Vmware, Inc.

Dattathreya Sathyamurthy

Piyush Bharat Masrani

Amarnath Palavalli

Hemanth Kumar Pannem

Akhil Sadashiv Hingane

Jonathan Clark

Kumar Gaurav

Swarnalatha Pasupuleti

Akshay Prabhakar Mirajkar

Prarthana Gopalakrishna Alevoor

Chandrashekhar Jha

Ritesh Ranjan Jha

Servesh Pratap Singh

Yash Bhatnagar

Soumya Panigrahi

Naveen Mudnal

Amrainder Singh

Chintesh Pulavarthi

Nikhil Dhankani

Avinash Shrimali

Amarnath Palayalli

Various examples are disclosed for generating a prediction of server requirements needed to deploy an application. The application can be deployed in virtualized environment in which virtual machines can execute the application. The predicted server requirements can be generated based upon data from other deployments of the application in other virtualized environments.

Generating predictive metrics for virtualized deployments

Methods and systems to compute application license costs of a number of applications run on virtual machines of a virtualized data center are described. In one aspect, one or more of the virtual machines ('VMs') that form the virtual data center are determined. Each VM is created from hardware components specifications of one or more application blueprints stored in a data-storage devices. The one or more blueprints are searched to determine the one more applications that run in each VM. For each VM, a total VM application licensing cost of the one or more applications is computed based on one or more of an application instance license cost, application socket license cost, and application core license of each of the one or more applications associated with each application.

Methods and systems to determine application license costs in a virtualized data center for use in virtual machine consolidation

Systems and techniques are described for generating test cases. In one example, a manual test of code is performed. The manual test uses proxy code to detect at least one input to the code and a data transfer between the code and other code. The data transfer includes an input to the other code and an output from the other code. A file is generated that includes the at least one input to the code, the input to the other code, the output from the other code, and data that specifies that the manual test of the code resulted in an expected output. A mock test automates the manual test of the code based at least in part on the file.

Generating test cases

Techniques for optimizing resource claims for containers is described. In one example, resource utilization data associated with at least one container may be obtained for a period. A set of forecasting models may be trained based on the resource utilization data associated with a portion of the period. Resource utilization of the at least one container may be predicted for a remaining portion of the period using the set of trained forecasting models. The predicted resource utilization may be compared with the obtained resource utilization data for the remaining portion of the period. A forecasting model may be determined from the set of trained forecasting models based on the comparison to optimize resource claims for the at least one container.

Resource claim optimization for containers

Methods and system to calculate a total container cost of each container of a data center are described. Methods compute a direct container cost of each container running in the data center based on container resource utilization and a resource utilization rate of each resource of a host. Methods also compute an indirect container cost of each container is calculated as a sum of indirect cost of each resource of the host used by the container and each resource of the host used by a monitor container that monitors each container utilization of host resources. For each container, a total container cost of the container is calculated as the sum of the direct container cost and the indirect container cost of the container. Total container costs are used to determine the cost of applications miming on the containers.

Methods and systems to determine container costs and attribute container costs to applications

A process of obtaining, in effect, a multi-virtual-machine snapshot by taking a single-virtual-machine snapshot begins with creating, by a host hypervisor, a host virtual machine and a guest hypervisor. The guest hypervisor executes on the host virtual machine. Virtual machines to be included together in an effective multi-virtual-machine snapshot are migrated to the guest hypervisor. A single-virtual-machine snapshot is taken, by the host hypervisor, of the host virtual machine. The snapshot contains the state data for the virtual machines migrated to the guest hypervisor.

Multi-virtual-machine snapshot using guest hypervisor

User configurations of an installed application are migrated by isolating them from a base application state of the installed application to generate a virtual application package. At runtime, a base virtual application package and a user configurations virtual application package are provisioned from a central repository and executed by the user. The resulting virtualized execution environment merges the application resources contained in the packages such that the user configurations override the base application to make available all of the user configurations. This technique may be used to preserve user configurations during migration of applications, or to maintain user-specific configurations for a particular application across different sessions of a user's virtualized desktop.

Selective migration of virtualized applications

This disclosure presents computational systems and methods that allocate cost of resources of a cluster of server computers used by virtual machines in a virtual data center. In one aspect, a fair unit rate is computed based on the larger of a measured average utilization or an expected utilization of a cluster resource of server computers within a physical data center by virtual machines. The fair unit rate is a cost per unit of resource used over a period of time and is used to compute an allocated cost of the virtual machine usage of the cluster resource.

Methods and systems that allocate cost of cluster resources in virtual data centers

Systems and techniques are described for generating test cases. A described technique includes monitoring a manual test of a code portion. The monitoring can include identifying one or more performed operations performed during the manual test. A seed file can be generated that includes data describing the one or more performed operations. A mock test can be generated for the code portion using the seed file. The mock test can include one or more mock operations that match the one or more performed operations. The mock test can be performed on the code portion, including performing the one or more mock operations using the code portion.

Growing community of inventors

Bengaluru, India

Dattathreya Sathyamurthy