Abstract: 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.

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 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 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: Apparatus and methods for estimating a location of a wireless device in communication with a wireless network, such as an LTE/LTE-A network, based at least in part on WLAN/WPAN AP measurements and/or barometric measurements are disclosed. The wireless device responds to a location capability inquiry from the wireless network by providing a response that indicates the wireless device is configurable to estimate its location based on WLAN/WPAN AP and/or barometric measurements. The wireless network sends WLAN/WPAN AP and/or barometric reference information to the wireless device to assist in estimating its location. The wireless device measures one or more WLAN/WPAN APs, and the wireless device uses the WLAN/WPAN AP and/or barometric measurements to estimate its location. In some embodiments, GPS/GNSS information is used in conjunction with WLAN/WPAN AP and/or barometric measurements to estimate the location of the wireless device.

Abstract: Apparatus and methods for estimating a location of a wireless device in communication with a wireless network, such as a UMTS network, based at least in part on WLAN/WPAN AP measurements and/or barometric measurements are disclosed. The wireless device responds to a location capability inquiry from the wireless network by providing a response that indicates the wireless device is configurable to estimate its location based on WLAN/WPAN AP and/or barometric measurements. The wireless network sends WLAN/WPAN AP and/or barometric reference information to the wireless device to assist in estimating its location. The wireless device measures one or more WLAN/WPAN APs, and the wireless device uses the WLAN/WPAN AP and/or barometric measurements to estimate its location. In some embodiments, GPS/GNSS information is used in conjunction with WLAN/WPAN AP and/or barometric measurements to estimate the location of the wireless device.

Abstract: The present disclosure describes methods and systems that allocate costs of deploying and operating a virtual network to tenants that use the virtual network. In one implementation, costs are allocated to tenant virtual machines (“VMs”) by determining a network bandwidth of a virtual network, determining a common cost of operating the virtual network, determining a service capacity for each network service provided by the virtual network, and determining a service cost for each network service. A portion of the common cost is allocated to each VM based on the proportion of network bandwidth used by each VM, and a portion of the service cost is allocated to each VM based on the proportion of the service capacity used by each VM.

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 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.

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: Techniques for minimizing guest OS licensing costs in a volume based licensing model in a virtual datacenter are described. In one example embodiment, a virtual machine (VM) that requires a license key for a type of guest OS installed in the VM is identified. A license key is then assigned to the VM by first attempting to reassign a license key from an inactive VM, and only if a license key is not available for reassignment, obtaining a new license key for the VM.

Abstract: Techniques for optimizing guest operating system (OS) utilization cost in a processor based licensing model in a virtual datacenter are described. In one example embodiment, a virtual machine (VM) that has or is scheduled to have an instance of an operating system (OS) that requires a license is identified. Availability of a physical processor of a first host computing system that is licensed to execute the OS based on the computing resource requirements of the VM, the physical processor based license, author assigned affinity to physical processors in the first host computing. system is determined. The VM is then migrated/placed to/on the physical processor of the first host computing system or migrated/placed to/on a physical processor of a second host computing system based on the outcome of the determination.

Abstract: Systems and methods of allocating network cost of a physical data center to data center tenants are disclosed. In one aspect, the systems and methods compute a total cost of the physical data center devices and networks and other operational expenditures over a period of time. The systems and methods compute local network and Internet utilization for each VM over the full period. Network utilization is computed for each VM as a fraction of the total cost. The cost allocated to each tenant is computed as a sum of the total cost of all VMs used by the tenant.

Abstract: The present disclosure describes methods and systems that monitor the utilization of computational resources. In one implementation, a system periodically measures the utilization of computational resources, determines an amount of computational-resource wastage, identifies the source of the wastage, and generates recommendations that reduce or eliminate the wastage. In some implementations, recommendations are generated based on a cost of the computational-resource wastage. The cost of computational-resource wastage can be determined from factors that include the cost of providing a computational resource, an amount of available computational resources, and the amount of actual computational-resource usage. Methods of presenting and modeling computational-resource usage and methods that associate an economic cost with resource wastage are presented.

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: The invention relates to a photostable sunscreen composition that not only provides enhanced sun protection by way of high SPF, but does that over sustained period of time after applying the composition on the skin. The present inventors have found that when a compound of the triazine group is used without additional UVA or UVB sunscreen, and a non-ionic surfactant is included in a cosmetic base comprising fatty acids, it is possible to achieve high SPF.

Abstract: A computer-implemented method and system for querying aggregates in a database include maintaining aggregates based on a dimension in the database with at least two grain sizes; receiving a query of the aggregates for a defined range of the dimension; finding a start and an end for a read operation for a larger grain size of the at least two grain sizes of the aggregates for the defined range; reading a first set from the start to the end in the database of the larger grain size of the at least two grain sizes of the aggregates; reading a second set comprising a smaller grain size of the at least two grain sizes of the aggregates based on the defined range and the start and the end; and adjusting the first set with the second set.

Abstract: The disclosure presents methods and systems for determining cost allocation for logical containers run on a data-center infrastructure. In one aspect, for each resource allocated to one or more logical containers, a method calculates a resource allocation value for each of the one or more logical containers, the resource allocation value represents an amount of the resource allocated to the logical container. An allocated container cost is then calculated for each of the one or more logical containers based on the resource allocation value of each logical container. A cost of unused portions of the resource for each of the one or more logical containers is calculated based on the allocated container cost and the resource allocation value associated with each logical container. The resource allocation value, the allocated container cost, and the cost of unused portion of the resource are stored in one or more data-storage devices.

Abstract: This disclosure presents computational systems and methods for calculating the cost of vCPUs from the cost of CPU computing cycles. In one aspect, a total number of computing cycles used by one or more virtual machines (“VMs”) is calculated based on utilization measurements of a multi-core processor for each VM over a period of time. The method also calculates a total number of virtual CPUs (“vCPUs”) used by the one or more VMs based on vCPU counts for each VM over the period of time. A cost per vCPU is calculated based on the total number of computing cycles, the total number of vCPUs, and cost per computing cycle. The cost per vCPU is stored in a data-storage device. The cost per vCPU can be used to calculate the cost of a VM that uses one or more of the vCPUs.