Vmware, Inc.

Arunvijai Sridharan

Gaurav Gupta

Vipul Chaudhary

Sandeep L Hegde

Vibhu Rastogi

Sidhartha Sahoo

Srinivas Kandula

Mohak Rajendra

Vimal Das Kammath Veerakumar

In one example, a request to migrate an application running on a first compute node to a second compute node with a change in a version of the application may be received. Migration information may be provided to a cloud-based Migration as a Service provider based on the request. An application migration component may be received from the cloud-based Migration as a Service provider based on the migration information. Further, the application migration component may be executed to migrate the application from the first compute node to the second compute node with the change in the version of the application.

Migration as a service-based applications migration

In one example, a request to migrate a server application running on a first server to a second server with a change in a version of the server application may be received. Migration information may be provided to a cloud-based Migration as a Service provider based on the request. An application migration component may be received from the cloud-based Migration as a Service provider upon validation of the migration information. The server application may be migrated from the first server to the second server with the change in the version of the server application using the application migration component and the cloud-based Migration as a Service provider. A first version of an agent application in a client may be upgraded with a modified version of the agent application using the application migration component.

Migration as a service-based server and agent applications migration

Techniques for monitoring health of an application based on historical application health data and application logs are disclosed. In one embodiment, the historical application health data and the historical application logs associated with a period may be obtained. The application may include multiple services running therein. Priority of services may be determined based on the historical application health data associated with a portion of the period. Priority of exceptions associated with each of the services may be determined based on the historical application health data and the historical application logs associated with the portion of the period. Further, an application regression model may be trained by correlating the priority of the services, the associated priority of the exceptions, and the corresponding historical application health data. The health of the application may be monitored by analyzing real-time application logs using tested application regression model.

Application health monitoring based on historical application health data and application logs

Techniques for determining dependencies between applications based on application logs are disclosed. In one embodiment, a system may include a log analyzer to receive application logs generated by a plurality of applications running in a computing environment and analyze the received application logs using a trained application inventory model to parse communication information of the plurality of applications. Further, the system may include an application management unit to determine dependency between a first application and a second application of the plurality of applications running on different compute nodes in the computing environment using the parsed communication information.

Application dependency determination based on application logs

Techniques for discovery of applications based on application logs are disclosed. In one embodiment, a system may include a log analyzer to receive application logs generated by a plurality of applications running in a computing environment and analyze the received application logs using a trained initialization model to parse information about the plurality of applications. Further, the system may include an application discovery unit to determine a presence of an application running on a compute node in the computing environment using the parsed information about the plurality of applications.

Application discovery based on application logs

An event-notification system provides for monitoring resource-status parameters so as to repeatedly generate resource-status values for each of N resource-status parameters applicable to the resources. Each datapoint specifies a resource, a time of collection, and the values of parameters for the resource at the time. The datapoints are represented in an N-dimensional space, including N dimensions corresponding to the resource-status parameters and one dimension devoted to time. The datapoints are clustered on a proximity basis. The clusters are manually classified as Normal or Ack-Abnormal. A machine-learning engine develops a model that allows the classifications to be automated. Alerts are sent for respective Abnormal clusters rather than for each abnormal datapoint, thus reducing the amount of data an administrator must deal with to address potential problems.

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Bengaluru, India

Arunvijai Sridharan