Abstract: Systems determine workload resource usage patterns of a computerized workload, using a computerized device. Such systems use the computerized device to place the computerized workload with a computer server cluster within a private cloud computing environment. Also, systems herein place the computerized workload on a selected computer server within the computer server cluster that has a resource usage pattern complementary to the workload resource usage profile, also using the computerized device. The complementary resource usage pattern peaks at different times from the workload resource usage patterns.

Abstract: A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins.

Abstract: Techniques for dynamically selecting a server state for one or more servers in a cluster of servers are provided. The techniques include tracking each active and sleep state of each server in a cluster of servers, and selecting a server state for one or more servers in the cluster of servers to meet one or more workload level requirements of the cluster of servers, wherein selecting a server state for one or more servers comprises scheduling a transition between one or more active and sleep states for the one or more servers, wherein scheduling the transition comprises using power consumption information for each state and transition time information for each transition.

Abstract: N applications are placed on M virtualized servers having power management capability. A time horizon is divided into a plurality of time windows, and, for each given one of the windows, a placement of the N applications is computed, taking into account power cost, migration cost, and performance benefit. The migration cost refers to cost to migrate from a first virtualized server to a second virtualized server for the given one of the windows. The N applications are placed onto the M virtualized servers, for each of the plurality of time windows, in accordance with the placement computed in the computing step for each of the windows.

Abstract: N applications are placed on M virtualized servers having power management capability. A time horizon is divided into a plurality of time windows, and, for each given one of the windows, a placement of the N applications is computed, taking into account power cost, migration cost, and performance benefit. The migration cost refers to cost to migrate from a first virtualized server to a second virtualized server for the given one of the windows. The N applications are placed onto the M virtualized servers, for each of the plurality of time windows, in accordance with the placement computed in the computing step for each of the windows.

Abstract: A method, system and computer-usable medium are disclosed for optimizing the power consumption of a plurality of information processing systems. Historical usage data representing power usage of a plurality of information processing systems is retrieved in response to a request to generate power savings recommendations. Statistical analysis is performed on the historical usage data are to determine usage patterns, which are then further analyzed to determine repetitions of the usage patterns. In turn, the repetitions of the usage patterns are analyzed to generate power consumption management recommendations to initiate power consumption management actions at particular times. One or more business constraints are determined, which are used to generate constraints to the power consumption management recommendations.

Abstract: An automated disaster recovery (DR) planning system for a computing environment is provided. A discovery module discovers servers, networks, and storage devices in a computing environment. An expert knowledge base module captures best practices in planning, and capabilities, interoperability, limitation and boundary values for different DR technologies. A match-making module determines multiple DR plans as combinations of one or more replication technologies that can be used to satisfy DR requirements. And, an optimizer configured for assessing a feasible DR plan from said multiple DR plans, to deploy for DR planning of a primary computing environment.

Abstract: Techniques for virtual machine placement in a datacenter are provided. The techniques include using one or more server power characteristics to determine a power cost for any given virtual machine placement in the datacenter, using a heat profile of one or more datacenter components in relation to one or more cooling resources in the datacenter to determine cooling cost for any given virtual machine placement in the datacenter, and using the power cost and cooling cost to apply integrated energy cost minimization at one or more levels of hierarchy in the datacenter to determine placement of a virtual machine in the datacenter such that the integrated energy cost of the datacenter is minimized.

Abstract: Embodiments of the invention provide power savings via performing application workload consolidation to servers using off-peak values for application workload demand. Embodiments of the invention are designed to achieve significant power savings while containing performance risk associated with server consolidation.

Abstract: A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins.

Abstract: A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins.

Abstract: Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.

Abstract: The embodiments of the invention provide a method of identifying a recovery point in a continuous data protection (CDP) log. More specifically, the method begins by detecting corrupted data in the CDP log and identifying the nature of corruption. Next, the nature of corruption is mapped to applications to identify components that may have caused the corrupted data. The method then finds a time instance of uncorrupted data in the components. Specifically, this can include searching CDP log entries in an order independent of log event age. Alternatively, the process of finding the time instance can include creating a data image of a first copy of uncorrupted data and sequentially apply entries of the CDP log until the corrupted data is reached.

Abstract: Techniques for dynamically selecting a server state for one or more servers in a cluster of servers are provided. The techniques include tracking each active and sleep state of each server in a cluster of servers, and selecting a server state for one or more servers in the cluster of servers to meet one or more workload level requirements of the cluster of servers, wherein selecting a server state for one or more servers comprises scheduling a transition between one or more active and sleep states for the one or more servers, wherein scheduling the transition comprises using power consumption information for each state and transition time information for each transition.

Abstract: Energy saving efforts should not compromise data center performance. An energy management application can determine usage patterns in historical energy usage data based on statistical analysis and energy models. Energy savings recommendations can be generated for future points-in-time based on the usage patterns. Business constraints can be applied to the energy savings recommendations to ensure that the energy savings recommendations meet performance requirements.

Abstract: Techniques for minimizing storage power consumption are provided. The techniques include generating one or more physical storage volumes and one virtual storage volume for each physical storage volume, creating a mapping from virtual storage volumes to physical storage volumes, determining input/output (I/O) access behavior of one or more applications using statistical analysis, and re-mapping the virtual to physical volume mapping based on the determined I/O access behavior of the one or more applications to minimize storage power consumption while meeting a required performance.

Abstract: Techniques for placing at least one composite application in a federated environment are provided. The techniques include analyzing a composite application to be deployed in a federated environment, obtaining one or more application artifacts, analyzing feasibility of placing one or more application components at one or more clusters in the federated environment without knowledge of resource availability at each of the one or more clusters, and generating a mapping of the one or more application components to the one or more clusters such that an application requirement is met, wherein the one or more application artifacts are distributed across a federated environment.

Abstract: A policy for a computer system is transformed into a plurality of sub-policies, at least one synchronization step, and a final action. The sub-policies are distributed to corresponding sub-components in the computer system. The sub-policies are evaluated, wherein each sub-component evaluates its corresponding one or more sub-policies as other sub-components evaluate their corresponding one or more sub-policies. The sub-policies are synchronized by using the at least one synchronization step. The final action is performed in response to synchronizing the sub-policies. The computer system can be singular or distributed.

Abstract: N applications are placed on M virtualized servers having power management capability. A time horizon is divided into a plurality of time windows, and, for each given one of the windows, a placement of the N applications is computed, taking into account power cost, migration cost, and performance benefit. The migration cost refers to cost to migrate from a first virtualized server to a second virtualized server for the given one of the windows. The N applications are placed onto the M virtualized servers, for each of the plurality of time windows, in accordance with the placement computed in the computing step for each of the windows.

Abstract: Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.