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: Methods and systems assist data center customer to plan virtual data center (“VDC”) configurations, create purchase recommendations to achieve either an expansion or contraction of a VDC, and optimize the data center cost. Methods generate recommendations on lower cost combinations of virtual machine (“VM”) guest OS licenses, server computer hardware and VM software to optimize the costs are generated, generate data center customer plans for additional VMs with Quest OS for a projected period of time, provide recommendations on lower cost combination of guest OS licenses, server hardware, and VM software to optimize the cost. Methods also report any underutilized licensed servers and provide recommendations for cost savings when volume licenses can be replaced by instance based software licenses. Methods may generate VM placement recommendations to data center customers while the customers attempt to manually migrate VMs to different server computers.

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: 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.

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: The present disclosure relates to system(s) and method(s) for registration of a custom component on a Stream Analytics Platform. The system is configured to receive a program file and one or more registration instructions corresponding to the custom component, from a primary user of the Stream Analytics Platform. Further, a program code may be extracted from the program file by parsing the program file. Further, the system is configured to register the program code as a registered custom component, on the Stream Analytics Platform based on the one or more registration instructions. Once registered, the registered custom component is available over a Graphical User Interface (GUI) of the Stream Analytics Platform. The system enables at least the primary user or a set of secondary users of the Stream Analytics platform to use the registered custom component, based on the one or more registration instructions, for designing a distributed processing pipeline.

Abstract: 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.

Abstract: Described above is a system for allocating variable compensation to a group of individuals. The system displays objective financial statistics associated with individuals, and subjective factors to be set by a user, where the system can convert subjective factors set by the user into a value. The user can then select base and variable compensation values for each individual. Other details are described herein.

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: 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.

Abstract: The current document is directed to methods and systems for efficiently executing OSL-virtualization containers within the execution environments provided by virtual machines that execute above traditional virtualization layers within large, virtualized, distributed computing systems. The currently disclosed methods and systems anticipate the need for additional virtual machines in order to meet anticipated demands for one or more computational resources by the containers. In addition, the methods and systems provision and launch virtual machines with computational-resource allocations that minimize overhead and computational-resource wastage. In one implementation, computational-resource utilization of ATMs and containers within the virtualized, distributed computer system are periodically monitored in order to estimate future demand for the computational resource and, when necessary, to launch additional virtual machines to meet the estimated future demand for the computational resource.

Abstract: Systems and methods for managing sparsely updated counters in memory include, for a given interval of time and N counters associated with the given interval, managing a first set of the N counters in a first level of storage in the memory, wherein the first level of storage utilizes a hash table to store a counter identifier and a value for each of the first set; and responsive to filling up the first level of storage for a given user in the given interval, managing the first set and a second set of the N counters in a second level of storage in the memory, wherein the set utilizes memory buckets to incrementally store the first set and the second set.

Abstract: Techniques for GNSS positioning using three-dimensional (3D) building models are described. A processor can determine a probable path for a signal from a GNSS space vehicle (e.g., a satellite) to reach the GNSS receiver. The probable path can include one or more specular reflections. The processor can determine a Doppler correction based on the probable path, including inverting a sense of a vector of the Doppler correction for each reflection. The processor can then incorporate the Doppler correction in an estimated velocity of the mobile device, an estimated position of the mobile device, or both.

Abstract: Techniques for GNSS positioning using three-dimensional (3D) building models are described. A processor of a mobile device can determine a lower bound of uncertainty for an estimated position of the mobile device. The processor can receive an estimated position from a GNSS receiver of the mobile device. The processor can acquire geographic feature data including 3D building models of buildings and other geographic features that are located near the estimated position and may reflect GNSS signals. The processor can then determine a lower bound of uncertainty of the estimated position, regardless of an estimated uncertainty provided by a GNSS estimator. The lower bound can be higher (e.g., have a greater error margin) than the uncertainty value provided by the GNSS estimator. The processor can then present the estimated position, in association with an error margin corresponding to the lower bound of uncertainty, on a map user interface of the mobile device.

Abstract: The current document is directed to methods and systems for efficiently executing OSL-virtualization containers within the execution environments provided by virtual machines that execute above traditional virtualization layers within large, virtualized, distributed computing systems. The currently disclosed methods and systems anticipate the need for additional virtual machines in order to meet anticipated demands for one or more computational resources by the containers. In addition, the methods and systems provision and launch virtual machines with computational-resource allocations that minimize overhead and computational-resource wastage. In one implementation, computational-resource utilization of ATMs and containers within the virtualized, distributed computer system are periodically monitored in order to estimate future demand for the computational resource and, when necessary, to launch additional virtual machines to meet the estimated future demand for the computational resource.

Abstract: A method for scheduling computing resources without container migration includes determining a resource availability for one or more hosts, a resource allocation for one or more virtual machines (VMs), and a resource usage for one or more containers. The method further includes calculating a target resource configuration for one or more VMs, wherein calculating a target resource configuration comprises determining an upper limit of resource demand on a VM from one or more containers allocated on the VM, based at least in part on the resource usage. The method also includes removing or adding resources to each of the one or more VMs for which a target resource configuration was calculated to achieve the target resource configuration for each VM. The method further includes allocating the one or more VMs on the one or more hosts based on the resource availability of the one or more hosts.

Abstract: A method for scheduling computing resources with container migration includes determining a resource availability for one or more hosts, a resource allocation for one or more virtual machines (VMs), and a resource usage for one or more containers. The method includes identifying the hosts on which VMs and containers can be consolidated based on resource availability. The method also includes calculating a target resource configuration for one or more VMs. The method further includes removing or adding resources to the VMs for which a target resource configuration was calculated to achieve the target resource configuration. The method further includes allocating the one or more VMs on the one or more hosts based on the resource availability of the one or more hosts, and allocating the one or more containers on the one or more VMs based on the resource configuration of each VM and the resource usage of each container.