Abstract: A method and system for optimizing power consumption of a data center by dynamic workload adjustment. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Abstract: Embodiments are provided for managing performance of a computer system. Both implicit and explicit recommendations for processing of tasks are provided. System performance is tracked and evaluation based upon the actions associated with the task. Future recommendations of the same or other tasks are provided based upon implicit feedback pertaining to system performance, and explicit feedback solicited from a system administrator.

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: A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to determine one or more optimal mappings between a server layer and a storage layer through a network layer based on performance metrics of one or more ports of at least one of the server layer, the storage layer and the network layer.

Abstract: A method and system for optimizing power consumption of a data center by dynamic workload adjustment. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Abstract: A method and system for optimizing power consumption of a data center by dynamic workload adjustment. At least one candidate workload solution for the data center is generated. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of the at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Abstract: Embodiments are provided for managing performance of a computer system. Both implicit and explicit recommendations for processing of tasks are provided. System performance is tracked and evaluation based upon the actions associated with the task. Future recommendations of the same or other tasks are provided based upon implicit feedback pertaining to system performance, and explicit feedback solicited from a system administrator.

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: 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 method, system, and article are provided for managing performance of a computer system. Both implicit and explicit recommendations for processing of tasks are provided. System performance is tracked and evaluated based upon the actions associated with the task. Future recommendations of the same or other tasks are provided based upon implicit feedback pertaining to system performance, and explicit feedback solicited from a system administrator.

Abstract: Embodiments of the present invention provide a hybrid (e.g., local and remote) approach for data backup in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a set of storage configuration parameters corresponding to a set of data to be backed up is received and stored in a computer data structure. The set of storage configuration parameters can comprise at least one of the following: a recovery time objective (RTO), a recovery point objective (RPO), and a desired type of protection for the set of data. Regardless, the set of data is compared to previously stored data to identify at least one of the following: portions of the set of data that have commonality with the previously stored data; and portions of the set of data that are unique to the set of data (i.e., not in common with any of the previously stored data). The above-described process is referred to herein as “de-duplication”.

Abstract: Embodiments of the present invention provide an approach for providing non-disruptive transitioning of application replication configurations and proactive analysis of possible error scenarios. Specifically, under embodiments of the present invention, a common integration model (CIM)-compatible representation of a system replication plan is provided in a computer data structure. Based on the representation, a hierarchical tree data structure having a set of nodes is created. A set of system configuration updates pertaining to the set of nodes are then classified (e.g., based upon the type of configuration update). Once the set of nodes has been classified, the set of nodes may then be analyzed to determine if any nodes of the set are isomorphic. If so, the plan can be modified accordingly. In any event, the replication plan (or modified replication plan) may then be implemented.

Abstract: Embodiments of the present invention provide a hybrid (e.g., local and remote) approach for data backup in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a set of storage configuration parameters corresponding to a set of data to be backed up is received and stored in a computer data structure. The set of storage configuration parameters can comprise at least one of the following: a recovery time objective (RTO), a recovery point objective (RPO), and a desired type of protection for the set of data. Regardless, the set of data is compared to previously stored data to identify at least one of the following: portions of the set of data that have commonality with the previously stored data; and portions of the set of data that are unique to the set of data (i.e., not in common with any of the previously stored data). The above-described process is referred to herein as “de-duplication”.

Abstract: Embodiments of the invention relate to testing a storage system point-in-time copy of data for consistency. An aspect of the invention includes receiving system and application event information from systems and applications associated with point-in-time copies of data. The system and application event information is associated with each of point-in-time copies of data. At least one point-in-time copy of data is selected for testing. The system and application event information is compared with inconsistency classes to determine tests for testing the point-in-time copy of data. The point-in-time copy of data is tested.

Abstract: Embodiments of the present invention provide an approach that utilizes discrete event simulation to quantitatively analyze the reliability of a modeled de-duplication system in a computer storage environment. In addition, the approach described herein can perform such an analysis on systems having heterogeneous data stored on heterogeneous storage systems in the presence of primary faults and their secondary effects due to de-duplication. In a typical embodiment, data de-duplication parameters and a hardware configuration are received in a computer storage medium. A data de-duplication model is then applied to a set of data and to the data de-duplication parameters, and a hardware reliability model is applied to the hardware configuration. Then a set (at least one) of discrete events is simulated based on the data de-duplication model as applied to the set of data and the data de-duplication parameters, and the hardware reliability model as applied to the hardware configuration.

Abstract: Embodiments of the present invention provide a hybrid (e.g., local and remote) approach for data backup in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a set of storage configuration parameters corresponding to a set of data to be backed up is received and stored in a computer data structure. The set of storage configuration parameters can comprise at least one of the following: a recovery time objective (RTO), a recovery point objective (RPO), and a desired type of protection for the set of data. Regardless, the set of data is compared to previously stored data to identify at least one of the following: portions of the set of data that have commonality with the previously stored data; and portions of the set of data that are unique to the set of data (i.e., not in common with any of the previously stored data). The above-described process is referred to herein as “de-duplication”.

Abstract: Disclosed are system and method embodiments for determining the root-causes of a performance objective violation, such as an end-to-end service level objection (SLO) violation, in a large-scale system with multi-tiered applications. This determination is made using a hybrid of component-level snapshots of the state of the system during a period in which an abnormal event occurred (i.e., black box mapping) and of known events and their causes (i.e., white-box mapping). Specifically, in response to a query about a violation (e.g., why did the response time for application a1 increase from r1 to r2), a processor will access and correlate the black-box and white-box mappings to determine a short-list of probable causes for the violation.

Abstract: A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to determine one or more optimal mappings between a server layer and a storage layer through a network layer based on performance metrics of one or more ports of at least one of the server layer, the storage layer and the network layer.

Abstract: Embodiments of the present invention provide an approach for providing non-disruptive transitioning of application replication configurations and proactive analysis of possible error scenarios. Specifically, under embodiments of the present invention, a common integration model (CIM)-compatible representation of a system replication plan is provided in a computer data structure. Based on the representation, a hierarchical tree data structure having a set of nodes is created. A set of system configuration updates pertaining to the set of nodes are then classified (e.g., based upon the type of configuration update). Once the set of nodes has been classified, the set of nodes may then be analyzed to determine if any nodes of the set are isomorphic. If so, the plan can be modified accordingly. In any event, the replication plan (or modified replication plan) may then be implemented.