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: Data-aware orchestration with respect to a distributed system platform enables at least lifting and shifting of pre-existing applications and associated data without developer action. A volume of a local store is created automatically in response to a container comprising a user application that is non-native with respect to the distributed system platform. The volume is then exposed to the container for use by the application to save and retrieve data. The container and local store are co-located on a compute node providing at least high availability. The application and local store can be duplicated on one or more replicas providing reliability in case of a failure. Further, partitions can be created automatically in response to declarative specification.

Abstract: A system can provide a visual representation of an inventory of data entities for a distributed computing system. Inventory data including cost and operational data for data entities such as data centers, servers, and virtual machines, can be converted into a format file. The format file can be used to create a tree of nodes and node summaries corresponding to the data entities. A user interface can display hierarchical and isolated views of the tree revealing parent child relationships between data entities within a computing system infrastructure. Node summaries including cost and utilization data can be displayed to reveal how specific sub-costs such as labor and licensing, are driven by data entities in one level of the infrastructure and pushed to respective parent or child data entities in other levels. Views of the tree can be used to determine areas of inefficiency or reduced value within the computing system.

Abstract: The disclosed embodiments use 3D city models and shadow mapping to improve altitude fixes in urban environments. In an embodiment, a method comprises: generating a set of three-dimensional (3D) candidate positions in a geographic area of interest; predicting global navigation satellite system (GNSS) signal visibility at selected ones of the 3D candidate positions; receiving GNSS signals at a current location of the mobile device; determining observed satellite visibility based on the received GNSS signals; comparing the predicted satellite visibility with the observed satellite visibility; determining a position fix based on a result of the comparing; determining an indoor environment where the mobile device is located based at least on an altitude component of the position fix; obtaining structural data for the identified indoor environment; and determining a floor lower bound for the current location of the mobile device based on the altitude component and the structural data.

Abstract: Methods and systems predict parameters in a dataset of an identified piece of (“information technology”) IT equipment. An automated method identifies datasets IT equipment in a same category of IT equipment as a piece of IT equipment identified as having incomplete dataset information. Each dataset of IT equipment parameters is used to construct generalized linear models of different classes of IT equipment within the category of IT equipment. The class of the identified IT equipment is determined. A predicted equipment parameter of incomplete information of the identified piece of IT equipment is computed using the generalized linear model associated with the class. The predicted equipment parameter can be used to complete the dataset of the identified piece of IT equipment.

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 guest 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: 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: 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: Methods and systems predict parameters in a dataset of an identified piece of (“information technology”) IT equipment. An automated method identifies datasets IT equipment in a same category of IT equipment as a piece of IT equipment identified as having incomplete dataset information. Each dataset of IT equipment parameters are used to construct generalized linear models of different classes of IT equipment within the category of IT equipment. The class of the identified IT equipment is determined. A predicted equipment parameter of incomplete information of the identified piece of IT equipment is computed using the generalized linear model associated with the class. The predicted equipment parameter can be used to complete the dataset of the identified piece of IT equipment.

Abstract: Methods and apparatus to select virtualization environments are disclosed. An example apparatus includes a logic circuit, a workload analyzer to determine characteristics of a virtualized application, a score generator to compare the characteristics of the virtualized application to a plurality of virtualization environment types to determine scores for each of the plurality of virtualization environment types, the scores based on rules that identify different scores for combinations of characteristics and virtualization environment types, and a workload deployer to deploy the virtualized application using one of the plurality of virtualization environment types based on the scores.

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: Data-aware orchestration with respect to a distributed system platform enables at least lifting and shifting of pre-existing applications and associated data without developer action. A volume of a local store is created automatically in response to a container comprising a user application that is non-native with respect to the distributed system platform. The volume is then exposed to the container for use by the application to save and retrieve data. The container and local store are co-located on a compute node providing at least high availability. The application and local store can be duplicated on one or more replicas providing reliability in case of a failure. Further, partitions can be created automatically in response to declarative specification.

Abstract: A system can provide a visual representation of an inventory of data entities for a distributed computing system. Inventory data including cost and operational data for data entities such as data centers, servers, and virtual machines, can be converted into a format file. The format file can be used to create a tree of nodes and node summaries corresponding to the data entities. A user interface can display hierarchical and isolated views of the tree revealing parent child relationships between data entities within a computing system infrastructure. Node summaries including cost and utilization data can be displayed to reveal how specific sub-costs such as labor and licensing, are driven by data entities in one level of the infrastructure and pushed to respective parent or child data entities in other levels. Views of the tree can be used to determine areas of inefficiency or reduced value within the computing system.

Abstract: Examples described herein include systems and methods for optimizing licensing costs for a server. In one example, at least two licenses (a first license and a second license) are available for the server. The first license can cost more than the second license but allow for a greater number of VMs to run on the server. The example method can include determining whether the server is running the first license and the number of VMs running on the server. If the server is using the first license but the number of VMs running on the server is less than or equal to the number potentially allowed by the second license, an action can be taken for changing the server's license subscription. For example, the method can include displaying a GUI element or sending a message that indicates the cost savings associated with changing the licenses.

Abstract: Various examples are disclosed for using clustering routines to extrapolate metrics to other computing resources in a cluster. One or more computing devices can classify computing resources, such as servers, based on various characteristics of the computing resources. For each class of computing resource, a clustering routine can be applied to generate clusters of the computing resources. A minimal number of metrics required to be obtained from an end user can be determined as a function of a number of the clusters. If one or more of the metrics are obtained from the end user, the metrics can be extrapolated to other computing resources in the same cluster.

Abstract: Methods and systems are directed to detection and correction of outliers in a dataset stored in a data-storage device. The dataset comprises parameter data that may be stored and organized in the form of a data table with rows and columns of parameter values. Each column of the parameter data is searched for outlier parameter values based on the parameters values in the same column. The parameter data as a whole may be searched for outlier rows of parameter values based on first and second largest variations in the parameter data. Substitute parameter values are determined for the outlier parameter values based on non-outlier parameter values of the parameter data. The substitute parameter values and corresponding outlier parameter values may be displayed in a database management user interface that enables a user to selectively accept or reject each of the substitute parameter values for the corresponding outlier parameter values.

Abstract: The detection of idle virtual machines through usage pattern analysis is described. In one example, a computing device can collect utilization metrics from a virtual machine over time. The utilization metrics can be related to one or more processing usage, disk usage, network usage, and memory usage metrics, among others. The utilization metrics can be separated into a set of training metrics and a set of validation metrics, and a number of clusters can be determined based on the set of training metrics. The clusters can be used to organize the set of validation metrics into groups. Depending upon the number or overall percentage of the utilization metrics assigned to individual ones of the plurality of clusters, it is possible to determine whether or not the virtual machine is an idle virtual machine. Once identified, idle virtual machines can be shut down to conserve processing resources and costs.

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: An exception handling system is described herein that provides one or more distinguished classes of software exceptions that are handled differently than other exceptions. The system treats a distinguished exception as a “hard to catch” exception that is not passed to the catch block of program code unless a developer performs extra steps to acknowledge the distinguished nature of the exception and confirm that the program code is prepared to properly handle the exception. Exceptions that fall into this class are typically those that represent conditions from which normal exception handling practices cannot successfully recover, namely exceptions that corrupt application state. Accordingly, the system prevents the developer from catching these classes of exceptions by default unless the developer explicitly requests to have these exceptions delivered to the program code. Thus, the exception handling system encourages correct programming practices by preventing developer error by default.

Abstract: Methods and systems predict parameters in a dataset of an identified piece of (“information technology”) IT equipment. An automated method identifies datasets IT equipment in a same category of IT equipment as a piece of IT equipment identified as having incomplete dataset information. Each dataset of IT equipment parameters are used to construct generalized linear models of different classes of IT equipment within the category of IT equipment. The class of the identified IT equipment is determined. A predicted equipment parameter of incomplete information of the identified piece of IT equipment is computed using the generalized linear model associated with the class. The predicted equipment parameter can be used to complete the dataset of the identified piece of IT equipment.