definition complies with predefined rules for job creation in the cloud-based service. Based on the determination of whether the job definition complies with the predefined rules for job creation in the cloud-based server, the job may be validated or not validated in the cloud-based service. Deployment policies are determined for validated jobs based on the job definition for the validated job.

Abstract: Disclosed herein is a system for improving the user experience in the face of a regression by returning resources that offer a service to a “last known good” upgrade. In other words, the state of the resources is reconfigured to scale back from recent upgrade(s), the deployments of which likely caused the regression, to a previous upgrade that is known to have little or no effect on the user experience. To identify a problem, the system collects performance data from different resource units that make up a cloud-based platform. The performance data is collected for each upgrade event in a sequence of upgrade events that are currently deployed or being deployed. The system continually tracks and analyzes qualification data collected for each of the deployed upgrade events. The system can tag an upgrade event as the last known good upgrade event when the collected qualification data satisfies predefined qualifications.

Abstract: Please replace the Abstract of the Disclosure with the following Abstract showing all changes relative to the previous version of the Abstract (In the replacement Abstract, the header and footer have been marked out):

Abstract: In non-limiting examples of the present disclosure, systems, methods, and devices for detecting and classifying service issues associated with a cloud-based service are presented. Operational event data for a plurality of operations associated with the cloud-based application service may be monitored. A statistical-based unsupervised machine learning model may be applied to the operational event data. A subset of the operational event data may be tagged as potentially being associated with a code regression, wherein the subset comprises a time series of operational event data. A neural network may be applied to the time series of operational event data, and the time series of operational event data may be flagged for follow-up if the neural network classifies the time series as relating to a positive code regression category.

Abstract: A data processing system implements obtaining a set of first input parameters associated with a first update to be deployed to one or more components of a cloud-based service; providing the set of first input parameters to a machine learning model to obtain a first deployment policy for the first update; analyzing the set of first input parameters using the machine learning model to generate the first deployment policy, the machine learning model being trained to analyze input parameters associated with an update to be deployed to the cloud-based service and to generate a deployment policy for the update, the deployment policy identifying a set of rings for deploying the update and when the update is to be deployed to a subset of the userbase of the cloud-based service associated with that ring; and executing the first deployment policy to deploy the update to the one or more components.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for detecting and classifying service issues associated with a cloud-based service are presented. Operational event data for a plurality of operations associated with the cloud-based application service may be monitored. A statistical-based unsupervised machine learning model may be applied to the operational event data. A subset of the operational event data may be tagged as potentially being associated with a code regression, wherein the subset comprises a time series of operational event data. A neural network may be applied to the time series of operational event data, and the time series of operational event data may be flagged for follow-up if the neural network classifies the time series as relating to a positive code regression category.

Abstract: A data processing system implements obtaining policy information for an update to be deployed to one or more components of a cloud-based service identifying a plurality of rings of the cloud-based service to which the update is to be deployed; analyzing the policy information to determine configuration information for a plurality of installer instances each associated with one or more rings or one or more stages of a ring to which the update is to be deployed; obtaining payload information for each respective installer instance, the payload information indicating one or more payloads associated with the update to be deployed to the respective one or more rings or one or more stages of the ring associated with the respective installer instance; and deploying the one or more payloads for each of the one or more rings or the one or more stages of the ring associated with each installer instance.

Abstract: A data processing system implements obtaining a set of input parameters associated with an update to be deployed to a plurality of server farms of a cloud-based service, wherein each server farm includes a primary replica configured to handle user traffic and a disaster recovery replica configured to handle user traffic responsive to a failure of the primary replica; determining temperature information for each of the server farms, ranking the server farms based on the temperature information to determine an order in which an update is to be deployed to the server farms; iteratively deploying the updates to the primary replicas of the server farms according to the ranking until an deployment threshold has been satisfied; and iteratively deploying the updates to the primary replicas of server farms for which the primary replicas have not yet been updated and to the disaster recovery replicas of the server farms.

Abstract: Systems and methods for automatically detecting and mitigating errors in a cloud computing environment. One example method includes receiving, from a telemetry server, telemetry data for the cloud computing environment, detecting an error within the cloud computing environment based on the telemetry data, determining an error type for the error based on the telemetry data, determining an impact severity for the error based on the telemetry data, and when the error type is a reportable error type and the impact severity exceeds a predetermined threshold, performing a mitigation action.

Abstract: A data processing system implements obtaining a set of first input parameters associated with a first update to be deployed to one or more components of a cloud-based service; providing the set of first input parameters to a machine learning model to obtain a first deployment policy for the first update; analyzing the set of first input parameters using the machine learning model to generate the first deployment policy, the machine learning model being trained to analyze input parameters associated with an update to be deployed to the cloud-based service and to generate a deployment policy for the update, the deployment policy identifying a set of rings for deploying the update and when the update is to be deployed to a subset of the userbase of the cloud-based service associated with that ring; and executing the first deployment policy to deploy the update to the one or more components.

Abstract: Systems and methods for automatically recovering from software regression in a cloud computing environment. One example method includes determining, with an electronic processor, that a batch software update has been applied to the cloud computing environment. The method includes, in response to determining that a batch software update has been applied, transmitting a problem request to an event listener server. The method includes receiving, from the event listener server, a problem statement including a stack trace. The method includes determining, based on the stack trace, a software feature indicator. The method includes transmitting the software feature indicator to a root cause analyzer. The method includes receiving, from the root cause analyzer, a change list indicator and a relevancy score associated with the change list indicator. The method includes performing a mitigation action based on the change list indicator when the relevancy score exceeds a relevancy threshold.

Abstract: Systems and methods are described for verifying functionality of software. A set of code that is to be validated is identified. A first configuration is determined for the set of code that configures the code as a first build for validation. The first build is released for a first validation process. Prior to completion of validation of the first build, a second configuration is determined for the set of code that configures the code as a second build for validation. The second build is released for a second validation process prior to completion of validation of the first build. The first and second validation process are staged so that the first and second builds can be reverted independently of one another in the event of a validation issue. The first and second validation process are independently completed in the absence of a validation issue.

Abstract: Traditionally, when a feature is updated or a new feature is released, the feature undergoes internal testing and validation before external distribution. However, some features may receive proportionately less internal usage than customer usage reflected externally. Low internal usage of features can lead to weak telemetry data, which can allow code regressions (e.g., bugs) to go undetected until the features are released to customers. Accordingly, accelerated internal feature usage is provided to mirror external customer usage. Highly used features are dynamically identified and, any deficiencies in internal feature usage are identified. Tenant sites estimated to generate at least a portion of the deficiency in feature usage are identified. These sites may be migrated or replicated to internal validation rings to generate additional internal feature usage.

Abstract: A method of and system for selecting users for a rollout process of a feature is carried out by receiving an indication of the rollout process for the feature being rolled out, accessing a rollout plan, the rollout plan including a plurality of stages for the rollout process, and selecting users from a user population for each of the plurality of stages of the rollout process. Selecting the users from a user population includes examining a property to determine if a user in the user population is indicated as opted into being a late-stage receiver, and upon determining that the user is opted into being the late-stage receiver, excluding the user from the user population for one or more early stages of the rollout and including the user into the user population in one or more late stages of the rollout process.

Abstract: The techniques disclosed herein enable systems to safely deploy a plurality of upgrade variants to different resource units that provide a service by utilizing small-scale deployment and validation. To deploy upgrade variants, a system receives a selection of upgrade variants from a feature group and automatically selects an appropriate set of resource units at which to deploy the upgrade variants. The system is further configured to collect and analyze telemetry data from the set of resource units to determine if any problems have occurred as a result of the deployed upgrade variants. By analyzing the telemetry data, the system can also identify one or more upgrade variants that are causing the problems. In response, the system can remove the identified variants and proceed with deployment of the remaining upgrade variants.

Abstract: Disclosed herein is a system for automating the causality detection process when upgrades are deployed to different resources that provide a service. The resources can include physical and/or virtual resources (e.g., processing, storage, and/or networking resources) that are divided into different, geographically dispersed, resource units. To determine whether a root cause of a problem is associated with an upgrade event that has recently been deployed, a system is configured to use telemetry data to compute an upgrade-to-upgrade score that represents differences between two different upgrade events that are deployed to the same resource unit. The system is further configured to use telemetry data to compute an upgrade unit-to-unit score that represents differences between the same upgrade event being deployed to two different resource units. The scores can be used to output an alert, for an analyst, that signals whether a recently deployed upgrade event is the cause of a problem.