Vmware, Inc.

Vipul Chaudhary

Sandeep L Hegde

Arunvijai Sridharan

Sidhartha Sahoo

Ranganathan Srinivasan

Zacharia George

V Vimal Das Kammath

Naren Lal

Madhan Sankar

Shyam Kasi Venkatram

Mahesh Voleti

Divith Shetty

Vinay Vivekananda

Vimal Das Kammath Veerakumar

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes resource type information. Further, an API response may be obtained from the endpoint by querying the endpoint using an API call. Furthermore, the API response may be parsed and a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information correspond to a resource type in the resource type information. Further, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

Resource determination based on resource definition data

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes adapter information and resource type information. Further, an adapter instance may be generated using the adapter information to establish communication with the endpoint. Furthermore, an API response may be obtained, via the adapter instance, from the endpoint by querying the endpoint using an API call. Further, the API response may be parsed. Further, a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information corresponding to a resource type in the resource type information. Furthermore, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

In one example, a system may include an application monitoring server and an endpoint in communication with the application monitoring server. Example endpoint may include an agent to collect performance metrics associated with a program running in the endpoint and a discovery unit in communication with the agent. The discovery unit may include a metric parser to receive the performance metrics in a source format and parse the received performance metrics. Further, the discovery unit may include a resource extractor to apply a transformation definition to the parsed performance metrics to determine a plurality of resources in a destination format. The plurality of resources may be associated with an application being executed in the endpoint. Furthermore, the discovery unit may include a transmitting unit to transmit the performance metrics and first information associated with the plurality of resources to the application monitoring server via a network.

Distributed application resource determination based on performance metrics

Techniques for discovering applications based on file system directories are disclosed. In one example, process information may be extracted from a file system directory of an application host executing a plurality of applications. Further, an expression match may be performed on the process information. Furthermore, a presence of an application running on the application host may be determined based on an outcome of the expression match.

Applications discovery based on file system directories

An example apparatus to configure a workload domain for security compliance includes a configuration normalizer to generate normalized workload domain configuration settings by normalizing workload domain configuration settings of a deployed workload domain based on a format of compliance configuration settings; a drift comparator to compare the normalized workload domain configuration settings with the compliance configuration settings before deploying an application in the workload domain; a post start-up controller to generate updated workload domain configuration settings by modifying ones of the workload domain configuration settings, the modifying of the ones of the workload domain configuration settings being based on the comparison of the normalized workload domain configuration settings with the compliance configuration settings; and a compliance verifier to determine whether the updated workload domain configuration settings satisfy the compliance configuration settings.

Methods and apparatus to deploy security-compliant workload domains

In one example, an application monitoring server may include a metric parser to receive performance metrics associated with an endpoint. Example performance metrics may be in a source format. Further, the metric parser may parse the received performance metrics. Furthermore, the application monitoring server may include a resource extractor to apply a transformation definition to the parsed performance metrics to determine a plurality of resources in a destination format. Example plurality of resources may be associated with an application being executed in the endpoint. Further, the resource extractor may present information associated with the plurality of resources on a graphical user interface.

Centralized application resource determination based on performance metrics

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.

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Bengaluru, India

Vipul Chaudhary