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: Embodiments for efficiently computing complex statistics from historical time series data are provided. A hierarchical summarization method includes receiving at least one stream of data and creating data blocks from the at least one stream of data. In another embodiment, a method for computing statistics for historical data includes accessing at least one online stream of historical data, the online stream of historical data including metadata, and creating data blocks from the at least one online stream of historical data. Each data block includes a pair of timestamps indicating a sampling start time and a sampling end time, a number of data samples spanned by the data block, a SUM(X) statistic, a SUM(XX) statistic, and a SUM(XY) statistic computed for the data samples spanned by the data block. Other methods are also presented, such as methods for efficiently and accurately calculating statistical queries regarding historical data for arbitrary time ranges, among others.

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: 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 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 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 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: A method, system and computer program product are disclosed for simulating a storage area network including a set of correlated devices, each of the devices having a device agent. The method comprises the step of forming a set of simulation agents representing said device agents, including the steps of, (i) for each of the simulation agents, obtaining a set of agent profiles, and storing said agent profiles in a data store, and (ii) obtaining files describing class definitions for the simulation agents, and storing said files in the data store. With this information and data, a Visual Workbench is used to generate a display of said simulation agents. The preferred embodiment provides a framework and implementation that simulates the CIM agent of any SAN device. Each individual device CIM agent can be simulated in this framework based on the specification defined in an XML file and/or through snapshot mechanism.

Abstract: A data center power cost accounting system uses server and storage and cooling power consumption models and device maps, together with runtime application maps, to estimate the equipment power consumption and cooling power consumption of individual applications. An approximation of the cooling cost over a period of time, for any given application, can be pieced together by adding up the equipment utilized by the application and applying the cooling estimates obtained from computational fluid dynamics (CFD) simulations. The cooling estimates can further account for changes or variability in resource usage over time since the cooling estimates are based directly on utilization. The per application power consumption costs are obtained without having to install or depend on power measurement instruments or other hardware in the datacenters.

Abstract: A relationship visualization system provides a user interface by which a user may select from among entities, elements, and sub-elements as an initial point in a desired relationship for display. The system designates the selected entities, elements, and sub-elements originating endpoints for the desired relationship. The system displays the relationship between entities, elements, and sub-elements to facilitate visualization and management of the information technology system. The relationship comprises a data path. The display of the relationship comprises a graphical view and a tabular view.

Abstract: A method, system, and article are provided for monitoring performance of hardware devices. Each hardware device is configured with an agent, and the server is configured with a coordinator. The agent collects device data at a first modifiable frequency and communicates the collected data to the coordinator at a second dynamically modifiable frequency. The collected data is periodically monitored and the first and second frequencies are modified subject to evaluation of the collected and monitored data.

Abstract: An apparatus, system, and method are disclosed for interacting with multi-attribute managed resources as groups. The apparatus, system and method identify in a repository one or more groups associated with a target attribute. The apparatus system and method further determine a set of managed resources from the repository wherein each managed resource in a set comprises the same value for the target attribute. The apparatus system and method further associate each set of managed resources with a group. The apparatus system and method may subdivide the groups into sub-groups according to additional attributes possessed by members of each group and may display the groups and sub-groups to summarize and highlight the managed resources of each group and sub-group.

Abstract: The invention provides an enterprise administration system and method. The system includes a user interface module configured to enter administration terms or select a predetermined script of administration terms, a knowledge base configured to store system information, a meta information module configured to use the system information to store entity-objective indexes, and a workflow mapping module configured to map the administration terms to system information extraction tasks to extract relevant entities and objectives and apply a rule to the extracted entities and objectives for presenting the extracted entities and objectives in a ranked order.

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: Disclosed is a technique for correcting a configuration problem. The configuration problem is detected. It is determined whether there is at least one solution for the configuration problem in a knowledge data store. When it is determined that there is at least one solution in the knowledge data store, automatically selecting a solution to solve the configuration problem. When said solution can be automatically applied, automatically applying said solution. When said solution cannot be automatically applied, notifying a user.

Abstract: A technique for performing configuration checking of a network is provided. A network data store is scanned for at least one transaction. At least one event is generated for the transaction. At least one configuration policy is associated with the event. The configuration policy is compared with configuration data associated with the event. It is determined whether the configuration policy has been violated based on the comparison.

Abstract: The invention provides a method and system for continuous optimization of a data center. The method includes monitoring loads of storage modules, server modules and switch modules in the data center, detecting an overload condition upon a load exceeding a load threshold, combining server and storage virtualization to address storage overloads by planning allocation migration between the storage modules, to address server overloads by planning allocation migration between the server modules, to address switch overloads by planning allocation migration mix between server modules and storage modules for overload reduction, and orchestrating the planned allocation migration to reduce the overload condition in the data center.

Abstract: An apparatus, system, and method are disclosed for detecting entity changes in a storage area network. A discovery module probes a plurality of entities of the storage area network to detect an event in response to a policy, stores attributes of the probed entities, and reports the event with a probe event reference. A monitor module receives a self-announced event reference of the event if an entity communicates the event. A configuration module records the event as an initiated event reference if the event is initiated by an administrator. An event manager synthesizes a report of the event from the event references.