International Business Machines Corporation

Gauri Sudhir Shah

Ramani Ranjan Routray

Kaladhar Voruganti

Sandeep Gopisetty

Murthy V Devarakonda

Kang-Won Lee

Norbert George Vogl

Chung-hao Tan

David Alson George

Konstantinos Magoutis

Dakshi Agrawal

Seraphin Bernard Calo

Akshat Verma

Rohit Jain

Srinivasan Balasubramanian

Roberto C Pineiro

Tushar Mohan

Sandeep Madhav Uttamchandani

Prasenjit Sarkar

Chatschik Bisdikian

Aameek Singh

Steve Richard White

Norman Jerome Pass

Joseph D Ryan

Techniques for automatically designing an application-requirement based configuration for a distributed computing system are provided. One or more application-level templates are obtained, wherein the one or more templates are representative of one or more requirements associated with one or more applications. Using the one or more application-level templates, one or more logical flows are created. The one or more logical flows are then used to generate one or more system-level configuration plans of the distributed computing system.

Application-requirement based configuration designer for distributed computing systems

A method for validating that a networked system of modules conforms to at least one interoperability rule pertinent to interoperability of at least two of the modules includes the steps of obtaining the at least one interoperability rule, the at least one rule being encoded in computer-operable format; obtaining configuration information pertinent to at least one of the modules, the configuration information being encoded in computer-operable format; and comparing the at least one interoperability rule to the configuration information to validate whether the system conforms to the at least one interoperability rule

Validation of module interoperability

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. A solution template library is generated for mapping the application to each of a plurality of candidate replication technology solutions. The template library is used to select a DR plan in the form of a replication technology solution for the application.

Methods, systems, and computer program products for disaster recovery planning

A policy-based resource management system provides an infrastructure that uses high-level definitions of data-types along with desired policy rules to manage legacy data. In particular, relevant legacy data is identified as well as the characteristics of storage devices where the legacy data resides. The storage management system determines what if any of the legacy data is stored in a manner that is non-compliant with the policy rules and corrects any violations. It generates corrective action plans, and then uses existing data migration software to migrate the data. Accordingly, the legacy data may be managed in a policy-based manner without requiring typical storage management software.

Policy based resource management for legacy data

A storage provisioning system generates a storage management framework comprising a resource model representing a set of storage devices for use by an application. The resource model comprises a set of data containers and at least one volume container such that the resource model provides storage to the application independent of a plurality of interfaces used by the set of storage devices. The volume container is a specialized data container that interfaces directly with the storage devices and represents a bottom of a storage stack comprising at least one data container and at least one volume container. The resource model comprises a rules module for governing the construction of the data containers and the volume container and association between the data containers and the volume container.

System and method for providing automated storage provisioning

A multi-plan generation system generates a multi-plan for a multi-layer storage area network. The system generates a predetermined number of plans while generating and retaining globally optimum plans. The system dynamically calculates and orders these plans according to optimization criteria and user requirements. The system comprises a planner for each of the layers in a storage area network such as a host planner, a storage planner, a network planner, a zone planner, etc. Each of the planners applies a variance optimization, a preview optimization, and a detail optimization and interacts with a policy repository to obtain policy guidance, enabling the system to prune the set of plans. A control module invokes the planners in a predetermined order.

System and method for generating a multi-plan for a multi-layer storage area network

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San Jose, CA, United States of America

Gauri Sudhir Shah