Abstract: A computer-implemented method includes receiving a template for creating a virtual machine (VM) instance; separating the template into a repeated portion and a unique portion; determining whether the repeated portion is stored in a cache; creating based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing the unique portion of the VM instance to create a completed VM instance; and deploying the completed VM instance.

Abstract: Systems and methods are provided for dynamic metering adjustment for service management of a computing platform. For example, a plurality of virtual machines are provisioned across a plurality of computing nodes of a computing platform. Data samples are collected for a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines. The data samples are initially collected at a predefined sampling frequency. The data samples collected over time for the metric are analyzed to determine an amount of deviation in values of the collected data samples. A new sampling frequency is determined for collecting data samples for the metric based on the determined amount of deviation. The new sampling frequency is applied to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency.

Abstract: An approach for ontological policy based data collection, processing, and negotiation for data in view of analytics is provided. The approach searches one or more data sources for data related to a data request. The approach collects data related to the data request from the one or more data sources. The approach determines whether one or more attributes generated from the data request match one or more descriptors associated with the data related to the data request. The approach creates one or more annotated ontologies for the data related to the data request. The approach displays a hierarchical visualization of the one or more annotated ontologies for the data related to the data request. The approach updates the one or more annotated ontologies for the data related to the data request based, at least in part, on an evaluation of the quality of the one or more data selections.

Abstract: A computer-implemented method includes: receiving, by a computing device, a template for creating a virtual machine (VM) instance; separating, by the computing device, the template into a repeated portion and a unique portion; determining, by the computing device, whether the repeated portion is stored in a cache; creating, by the computing device and based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing, by the computing device, the unique portion of the VM instance to create a completed VM instance; and deploying, by the computing device, the completed VM instance.

Abstract: A computer-implemented method includes: receiving, by a computing device, a template for creating a virtual machine (VM) instance; separating, by the computing device, the template into a repeated portion and a unique portion; determining, by the computing device, whether the repeated portion is stored in a cache; creating, by the computing device and based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing, by the computing device, the unique portion of the VM instance to create a completed VM instance; and deploying, by the computing device, the completed VM instance.

Abstract: A computer executes a first version of a code module in a first test environment, collects a first set of execution measurements, and creates a first profile based on the first set of execution measurements. The computer executes the first version of the code module in a second test environment, collects a second set of execution measurements, and creates a second profile based on the second set of execution measurements. In response to a difference between the first profile and the second profile exceeding a threshold, the computer provides diagnostic data characterizing the difference, analyzes an impact of the difference on the execution of the code module, calculates a risk of code module failure value based on analyzing the impact of the difference, and provides a recommended action based on analyzing the impact of the difference and the calculated risk of code module failure value.

Abstract: A computer-implemented method includes: receiving, by a computing device, a template for creating a virtual machine (VM) instance; separating, by the computing device, the template into a repeated portion and a unique portion; determining, by the computing device, whether the repeated portion is stored in a cache; creating, by the computing device and based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing, by the computing device, the unique portion of the VM instance to create a completed VM instance; and deploying, by the computing device, the completed VM instance.

Abstract: Examples of GPU resource sharing among distributed applications in a distributed computing environment are disclosed. In one example, a method includes receiving a first request from a first distributed application of the plurality of distributed applications for first requested GPU resources. The method may further include receiving a second request from a second distributed application of the plurality of distributed applications for second requested GPU resources. The method may also include receiving response from each of the plurality of computing nodes indicating an availability of GPU resources for each of the plurality of computing nodes. Additionally, the method may include, responsive to determining that at least one of the first and second requests can be fulfilled by at least one of the plurality of computing nodes, allocating a first set of GPU slices for the first application and allocating a second set of GPU slices for the second application.

Abstract: Examples of GPU resource sharing among applications are disclosed. In one example, a method includes receiving a first request from a first application of the plurality of applications for first requested GPU resources, and receiving a second request from a second application of the plurality of applications for second GPU resources. The method also includes, responsive to determining that the first requested GPU resources are available, allocating a first slice of the GPU resources with a first requested amount of resources to the first application and, responsive to determining that the second requested GPU resources are available, allocating a second slice of the GPU resources with a second requested amount of resources to the second application. Further, the method includes enabling the first application and the second application to execute concurrently within the first slice of the GPU and the second slice of the GPU respectively.

Abstract: Examples of GPU resource sharing among distributed applications in a distributed computing environment are disclosed. In one example, a method includes receiving a first request from a first distributed application of the plurality of distributed applications for first requested GPU resources. The method may further include receiving a second request from a second distributed application of the plurality of distributed applications for second requested GPU resources. The method may also include receiving response from each of the plurality of computing nodes indicating an availability of GPU resources for each of the plurality of computing nodes. Additionally, the method may include, responsive to determining that at least one of the first and second requests can be fulfilled by at least one of the plurality of computing nodes, allocating a first set of GPU slices for the first application and allocating a second set of GPU slices for the second application.

Abstract: Examples of GPU resource sharing among applications are disclosed. In one example, a method includes receiving a first request from a first application of the plurality of applications for first requested GPU resources, and receiving a second request from a second application of the plurality of applications for second GPU resources. The method also includes, responsive to determining that the first requested GPU resources are available, allocating a first slice of the GPU resources with a first requested amount of resources to the first application and, responsive to determining that the second requested GPU resources are available, allocating a second slice of the GPU resources with a second requested amount of resources to the second application. Further, the method includes enabling the first application and the second application to execute concurrently within the first slice of the GPU and the second slice of the GPU respectively.

Abstract: Historical data and real-time data are collected for a plurality of computing resources. Based on the collected historical data, typical behavior of the plurality of computing resources is modeled and resulting models are stored in a model repository. With an inference engine, the real-time data is compared to the models. The plurality of computing resources are managed based on the comparing step.

Abstract: A computer executes a first version of a code module in a first test environment, collects a first set of execution measurements, and creates a first profile based on the first set of execution measurements. The computer executes the first version of the code module in a second test environment, collects a second set of execution measurements, and creates a second profile based on the second set of execution measurements. In response to a difference between the first profile and the second profile exceeding a threshold, the computer provides diagnostic data characterizing the difference, analyzes an impact of the difference on the execution of the code module, calculates a risk of code module failure value based on analyzing the impact of the difference, and provides a recommended action based on analyzing the impact of the difference and the calculated risk of code module failure value.

Abstract: An approach for ontological policy based data collection, processing, and negotiation for data in view of analytics is provided. The approach searches one or more data sources for data related to a data request. The approach collects data related to the data request from the one or more data sources. The approach determines whether one or more attributes generated from the data request match one or more descriptors associated with the data related to the data request. The approach creates one or more annotated ontologies for the data related to the data request. The approach displays a hierarchical visualization of the one or more annotated ontologies for the data related to the data request. The approach updates the one or more annotated ontologies for the data related to the data request based, at least in part, on an evaluation of the quality of the one or more data selections.

Abstract: Systems and methods are provided for dynamic metering adjustment for service management of a computing platform. For example, a plurality of virtual machines are provisioned across a plurality of computing nodes of a computing platform. Data samples are collected for a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines. The data samples are initially collected at a predefined sampling frequency. The data samples collected over time for the metric are analyzed to determine an amount of deviation in values of the collected data samples. A new sampling frequency is determined for collecting data samples for the metric based on the determined amount of deviation. The new sampling frequency is applied to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency.

Abstract: A computer-implemented method or real-time measurement of a user interface performance in a remote desktop environment is provided. The computer-implemented method may include host computer receiving a command data package and a client request time from client computer, where the client request time is a timestamp of a time at which the command data package was sent from client computer to host computer. The computer-implemented method may further include host computer recording the client request time and a request arrival time, where the request arrival time is a timestamp of a time at which the command data package was received by host computer. The computer-implemented method may further include host computer calculating a first traverse time, where the first traverse time is a time difference between the client request time and the request arrival time to determine a real-time measurement of a user interface performance in the remote desktop environment.

Abstract: A method, an apparatus and an article of manufacture for cloud-driven application execution. The method includes determining a plurality of attributes of a failed application, wherein the plurality of attributes comprises at least one policy context attribute and at least one context attribute, correlating each of the plurality of attributes to at least one alternative asset, wherein the at least one alternative asset is a part of an environment on which the failed application can be executed, using the plurality of attributes correlated to the at least one alternative asset to identify an alternative asset set of alternative assets, wherein the alternative asset set is capable of enabling an alternative environment on which to execute the failed application, and provisioning the alternative assets in the alternative asset set from at least one cloud network to create the alternative environment on which the failed application is executed.

Abstract: Remediating events of components using behaviors via an administrator system and an administrator client. The administrator system receives an event from a component of an information technology (IT) environment. A behavior is determined at least partly from the event. The behavior is determined to be an anomalous behavior at least partly from a group of previously received events. A coefficient is calculated, via a calculation, for the anomalous behavior at least partly from a weight. The administrator system sends a description of the anomalous behavior and a group of options to the administrator client. The description is at least partly based on the calculation. The administrator system receives a severity indication from the administrator client. The weight, the calculation, and the description are updated based on the severity indication.

Abstract: Remediating events of components using behaviors via an administrator system and an administrator client. The administrator system receives an event from a component of an information technology (IT) environment. A behavior is determined at least partly from the event. The behavior is determined to be an anomalous behavior at least partly from a group of previously received events. A coefficient is calculated, via a calculation, for the anomalous behavior at least partly from a weight. The administrator system sends a description of the anomalous behavior and a group of options to the administrator client. The description is at least partly based on the calculation. The administrator system receives a severity indication from the administrator client. The weight, the calculation, and the description are updated based on the severity indication.

Abstract: A system and method is provided for integrating the front-end and back-end of a virtualized desktop environment to achieve an objective. Network measurements are gathered which are related to enforcement of an objective. Based on gathered network measurements and quality of service requirements for user sessions, a determination is made as to how back-resources and front-end remoting protocol settings are to be adjusted. The back-end resources and the front-end remoting protocol settings are controlled to achieve the objective.