Microsoft Technology Licensing, LLC

Bhavin J Shah

Nagaraju Palla

Ricardo Soares Stern

Rajmohan Rajagopalan

Karan Singh Rekhi

Parikshit Patidar

Eddie W M Fong

Narendra Babu Alagiriswamy

Mufaddal M Pratapgarhwala

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

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

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Bhavin J Shah