Microsoft Technology Licensing, LLC

Nagaraju Palla

Nagaraj Patil

Kiran Nallabothula

Ricardo Soares Stern

Raghavendra Bhuthpur

Narendra Babu Alagiriswamy

Christopher Barnes

Amanda A Alvarado

Suneetha Dhulipalla

Dylan Symington

Nicholas John Nehrhood

Rajmohan Rajagopalan

Doru Constantin Nica

Karan Singh Rekhi

Himanshu Misra

Srihari Busam

Sathish Kumar Venkat Rangam

Chris Barnes

Minjun Wu

Parikshit Patidar

Eddie W M Fong

Bhavin J Shah

MadhaviLatha Kaniganti

Katy Chen

Dheeraj Kumar Jain

Ning Zhuang

Hsien-chieh Cheng

Ming Yang

Xiaoyu Li

Eddie Fong

Mufaddal M Pratapgarhwala

Akash Bhatia

A computing system in a server environment receives a diagnostic data package, corresponding to a problem, from a client computing system and runs a problem-specific analyzer to generate additional problem-specific data. A root cause for the problem is identified along with a recovery action, based on the diagnostic data package and the additional problem-specific data. The root cause and recovery action are communicated to the client computing system.

Cloud-based recovery system

Technologies are described herein for changelog transformation and correlation in a multi-tenant cloud service. Components within the multi-tenant cloud service generate changelogs that describe changes made to hardware or software components within the multi-tenant cloud service. The changelogs are received and transformed from different schemas into a common schema. A central change management service ('CCMS') exposes a network service application programming interface ('API'), or other type of interface, through which other network services can obtain the changelogs that have been transformed into the common schema. For example, services can obtain changelogs in order to correlate changes to anomalies or other events taking place in the multi-tenant cloud service, to identify upstream or downstream components that might be impacted by a change, to provide a user interface for viewing the changelogs, the correlation, or the potential impact of a change, and/or to perform other types of functions.

Changelog transformation and correlation in a multi-tenant cloud service

Trace information representing a program object is automatically logged on a client computing system. It is uploaded to a service computing system where it is configured to be searchable on a property-by-property basis.

Object oriented data tracking on client and remote server

An assistance service through its local client application or agent at a user's device (or devices) may collect diagnostic related information associated with the user's operating environment (physical and software operation parameters and configurations) and monitor a health of one or more applications. Upon detecting an issue or being activated by the user, the local client application or agent may perform diagnostic and/or recovery actions. In some cases, the diagnostic related signals may be sent directly by the application being monitored or diagnosed to its hosting service or the assistance service. Upon detecting a disconnect of the application being monitored or diagnosed, the local client application or agent may determine an alternative server and transmit the diagnostic related signals to the alternative server. An alert indicating the disconnect of the application may also be sent.

Diagnostic and recovery signals for disconnected applications in hosted service environment

Incident information that describes a timing of an incident in a service, is received from a service. User activity information is received from a client system in a tenant. An impact of an incident, on a tenant, is identified by combining the incident information with the user activity information.

Detecting and surfacing user interactions

A method, performed by a computing system deployed in a server environment, comprises receiving, from a client computing device that is remote from the server environment, a problem scenario identifier that identifies a problem scenario indicative of a problem associated with the client computing device, identifying a problem-specific diagnostic analyzer, that is specific to the problem associated with the client computing device, based on mapping information that maps the problem scenario to the problem-specific diagnostic analyzer, running the problem-specific diagnostic analyzer to obtain problem-specific diagnostic data that is specific to the problem associated with the client computing device, the problem-specific diagnostic data including first data associated with the client computing device and second data associated with the server environment, identifying a suggested recovery action based on the problem-specific diagnostic data, and communicating the suggested recovery action to the client computing device.

Technologies are described herein for identification and presentation of changelogs relevant to a tenant of a multi-tenant cloud service. Change feature extraction is performed on changelogs associated with a tenant of the multi-tenant cloud service to identify features associated with the changelogs. Machine learning based classification can then be performed on the changelogs to classify the changelogs. Misclassification correction might also be performed on the classified changelogs. Machine learning can also be utilized to identify a subset of the changelogs as being relevant to the tenant. A user interface (UI) can then be generated and provided to the tenant that includes the subset of the changelogs. The tenant's interaction with the changelogs presented in the UI can be monitored and data describing the interaction can be used to modify machine learning models utilized for machine learning change classification and for determining the relevance of a changelog to the tenant.

Identification and presentation of changelogs relevant to a tenant of a multi-tenant cloud service

Route information identifying servers that an e-mail message travels through, along with per-hop latency information, is aggregated. A choke point or other latency-related issue is identified and a control signal is generated to control the e-mail system based upon the identified choke point or other latency-related issue.

Electronic mail system routing control

Historical telemetry data can be used to generate predictions for various classes of data at various aggregates of a system that implements an online service. An anomaly detection process can then be utilized to detect anomalies for a class of data at a selected aggregate. An example anomaly detection process includes receiving telemetry data originating from a plurality of client devices, selecting a class of data from the telemetry data, converting the class of data to a set of metrics, aggregating the set of metrics according to a component of interest to obtain values of aggregated metrics over time for the component of interest, determining a prediction error by comparing the values of the aggregated metrics to a prediction, detecting an anomaly based at least in part on the prediction error, and transmitting an alert message of the anomaly to a receiving entity.

Anomaly detection and classification using telemetry data

An assistance service through its local client application or agent at a user's device for devices) may collect diagnostic related information associated with the user's operating, environment (physical and software operation parameters and configurations) and monitor a health of one or more applications. Upon detecting an issue or being activated b the user, the local client application or agent may perform diagnostic and/or recovery actions. In some cases, the diagnostic related signals may be sent directly by the application being monitored or diagnosed to its hosting service or the assistance service. Upon detecting a disconnect of the application being monitored or diagnosed, the local client application or agent may determine an alternative server and transmit the diagnostic related signals to the alternative server. An alert indicating the disconnect of the application may also be sent

A problem with a computing system is detected, a root cause is identified and a solution is also identified. Diagnostic data is obtained and an issue signature is generated that maps the issue to failed components and product functionality.

Issue detection and signature generation

An assistance service through its local client application or agent at a user's device hardware and software environment information and monitor a health of one or more applications. Upon detecting an issue or being activated by the user, the assistance service may perform diagnostic and/or recovery actions. If predefined or dynamically determined recovery actions are inadequate to address the issue, the assistance service may engage a support service providing collected user device environment information and received server environment information to the support service. A history of attempted recovery actions and diagnostic results may also be provided. The assistance service may, through a user interface of its local, client or agent, facilitate communication with the supports service and perform recovery actions, suggested by the support service providing feedback to the support service. When needed, the assistance service may rollback a version of locally installed or server-side applications to address the issue.

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Bothell, WA, United States of America

Nagaraju Palla