Microsoft Technology Licensing, LLC

Nidhi Verma

Rahul Nigam

Ankur Jauhari

Dmitry Valentinovich Kholodkov

Roberta Cannerozzi

Min Shao

Shobana Krishnamoorthy

Rohan Khanna

Sudharsan Ganesan

Le Chang

Zohar Raz

Divyachapan Sridharan Padur

Daniel Oh

Hans Christian Andersen

Darius Snapkauskas

Parminder Pal Singh

Pritvinath Obla

Mangalam Rathinasabapathy

Phillip I Beish

Harpreet Miglani

Andrei Nicolae

Henry Chen

Dylan Thomas Nunley

Amber Litteken

Erik Wahlstrom

Brian Gregory O'connor

Sandhya Shahdeo

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.

Deployment sequencing for dependent updates

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.

Techniques for deploying changes to improve reliability of a cloud service

A logging system for an orchestration system that implements a rollout service to deploy updates to a cloud service includes: an orchestrator service host computer hosting the rollout service; a service bus connecting the orchestrator service host computer with a network on which the cloud service is provided; and a database to which the rollout service records a log of a deployment of an update, the log comprising an entry for each of a number of subsets of network components supporting the cloud service. Each record in the log refers to a payload version deployed to a corresponding subset of the network components supporting the cloud service. In response to detection of an issue with the deployment of the update in a particular subset of the network components, the rollout service retains a record in the log of each payload version deployed by changing a payload version number to be negative in each record of the log prior to a restart of the deployment back to an earlier subset of the network components with a new payload version to avoid the issue.

Negative numbering to log web service update attempts

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.

Dark deployment of infrastructure cloud service components for improved safety

A method of and system for implementing a safe deployment of a first feature in a computing environment is carried out by receiving registration information for the first feature, the registration information including a second feature on which the first feature depends or a feature type on which the first feature depends. Based on the second feature or the feature type, a target build version that meets dependency requirements of the second feature or the feature type is then identified before obtaining a list of build versions for a plurality of computing devices in the computing environment. From among the plurality of computing devices, one or more computing devices that have build versions that meet the target build version are then identified before marking the identified computing devices as being available for deployment of the feature and advancing deployment of the feature to those computing devices.

Method and system for implementing safe deployment of features

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.

Automatically halting cloud service deployments based on telemetry and alert data

The techniques disclosed herein enable improved security as well as more scalable and reliable job execution by utilizing granular security boundaries and certificate-based authentication for all communication within cloud-based platforms. To manage a cloud-based platform, a system receives a plurality of jobs and associated certificates at a first security boundary that are to be executed at various resource units within a second security boundary. The system then authenticates each certificate before transmitting each job to its respective resource unit for execution. In addition, the system is further configured to monitor active certificates for compromise and accordingly isolate various security boundaries in the event of a security breach. By isolating portions of the cloud-based platform within security boundaries, the system can mitigate the impact of security breaches. Furthermore, certificate-based authentication addresses performance constraints to enable more efficient and scalable job execution.

Resource unit isolation for increased safety and security in cloud services

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.

Targeted release updates for cloud service deployments

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.

Partitioned deployment of updates to cloud service based on centerally updated configuration store

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.

Ranking changes to infrastructure components based on past service outages

Technologies are disclosed for improving the deployment of a cloud-hosted service. Before being deployed to a particular environment, a cloud-hosted service must be configured for that environment. Configuring a deployment includes determining which components to deploy, determining how to connect with external components, identifying onboarding procedures, etc. A dependency data model defines a hierarchy of components utilized by the cloud-hosted service. For each component in the hierarchy, configuration parameters define how to deploy that component. A list of configuration parameters that do not yet have values for a target environment may be generated and provided to a user. Values for these parameters may then be received. A configuration for the target environment is then generated based on the dependency data model and the received values. The dependency data model may inherit dependencies and configuration properties from ancestors in a hierarchy of dependency data models.

Cloud service component dependencies

A method of and system for customizing a rollout process of application features includes providing a list of one or more application features for display on a user interface screen, the software application features being application features that are scheduled for a staged rollout, enabling selection of one of the application features for enrolling in late-stage rollout or opting out of the staged rollout, receiving a request for enrolling a user entity in late-stage rollout or opting the user entity out of the staged rollout, storing a property associated with the user entity in a data store, the property indicative of the user entity enrolling in late-stage rollout or opting out of the staged rollout, accessing the stored property, when selecting a plurality of enterprises or users for the staged rollout of the application feature, and depending on the accessed property, selecting the user entity for late-stage rollout or not selecting the user entity for the staged rollout.

Method and system for providing customized rollout of features

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.

Tagging a last known good upgrade event for automatic rollback based on detected regression

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.

Dynamic ring structure for deployment policies for improved reliability of cloud service

Reliable feature deployment is provided. Features are evaluated for their readiness to be graduated and features that are ready to be graduated are added to a graduation list. When features are added to the graduation list, evaluation of the features is discontinued, thereby reducing the performance and runtime costs associated with deploying features. Furthermore, engineers can consult the graduation list to identify features that are ready for graduation, thereby reducing the risk of premature flight graduation. Data associated with features is analyzed to map the features to corresponding flights. In this way, when an engineer identifies a feature for graduation, the engineer is able to identify the corresponding flight (or flights) from the mapping. Even when a feature is selected for graduation, the present systems provide additional safeguards to ensure that corresponding flights are not improperly or prematurely graduated, thereby preventing a subpar customer experience.

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Redmond, WA, United States of America

Nidhi Verma