Abstract: Techniques for determining one or more disaster recovery (DR) service level agreements (SLAs) for each of one or more components of an application are provided. The techniques include identifying one or more components of an application, capturing one or more intra-application data dependencies between the one or more components, and mapping each of the one or more components to a DR profile to determine one or more DR SLAs for each of the one or more components of an application.

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 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: 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: Provided are methods, apparatus arid computer programs for scheduling storage input and/or output (I/O) requests. A method for scheduling storage access requests determines a request processing sequence calculated to maximize SLA-based revenues achievable from processing a number of requests. A storage controller includes a scheduler which implements a revenue-based scheduling function to determine a revenue-maximizing processing sequence, and then assigns storage access requests to locations in a queue corresponding to the determined sequence. In an on-line mode, the scheduler can adapt to additional received requests, evaluating the revenue function for the additional requests and modifying the schedule if required. The method may include analyzing a request stream to predict requests that are likely to be received in the near future, and taking account of the predicted requests when determining a processing schedule.

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: 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: 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: Logical data stores are placed on storages to minimize store request time. The stores are sorted. A store counter and a storage counter are each set to one. (A), (B), and (C) are repeated until the storage counter exceeds the number of storages within the array. (A) is setting a load for the storage specified by the storage counter to zero. (B) is performing (i), (ii), and (iii) while the load for the storage specified by the storage counter is less an average determined load over all the storages. (i) is allocating the store specified by the store counter to the storage specified by the storage counter; and, (ii) is incrementing the load for this storage by this storage's request arrival rate multiplied by an expected service time for the requests of this store. (iii) is incrementing the store counter by one. (C) is incrementing the storage counter by one.

Abstract: Logical data stores are placed on storages to minimize store request time. The stores are sorted. A store counter and a storage counter are each set to one. (A), (B), and (C) are repeated until the storage counter exceeds the number of storages within the array. (A) is setting a load for the storage specified by the storage counter to zero. (B) is performing (i), (ii), and (iii) while the load for the storage specified by the storage counter is less an average determined load over all the storages. (i) is allocating the store specified by the store counter to the storage specified by the storage counter; and, (ii) is incrementing the load for this storage by this storage's request arrival rate multiplied by an expected service time for the requests of this store. (iii) is incrementing the store counter by one. (C) is incrementing the storage counter by one.

Abstract: Provided are methods, apparatus arid computer programs for scheduling storage input and/or output (I/O) requests. A method for scheduling storage access requests determines a request processing sequence calculated to maximize SLA-based revenues achievable from processing a number of requests. A storage controller includes a scheduler which implements a revenue-based scheduling function to determine a revenue-maximizing processing sequence, and then assigns storage access requests to locations in a queue corresponding to the determined sequence. In an on-line mode, the scheduler can adapt to additional received requests, evaluating the revenue function for the additional requests and modifying the schedule if required. The method may include analysing a request stream to predict requests that are likely to be received in the near future, and taking account of the predicted requests when determining a processing schedule.

Abstract: Prediction-based online admission control for incoming jobs has an explicit objective of optimizing a utility function. The input to an algorithmic procedure is a set of requests made in respect of a network service. Each request has information about the length of the request. An output of the algorithmic procedure is a selected subset of requests that can be served within the capacity constraints of the network service, such that the utility function is approximately optimized (for example, minimized or maximized) depending on the context of the particular application.

Abstract: Provided are methods, apparatus and computer programs for scheduling storage input and/or output (I/O) requests. A method for scheduling storage access requests determines a request processing sequence calculated to maximize SLA-based revenues achievable from processing a number of requests. A storage controller includes a scheduler which implements a revenue-based scheduling function to determine a revenue-maximizing processing sequence, and then assigns storage access requests to locations in a queue corresponding to the determined sequence. In an on-line mode, the scheduler can adapt to additional received requests, evaluating the revenue function for the additional requests and modifying the schedule if required. The method may include analysing a request stream to predict requests that are likely to be received in the near future, and taking account of the predicted requests when determining a processing schedule.

Abstract: A method for performing a data migration task on an on-line data storage system comprises computing a migration utility, which is a function of the expected time taken to complete the data migration task and generating migration requests for performing the data migration task, where the data migration task is divided into sub-tasks and a migration request is generated for each sub-task. Next determining a migration deadline for performing the data migration; assigning reward values to customer storage requests; assigning reward values to the migration requests.

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: Provided are methods, apparatus and computer programs for scheduling storage input and/or output (I/O) requests. A method for scheduling storage access requests determines a request processing sequence calculated to maximize SLA-based revenues achievable from processing a number of requests. A storage controller includes a scheduler which implements a revenue-based scheduling function to determine a revenue-maximizing processing sequence, and then assigns storage access requests to locations in a queue corresponding to the determined sequence. In an on-line mode, the scheduler can adapt to additional received requests, evaluating the revenue function for the additional requests and modifying the schedule if required. The method may include analysing a request stream to predict requests that are likely to be received in the near future, and taking account of the predicted requests when determining a processing schedule.

Abstract: Prediction-based online admission control for incoming jobs has an explicit objective of optimizing a utility function. The input to an algorithmic procedure is a set of requests made in respect of a network service. Each request has information about the length of the request. An output of the algorithmic procedure is a selected subset of requests that can be served within the capacity constraints of the network service, such that the utility function is approximately optimized (for example, minimized or maximized) depending on the context of the particular application.