Abstract: A storage placement planning system receives a resource graph describing SAN's resources and virtual machine applications requiring a particular amount of a processing resource element and a storage resource element. The system then determines a coupled placement of the processing element and storage element for each of the applications on a coupled pair of the resource nodes based on a specified throughput and a distance factor between coupled pairs of resource nodes. The coupled placement is determined using an algorithm that implements a cost function that determines affinities between processing nodes and storage nodes for each of said applications of a particular workload. The coupled placement for each of said applications identifies the particular amount of processing resource element placed on a first node for providing a processing resource and the particular amount of storage resource element placed on a second node for providing a storage resource for that application.

Abstract: Embodiments of the invention relate to creating and maintaining consistent data sets in a shared pool of configurable computer resources to support disaster recovery support. Data from an application is stored in local data storage and replicated to another data storage. A consistency point of the data is created in both of the data storage, with the consistency point representing an identical data set at a point-in-time. Based upon the created consistency points, a consistent set of application data may be requested to support a read operation for a migrated application.

Abstract: Embodiments of the invention relate to creating and maintaining consistent data sets in a shared pool of configurable computer resources to support disaster recovery support. Data from an application is stored in local data storage and replicated to another data storage. A consistency point of the data is created in both of the data storage, with the consistency point representing an identical data set at a point-in-time. Based upon the created consistency points, a consistent set of application data may be requested to support a read operation for a migrated application.

Abstract: Managing a data transfer from one or more source storage devices to one or more target storage devices. The data transfer comprises concurrent transfer of a multiplicity of data units pursuant to respective data transfer commands. The concurrent transfer of the multiplicity of data units is currently in-progress. A computer determines a currently-overloaded storage component involved in the data transfer. The computer determines a plurality of the data transfer commands that involve the overloaded storage component. The computer determines an approximately-minimum number of the data transfer commands to cancel to stop overload of the overloaded storage component. In response, the computer cancels the minimum number of the data transfer commands.

Abstract: Scheduling a proposed additional data transfer from one or more source storage devices to one or more target storage devices. A computer receives a request for the proposed additional data transfer, and in response, determines a measure of the proposed additional data transfer. The computer determines a measure of recent actual data transfers. The recent actual data transfers involve one or more of the source storage devices and one or more of the target storage devices. In response to the request for the proposed additional data transfer, the computer estimates performance of one or more of the source storage devices and one or more of the target storage devices that would occur during the proposed additional data transfer based on the measure of recent actual data transfers combined with the measure of the proposed additional data transfer.

Abstract: Embodiments of the invention relate to dynamic application migration in a shared pool of configurable computer resources with disaster recovery support. Data from an application is replicated from local data storage to remote data storage. A consistency point of the data is created in both the local data storage and the remote data storage. The application may be migrated to a second data site with separate local data storage. The migration may be planned or unplanned. Based upon the created consistency point, a consistent set of application data may be requested to support a read operation from the migrated application.

Abstract: Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.

Abstract: Embodiments of the present invention provide an approach for automatic storage planning and provisioning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will receive planning input for a set of storage area network volume controllers (SVCs) within the clustered computing environment, the planning input indicating a potential load on the SVCs and its associated components. Along these lines, analytical models (e.g., from vendors) can be also used that allow for a load to be accurately estimated on the storage components. Regardless, configuration data for a set of storage components (i.e., the set of SVCs, a set of managed disk (Mdisk) groups associated with the set of SVCs, and a set of backend storage systems) will also be collected. Based on this configuration data, the set of storage components will be filtered to identify candidate storage components capable of addressing the potential load.

Abstract: The present invention proactively identifies hotspots in a cloud computing environment through cloud resource usage models that use workload parameters as inputs. In some embodiments the cloud resource usage models are based upon performance data from cloud resources and time series based workload trend models. Hotspots may occur and can be detected at any layer of the cloud computing environment, including the server, storage, and network level. In a typical embodiment, parameters for a workload are identified in the cloud computing environment and inputted into a cloud resource usage model. The model is run with the inputted workload parameters to identify potential hotspots, and resources are then provisioned for the workload so as to avoid these hotspots.

Abstract: A computer-implemented method for determining, from a system including a plurality of data center resources, at least one configuration of data center resources for an implementation of an application. The method includes receiving application information and receiving information regarding known internal features up the data center resources. The method also includes provisioning the system of data center resources and creating possible configurations of data center resources for implementing application. The method also includes correlating models and data center resources to create an interrelated representation of the models and the data center resources. The models predict a relationship of parameters for the possible configurations.

Abstract: In general, embodiments of present invention provide an approach for calibrating a cloud computing environment. Specifically, embodiments of the present invention provide an empirical approach for obtaining end-to-end performance characteristics for workloads in the cloud computing environment (hereinafter the “environment”). In a typical embodiment, different combinations of cloud server(s) and cloud storage unit(s) are determined. Then, a virtual machine is deployed to one or more of the servers within the cloud computing environment. The virtual machine is used to generate a desired workload on a set of servers within the environment. Thereafter, performance measurements for each of the different combinations under the desired workload will be taken. Among other things, the performance measurements indicate a connection quality between the set of servers and the set of storage units, and are used in calibrating the cloud computing environment to determine future workload placement.

Abstract: Embodiments of the present invention provide an approach for migrating virtual machines across network (e.g., WAN) separated data centers (e.g., storage clouds). Specifically, under embodiments of the present invention, a first storage system associated with a first data center is synchronized with a second storage system associated with a second data center via a storage system link. Then, a minimal state of a virtual machine is migrated from a first computer in the first data center to a second computer in the second data center via a WAN link. Using the minimal state, the virtual machine is stored in the second computer. Thereafter, the storage system link is terminated. In addition, as updated pages are received in memory of the first computer, they are migrated to the second computer via the WAN link. Once this migration is complete, the WAN link can be terminated.

Abstract: Embodiments of the present invention provide an approach for intelligent storage planning and planning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will first determine/identify a set of storage area network volume controllers (SVCs) that is accessible from a host that has submitted a request for access to storage. Thereafter, a set of managed disk (mdisk) groups (i.e., corresponding to the set of SVCs) that are candidates for satisfying the request will be determined. This set of mdisk groups will then be filtered based on available space therein, a set of user/requester preferences, and optionally, a set of performance characteristics. Then, a particular mdisk group will be selected from the set of mdisk groups based on the filtering.

Abstract: Embodiments of the invention are directed to streaming virtual machine boot services over a network. An aspect of the invention includes booting a first virtual machine and recording data and metadata from a virtual machine boot image into a virtual machine boot file. The data and metadata are accessed in the process of booting the first virtual machine. The virtual machine boot image has setup information of the virtual machine type of the first virtual machine. The virtual machine boot file is configured for the virtual machine type of the first virtual machine. A descriptor is added to metadata of the virtual machine boot image, which references a location of the virtual machine boot file for the virtual machine type of the first virtual machine.

Abstract: Embodiments of the present invention provide an integrated host and subsystem port selection methodology that uses performance measurements combined with information about active data paths. This technique also helps in resilient fabric planning by selecting ports from redundant fabrics. In a typical embodiment, host port to storage port pairs that create a path between a host and a storage device will be identified. From these pairs, a set of host port to storage port candidates for communicate data from the host to the storage device will be identified based on a set of resiliency constraints. Then, a specific host port to storage port pair will be selected from the set based on a lowest joint workload measurement. A path will then be created between the specific host port and storage port, and data will be communicated from the host to the storage device via the path.

Abstract: Embodiments of the present invention provide lifecycle storage management for data within a Cloud computing environment. Specifically, a set of policies can be defined that allow for automatic valuation of the data and migration of the data between a set of storage tiers. Before a policy set is deployed, it can be assessed to determine effects it will have on cost, performance, and data location. Based on data characteristics and access patterns, a set of policy recommendations can be provided that predict the value of the data over time, and offer an improved migration strategy for moving the data between the set of storage tiers as the value of the data changes.

Abstract: This invention is directed to a system and method for the close coupling of Session Initiation Protocol (SIP) based Voice over Internet Protocol (VoIP) with multiplayer network games. Game state dependent voice communications are used as another method of player interaction in the game environment. The system uses SIP enhanced game servers and conference servers with audio mixing capabilities, and the game server controls the creation, maintenance and changing of the voice conferences. Participation in a voice conference is determined by the game state features associated with each player including distance and direction to determine how the voices are mixed in a voice conference.

Abstract: Systems and methods are provided for the close coupling of Session Initiation Protocol (SIP) based Voice over Internet Protocol (VoIP) with multiplayer network games. Game state dependent voice communications are used as another method of player interaction in the game environment. The system uses SIP enhanced game servers and conference servers with audio mixing capabilities, and the game server controls the creation, maintenance and changing of the voice conferences. Participation in a voice conference is determined by the game state features associated with each player including distance and direction to determine how the voices are mixed in a voice conference.

Abstract: A computer-implemented method for determining, from a system including a plurality of data center resources, at least one configuration of data center resources for an implementation of an application. The method includes receiving application information and receiving information regarding known internal features up the data center resources. The method also includes provisioning the system of data center resources and creating possible configurations of data center resources for implementing application. The method also includes correlating models and data center resources to create an interrelated representation of the models and the data center resources. The models predict a relationship of parameters for the possible configurations.

Abstract: Management of a planner through use of a middleware layer. A computer system is configured with the middleware layer in communication with both a planner and a data repository. One or more modules are provided in the middleware layer to support the functionality of the planner. Application program interface calls are employed to call the modules, thereby mitigating duplication of the functionality in separate planners.