Vmware, Inc.

Piyush Bharat Masrani

Kumar Gaurav

Dattathreya Sathyamurthy

Hemanth Kumar Pannem

Akhil Sadashiv Hingane

Amarnath Palavalli

Ajay Gautam

Amrainder Singh

Chintesh Pulavarthi

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

Methods and systems to compute cost efficiency of virtual machines ('VMs') running in a private cloud are described. Methods and systems compute a cost efficiency value for each VM in the private cloud based on cost of the VM in the private cloud, cost of similar VMs in the private cloud, price of similar VM running in the public cloud, and the cost of similar VMs running in one or more private clouds in the same geographical area. When the cost efficiency of a VM is greater than a cost efficiency threshold, the methods generate an alert and recommendations for moving the VM to a lower cost public cloud.

Methods and systems to determine and improve cost efficiency of virtual machines

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

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.

The current document is directed to a machine-learning-based subsystem, included within a virtualization layer, that learns, over time, how to accurately predict operational characteristics for the virtual machines executing within the virtual execution environment provided by the virtualization layer that result from changes to the states of the virtual machines. When the virtualization layer receives requests that, if satisfied, would result in a change of the state of one or more virtual machines, the virtualization layer uses operational characteristics predicted by the machine-learning-based subsystem from virtual-machine resource-allocation states that would obtain by satisfying the requests. When the predicted operational characteristics are indicative of potential non-optimality, instability, or unpredictability of virtualized-computer-system operation, the virtualization layer anticipates a deleterious state change and undertakes preventative measures.

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Bengaluru, India

Piyush Bharat Masrani