Abstract: Embodiments relate to cross-domain service request placement in a software defined environment (SDE). An aspect includes receiving a service request corresponding to a job to be completed in the SDE. Another aspect includes determining a first computer device in a first domain, and a second computer device in a second domain, that are capable of performing the service request. Another aspect includes determining, for the first and second computer devices, first and second pluralities of available service classes. Another aspect includes determining, for the first and second computer devices, a first and second plurality of costs of performing the service request, wherein each of the first and second plurality of costs corresponds to a single respective service class. Yet another aspect includes selecting one of the first computer device and the second computer device to perform the service request based on the first and second plurality of costs.

Abstract: Embodiments relate to cross-domain service request placement in a software defined environment (SDE). An aspect includes receiving a service request corresponding to a job to be completed in the SDE. Another aspect includes determining a first computer device in a first domain, and a second computer device in a second domain, that are capable of performing the service request. Another aspect includes determining, for the first and second computer devices, first and second pluralities of available service classes. Another aspect includes determining, for the first and second computer devices, a first and second plurality of costs of performing the service request, wherein each of the first and second plurality of costs corresponds to a single respective service class. Yet another aspect includes selecting one of the first computer device and the second computer device to perform the service request based on the first and second plurality of costs.

Abstract: Embodiments of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.

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: 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 protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Under embodiments of the present invention, data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Abstract: Embodiments of the present invention provide an approach for protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Under embodiments of the present invention, data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Abstract: A virtual machine is assigned to a target physical server based on virtualization parameters for maximizing utility of a multiple virtual machines and physical servers. Resource allocation is performed for and deployment of the virtual machine to the target physical server based on capabilities of the target physical server and multiple virtual machine resource requirements. The virtualization parameters include a minimum parameter, a maximum parameter and a shares parameter. Processing resources are allocated based on utility priority of applications operating on the virtual machine using the shares parameter of contending virtual machines to determine a processing cycle ratio for distributing processing cycles between different utility priority applications operating on the contending virtual machines.

Abstract: Embodiments of the present invention provide performance isolation for storage clouds. Under one embodiment, workloads across a storage cloud architecture are grouped into clusters based on administrator or system input. A performance isolation domain is then created for each of the clusters, with each of the performance isolation domains comprising a set of data stores associated with a set of storage subsystems and a set of data paths that connect the set of data stores to a set of clients. Thereafter, performance isolation is provided among a set of layers of the performance isolation domains.

Abstract: The present invention provides an approach to provision storage resources (e.g., across an enterprise storage system) for different workloads in an energy efficient manner. Typically, energy consumption characteristics for handling a particular storage workload will be determined. Thereafter, a type of storage device capable of handling the workload will be determined. Then, an allocation plan that results in the most efficient energy consumption for handling the workload will be developed. The allocation plan is based upon the energy consumption characteristics and an energy efficiency algorithm. The energy efficiency algorithm serves to identify storage device(s) that can handle the workload in such a way as to reduce total energy consumption and, accordingly, costs. The energy efficiency algorithm may also consider other factors such as capacity and load of storage devices and service level agreement (SLA) terms. At least one storage device can then be selected for handling the storage workload.

Abstract: Server consolidation using virtual machine resource tradeoffs, is provided. One implementation involves assigning a virtual machine to a target physical server based on a plurality of virtualization parameters for maximizing utility of a plurality of virtual machines and physical servers. The assigning performs resource allocation for the virtual machine based on capabilities of the target physical server and a plurality of virtual machine resource requirements. Virtualization parameters include a reservation parameter (min) representing a minimum resources required for a VM, a limit parameter (max) representing a maximum resources allowable for the VM, and a weight parameter (shares) representing a share of spare resources for the VM.

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: Embodiments relate to cross-domain service request placement in a software defined environment (SDE). An aspect includes receiving a service request corresponding to a job to be completed in the SDE. Another aspect includes determining a first computer device in a first domain, and a second computer device in a second domain, that are capable of performing the service request. Another aspect includes determining, for the first and second computer devices, first and second pluralities of available service classes. Another aspect includes determining, for the first and second computer devices, a first and second plurality of costs of performing the service request, wherein each of the first and second plurality of costs corresponds to a single respective service class. Yet another aspect includes selecting one of the first computer device and the second computer device to perform the service request based on the first and second plurality of costs.

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: 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 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: The present invention provides an approach for automatic storage planning and provisioning within a clustered computing environment (e.g., a cloud computing environment). The present invention will receive planning input for a set of storage area network volume controllers (SVCs), the planning input indicating a potential load on the SVCs and its associated components. 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. Then, performance data for the candidate storage components will be analyzed to identify an SVC and an Mdisk group to address the potential load.

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: Embodiments of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.

Abstract: An apparatus, system, and method are disclosed for monitoring computer system components in large or complex systems. The apparatus includes an identifier module for associating at least one visual identifier with a computer system component. A function module associates one or more control functions with the visual identifier. A presentation module selectively displays the at least one identifier for the computer system component within a present view of a user interface. A monitoring module monitors the computer system component associated with the at least one identifier and modifies the identifier in response to a change in operational status for the computer system component.