Abstract: 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.

Abstract: 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.

Abstract: The present disclosure is related to methods, systems, and machine-readable media for allocating cost of disk usage to a linked clone virtual machine (VM). A determination can be made as to a number of disks used by a linked clone VM, among the disks in a software defined data center, over a time period. In some examples, a cost for a total usage of the number of disks over the time period can be allocated to the linked clone VM, regardless of whether the total usage over the time period includes usage by other VMs. In some examples, a cost for a total usage of the number of disks over the time period can be allocated to the linked clone VM, in proportion to the number of VMs that use the disk regardless of relative usage of the disk by each of the number of VMs.

Abstract: Methods and apparatus to select virtualization environments are disclosed. An example method includes determining, via a processor, characteristics of a virtualized application that is deployed in an existing virtualization environment. analyzing, via the processor, the characteristics of the virtualized application to select a subset of virtualization environments that are capable of executing the virtualized application, the subset of virtualization environments selected from a set of virtualization environments of different virtualization environment types used in the datacenter, comparing, via the processor, the characteristics of the virtualized application to the virtualization environments of the subset of virtualization environments to determine scores for the virtualization environments, and migrate the virtualized application from the existing virtualization environment to a new virtualization environment based on the scores.

Abstract: Methods and systems are directed to allocating costs of storage arrays to logical disks. A fully loaded cost of storage of a storage area network (“SAN”) is calculated based on capital and operational expenditures of the SAN. Data is stored in the SAN in logical groups partitioned into logical disks. The unit rates of the logical groups are calculated from the capacities and utilization of the storage arrays that form the SAN. The unit rate of a logical disk is the unit rate the logical group to which the logical disk belongs. The storage cost of each logical disk is calculated from the unit rate of the logical disk and the utilized capacity of the logical disk. Methods also calculate cost of unallocated capacities of the logical disks, storage arrays, and the SAN, in order to evaluate efficient use of the SAN storage resources.

Abstract: Methods and systems of determining an optimum power-consumption profile for virtual machines running in a data center are disclosed. In one aspect, a power-consumption profile of a virtual machine and a unit-rate profile of electrical power cost over a period are received. The methods determine an optimum power-consumption profile based on the power-consumption profile and the unit-rate profile. The optimum power-consumption profile may be used reschedule the virtual machine over the period.

Abstract: Methods and systems allocate storage costs to virtual machines (“VMs”) in a virtual data center. Methods calculate a datastore-base rate based on datastore utilized-storage capacity in each LD and each LD-base rate when the datastore utilized-storage capacity and each LD-base rate are available. Datastore total cost is calculated by multiplying the datastore-base rate by the datastore utilized-storage capacity. Methods also use graph based methods to calculate datastore-base rates when the datastore utilized-storage capacity is unknown for each LD. The datastore-base rate associated with each datastore may then be used to calculate a VM storage cost of each VM hosted by a datastore.

Abstract: A power distribution unit having a power supply inputs including mains, secondary, generator, and renewable can be configured to measure how much power is drawn from each of the power supply inputs over a time period and to provide data to a unit rate of power engine indicating the same. A cost information engine can be configured to provide cost information, applicable over the time period, for each of the power supply inputs to the unit rate of power engine. The unit rate of power engine can be configured to compute the unit rate of power consumed in the data center over the time period based on the power drawn from each of the power supply inputs and the cost information.

Abstract: The present disclosure is related to methods, systems, and machine-readable media for allocating cost of disk usage to a linked clone virtual machine (VM) based on a parameter of usage. A determination can be made as to a number of disks used by a linked clone VM among a plurality of disks in a software defined data center over a time period and as to a respective portion of a parameter of usage for each of the number of disks used by the linked clone VM over the time period that is attributable to the linked clone VM. A portion of a cost for usage of each of the number of disks over the time period can be allocated to the linked clone VM in proportion to the respective portion of the parameter of usage attributable to the linked clone VM and/or on a relative latency.

Abstract: Techniques for performing dynamic cost per unit resource usage in a virtual data center are described. In one example embodiment, an initial unit resource usage price is received for the virtual data center for a first cycle. Further, capital expenditure (CAPEX) and operating expenditure (OPEX) information of the virtual data center of the first cycle is obtained. Furthermore, a target return on investment (ROI) for the virtual data center for a second cycle is received. A unit resource usage price is then computed for the second cycle using the received initial unit resource usage price for the first cycle and the CAPEX and OPEX information of the first cycle. The unit resource usage price is then dynamically calibrated for the second cycle using the computed unit resource usage price and the target ROI.

Abstract: A power distribution unit having a power supply inputs including mains, secondary, generator, and renewable can be configured to measure how much power is drawn from each of the power supply inputs over a time period and to provide data to a unit rate of power engine indicating the same. A cost information engine can be configured to provide cost information, applicable over the time period, for each of the power supply inputs to the unit rate of power engine. The unit rate of power engine can be configured to compute the unit rate of power consumed in the data center over the time period based on the power drawn from each of the power supply inputs and the cost information.

Abstract: Methods and apparatus to select virtualization environments are disclosed. An example method includes determining, via a processor, characteristics of a virtualized application that is awaiting deployment, analyzing, via the processor, the characteristics of the virtualized application to select a subset of virtualization environments that are capable of executing the virtualized application, the subset of virtualization environments selected from a set of virtualization environments of different virtualization environment types used in the datacenter, comparing, via the processor, the characteristics of the virtualized application to the virtualization environments of the subset of virtualization environments to determine scores for the virtualization environments, and deploying the virtualized application in the virtualization environment based on the scores.

Abstract: Methods and apparatus to select virtualization environments are disclosed. An example method includes determining, via a processor, characteristics of a virtualized application that is deployed in an existing virtualization environment, analyzing, via the processor, the characteristics of the virtualized application to select a subset of virtualization environments that are capable of executing the virtualized application, the subset of virtualization environments selected from a set of virtualization environments of different virtualization environment types used in the datacenter, comparing, via the processor, the characteristics of the virtualized application to the virtualization environments of the subset of virtualization environments to determine scores for the virtualization environments, and migrate the virtualized application from the existing virtualization environment to a new virtualization environment based on the scores.

Abstract: 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.

Abstract: The present disclosure is related to methods, systems, and machine-readable media for information technology (IT) cost calculation in a software defined data center. A cost of infrastructure IT objects in a software defined data center can be calculated. The infrastructure IT objects can be mapped to an IT service construct over time, during runtime of the infrastructure IT objects. A portion of the cost of the infrastructure IT objects can be allocated to the IT service construct according to the map. A non-infrastructure IT cost can be allocated to the IT service construct. A total cost of the IT service construct can be calculated based on the allocations.

Abstract: The present disclosure is related to methods, systems, and machine-readable media for allocating cost of disk usage to a linked clone virtual machine (VM). A determination can be made as to a number of disks used by a linked clone VM, among the disks in a software defined data center, over a time period. In some examples, a cost for a total usage of the number of disks over the time period can be allocated to the linked clone VM, regardless of whether the total usage over the time period includes usage by other VMs. In some examples, a cost for a total usage of the number of disks over the time period can be allocated to the linked clone VM, in proportion to the number of VMs that use the disk regardless of relative usage of the disk by each of the number of VMs.

Abstract: 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.

Abstract: The present disclosure is related to methods, systems, and machine-readable media for allocating cost of disk usage to a linked clone virtual machine (VM) based on a parameter of usage. A determination can be made as to a number of disks used by a linked clone VM among a plurality of disks in a software defined data center over a time period and as to a respective portion of a parameter of usage for each of the number of disks used by the linked clone VM over the time period that is attributable to the linked clone VM. A portion of a cost for usage of each of the number of disks over the time period can be allocated to the linked clone VM in proportion to the respective portion of the parameter of usage attributable to the linked clone VM and/or on a relative latency.

Abstract: Methods and systems of determining an optimum power-consumption profile for virtual machines running in a data center are disclosed. In one aspect, a power-consumption profile of a virtual machine and a unit-rate profile of electrical power cost over a period are received. The methods determine an optimum power-consumption profile based on the power-consumption profile and the unit-rate profile. The optimum power-consumption profile may be used reschedule the virtual machine over the period.

Abstract: A method is provided for a proxy server to assist in the testing of a product. The method includes receiving, from the product, a first request and passing the first request to a server, receiving, from the server, a first response to the first request and passing the first response to the product, recording the first request and the first response, generating one or more second responses from one or more simulated servers based on the first request and the first response, intercepting a second request from the product, in response to the second request, matching the second request to a second response, and sending the second response to the product.