Abstract: An optimization method and system. The method includes receiving by a computing system a data footprint associated with data and a human resource model. The data footprint comprises a primary data section, a secondary data section, and an archive data section. A plurality of data storage strategies are associated with the primary data section, said secondary data section, and said archive data section. The plurality of data storage strategies are compared to each other. A data staging orchestrator software module is executed. The computing system determines based on results of executing the data staging orchestrator software module, an optimal migration time, an optimal migration speed, and an optimal migration cost for managing storage for portions of the data. The computing system executes a risk modulation software module and determines a risk associated with the managing.

Abstract: Embodiments of the present invention provide an approach to provision storage resources (e.g., across an enterprise storage system (ESS) such as a general parallel file system (GPFS) or the like) for different workloads in an energy efficient manner. The system evaluates different energy profiles/workloads' energy consumption characteristics of storage devices to determine an allocation plan that reduces the energy cost (e.g., results in the lowest cost/energy consumption for handling a storage workload). In a typical embodiment, 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. In general, the allocation plan is based upon the energy consumption characteristics and an energy efficiency algorithm.

Abstract: The present invention provides technology neutral process integration (Cloud Resource Planning), and optimization methodology leveraging a business meta-schema format Cloud Data Interchange (CDI) to integrate, enable, and invoke Cloud services. One example is that the present invention provides a management layer at the process level. There can be multiple Cloud implementations/types within a govern enterprise—perhaps utilizing different infrastructure (e.g., hardware of one supplier versus that of another) or different areas of functionality (computing services, storage services, etc). This disclosure provides an abstraction or ‘resource planning’ layer above these core services such that a customer does not have to have knowledge or choose different Cloud types and/or understand or choose each underlying service. As such, it provides a ‘one stop’ portal.

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: A system and associated method for continuously optimizing data archive management scheduling. A job scheduler receives, from an archive management system, inputs of task information, replica placement data, infrastructure topology data, and resource performance data. The job scheduler models a flow network that represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices according to the received inputs. An optimal path in the modeled flow network is computed as an initial schedule, and the archive management system performs tasks according to the initial schedule. The operations of scheduled tasks are monitored and the job scheduler produces a new schedule based on feedbacks of the monitored operations and predefined heuristics.

Abstract: Embodiments of the present invention provide an approach to provision storage resources (e.g., across an enterprise storage system (ESS) such as a general parallel file system (GPFS) or the like) for different workloads in an energy efficient manner. The system evaluates different energy profiles/workloads' energy consumption characteristics of storage devices to determine an allocation plan that reduces the energy cost (e.g., results in the lowest cost/energy consumption for handling a storage workload). In a typical embodiment, 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. In general, the allocation plan is based upon the energy consumption characteristics and an energy efficiency algorithm.

Abstract: A system and associated method for continuously optimizing data archive management scheduling. A job scheduler receives, from an archive management system, inputs of task information, replica placement data, infrastructure topology data, and resource performance data. The job scheduler models a flow network that represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices according to the received inputs. An optimal path in the modeled flow network is computed as an initial schedule, and the archive management system performs tasks according to the initial schedule. The operations of scheduled tasks are monitored and the job scheduler produces a new schedule based on feedbacks of the monitored operations and predefined heuristics.

Abstract: An optimization method and system. The method includes receiving by a computing system a data footprint associated with data and a human resource model. The data footprint comprises a primary data section, a secondary data section, and an archive data section. A plurality of data storage strategies are associated with the primary data section, said secondary data section, and said archive data section. The plurality of data storage strategies are compared to each other. A data staging orchestrator software module is executed. The computing system determines based on results of executing the data staging orchestrator software module, an optimal migration time, an optimal migration speed, and an optimal migration cost for managing storage for portions of the data. The computing system executes a risk modulation software module and determines a risk associated with the managing.

Abstract: An optimization method and system. The method includes receiving by a computing system a data footprint associated with data and a human resource model. The data footprint comprises a primary data section, a secondary data section, and an archive data section. A plurality of data storage strategies are associated with the primary data section, said secondary data section, and said archive data section. The plurality of data storage strategies are compared to each other. A data staging orchestrator software module is executed. The computing system determines based on results of executing the data staging orchestrator software module, an optimal migration time, an optimal migration speed, and an optimal migration cost for managing storage for portions of the data. The computing system executes a risk modulation software module and determines a risk associated with the managing.

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: 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 discussed in this disclosure provide an integrated provisioning framework that automates the process of provisioning storage resources, end-to-end, for an enterprise storage cloud environment. Such embodiments configure and orchestrate the deployment of a user's workload and, at the same time, provide optimization across a multitude of storage cloud resources. Along these lines, input is received in the form of workload requirements and configuration information for available system resources. Based on the input, a set (at least one) of storage cloud configuration plans is developed that satisfy the workload requirements. A set of scripts is then generated that orchestrate the deployment and configuration of different software and hardware components based on the plans.

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 system and associated method for automated disaster recovery (DR) planning. A DR planning process receives disaster recovery requirements and a target environment configuration from a user to design DR plans for the target environment configuration that meets disaster recovery requirements. The DR planning process accesses a knowledgebase containing information on replication technologies, best practice recipes, and past deployment instances. The DR planning process creates the DR plans by analyzing the disaster recovery requirements into element risks, associating replication technologies to protect each element risks, combining associated replication technologies based on the best practice recipes, and selecting highly evaluated combination based on the past deployment instances. The DR planning process presents the DR plans as classified by replication strategy-architecture combination for each DR plans and marks how strongly each DR plans are recommended.

Abstract: The present invention provides technology neutral process integration (Cloud Resource Planning), and optimization methodology leveraging a business meta-schema format Cloud Data Interchange (CDI) to integrate, enable, and invoke Cloud services. One example is that the present invention provides a management layer at the process level. There can be multiple Cloud implementations/types within a govern enterprise—perhaps utilizing different infrastructure (e.g., hardware of one supplier versus that of another) or different areas of functionality (computing services, storage services, etc). This disclosure provides an abstraction or ‘resource planning’ layer above these core services such that a customer does not have to have knowledge or choose different Cloud types and/or understand or choose each underlying service. As such, it provides a ‘one stop’ portal.