Abstract: A server implementing a service receives a request from a client computing system and stores the request in a cache, along with a correlation identifier. The server returns a response to the request, along with a correlation identifier (ID). When a trigger input is received, indicating that a problem has been encountered at the client computing system, a diagnostic data generation system on the service accesses the cache and generates diagnostic data for the request corresponding to the correlation ID. The diagnostic data corresponding the correlation ID is then provided to a support data storage system where it can be combined with diagnostic data from the client computing system, to be accessed by a support computing system. This reduces consumption of computing resources, such as processing time, memory initialization and/or network bandwidth.

Abstract: 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.

Abstract: 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.

Abstract: A server implementing a service receives a request from a client computing system and stores the request in a cache, along with a correlation identifier. The server returns a response to the request, along with a correlation identifier (ID). When a trigger input is received, indicating that a problem has been encountered at the client computing system, a diagnostic data generation system on the service accesses the cache and generates diagnostic data for the request corresponding to the correlation ID. The diagnostic data corresponding the correlation ID is then provided to a support data storage system where it can be combined with diagnostic data from the client computing system, to be accessed by a support computing system. This reduces consumption of computing resources, such as processing time, memory initialization and/or network bandwidth.

Abstract: 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.

Abstract: A server implementing a service receives a request from a client computing system and stores the request in a cache, along with a correlation identifier. The server returns a response to the request, along with a correlation identifier (ID). When a trigger input is received, indicating that a problem has been encountered at the client computing system, a diagnostic data generation system on the service accesses the cache and generates diagnostic data for the request corresponding to the correlation ID. The diagnostic data corresponding the correlation ID is then provided to a support data storage system where it can be combined with diagnostic data from the client computing system, to be accessed by a support computing system. This reduces consumption of computing resources, such as processing time, memory utilization and/or network bandwidth.

Abstract: A server implementing a service receives a request from a client computing system and stores the request in a cache, along with a correlation identifier. The server returns a response to the request, along with a correlation identifier (ID). When a trigger input is received, indicating that a problem has been encountered at the client computing system, a diagnostic data generation system on the service accesses the cache and generates diagnostic data for the request corresponding to the correlation ID. The diagnostic data corresponding the correlation ID is then provided to a support data storage system where it can be combined with diagnostic data from the client computing system, to be accessed by a support computing system. This reduces consumption of computing resources, such as processing time, memory utilization and/or network bandwidth.

Abstract: 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 computin

Abstract: 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.

Abstract: 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 computin

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.