Microsoft Technology Licensing, LLC

Mangalam Rathinasabapathy

Rahul Nigam

Mark Raymond Gilbert

Patrick Simek

Roshane Silva

Harpreet Miglani

Xinghuo Zeng

Priyanka Gundeli

Nidhi Verma

Esha Sharma

Vinod Mukundan Menon

Srigopal Chitrapu

Tom Wunshe Tseng

Rakesh Patnaik

Baskar Narayanan

Vinod Menon

Mark R Gilbert

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.

Automated detection and classification of dynamic service outages

Traditionally, engineers (or developers) of a software provider may implement or maintain aspects of application services by executing jobs or joblets on computing resources of various scopes in a cloud computing environment. However, in some cases, executing certain types of engineer-initiated jobs may negatively impact customer experience and/or satisfaction with the application services. Automated policies for distributing engineer-initiated jobs increase validation testing, scope control, and deployment timing based on a dynamic risk analysis of each job. A multi-faceted approach to distributing engineer-initiated jobs ensures adequate regression testing (e.g., via ring validation and cool-down period) and facilitates controlled distribution (e.g., based on throttle, distribution timing, and deployment train position). An automatic override ensures critical customer outages can be resolved quickly and efficiently by bypassing at least some of the rules. Thereby, customer experience is improved and service interruptions and customer dissatisfaction are minimized when distributing engineer-initiated jobs for application services.

Automated rules for controlled distribution of program instructions

The techniques described herein facilitate scope-based certificate deployment for secure dedicated tenant access in multi-tenant, cloud-based content and collaboration environments. In some embodiments, a method is described that includes receiving an incoming authentication request from an access system, wherein the authentication request includes metadata, extracting the metadata from the authentication request, and processing the metadata to identify a tenant corresponding to the request. A tenant-specific certificate associated with the tenant is then accessed and provided to the access system for validation by a third-party certificate authority.

Scope-based certificate deployment

In non-limiting examples of the present disclosure, systems, methods and devices for monitoring telemetry data from a cloud-based application service are presented. Telemetry data for a plurality of operations for the cloud-based application service may be analyzed, wherein the analysis comprises comparing a first time series with a second time series, and where data from the second time series relates to operations that were executed prior in time compared with execution of operations related to the first time series. One or more operational changes in the cloud-based application service may be identified based on the analysis, and at least one telemetry monitor may be dynamically configured based on the one or more operational changes that were identified.

Dynamic monitoring on service health signals

Aspects of the present disclosure relate to systems and methods for deploying payloads in a cloud service. In one aspect, one or more payloads may be deployed to a plurality of sample servers. Each of the one or more payloads may include a plurality of files. A hash value may be generated for each file of the plurality of files. A master hash value may be generated for each payload from the generated hashes for each file of the plurality of files. It may be determined whether the one or more payloads have changed since a previous deployment of the one or more payloads. When it is determined that at least one payload of the one or more payloads has changed, the at least one changed payload may be deployed to a plurality of data servers.

Runtime deployment of payloads in a cloud service

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Bellevue, WA, United States of America

Mangalam Rathinasabapathy