Emc Corporation

Serge Marokhovsky

Christopher Chaulk

Andrew S Becher

Sadasiva Prathab

Shuzi Chen

Valery Altman

Zakir G Magdum

Anuradha Shivnath

Yongmei Xu

Hanna Yehuda

Eric Young

Ilya Liubovich

Francois Gauvin

David Barta

Serena M Doyle

Paul Timmins

Gregory MacKinnon

Jay S Stevens

Steven Tanaka

Viren Pherwani

Joshua Onffroy

Sarah Yongmei Xu

Sunil Ramchandra Upalekar

Joseph Berry

Ravi Luhadiya

Douglas Leech

Valeria Altman

Anthony Ward

A report generator of a storage area network management application includes a data validation mechanism that validates data related to managed resources operating in a storage area network. The data validation mechanism obtains first and second data sets concerning first and second characteristics of a resource operating in the storage area network and applies at least one data validation test using data sets to detect inconsistencies in data in the first and second data sets associated with the resource operating in the storage area network. Values can be compared in different columns of the same or different reports to detect report integrity errors or data mining errors. The report generator provides a storage area network management report based on data of the first data set and the second data set that accounts for inconsistencies in data associated with the resource as detected by the at least one data validation test.

Methods and apparatus for performing data validation in a network management application

A system displays a set of alerts in an alert view. Each alert indicates a problem associated with a managed resource operating in a storage area network. The system receives a selection of an alert from the alert view, and performs an analysis of managed resource data associated with the managed resource experiencing the alert to identify performance details concerning the alert in relation to the managed resource data. The system then presents the performance details associated with the alert in a performance view. The performance view includes a topology region identifying a location of the managed resource experiencing the alert within the storage area network, and a graph region identifying the alert in relation to managed resource data for the managed resource experiencing the alert.

Methods and apparatus providing root cause analysis on alerts

Deployment of new elements in a computer network can result in development of patch kits adapters, and other software and trigger corresponding regressive test efforts. In a storage area network, a method is disclosed for defining a general, extensible model of a manageable entity for use in management of the entity. The method includes obtaining a set of attribute values related to the manageable entity in relation to the storage area network. A deployment engine identifies corresponding entity categories within a storage area network entity model that contains attribute definitions that correspond to respective attribute values in the set of attribute values obtained from the manageable entity. The deployment engine assigns the respective attribute values of the manageable entity to the corresponding respective attribute definitions identified in the model. The deployment engine then determines if all attribute values of the manageable entity have been assigned. If unassigned attribute values remain, a classification processor defines new entity categories to contain new attribute definitions, and assigns the unassigned attributes value to the new attribute definitions contained in the new entity category.

Methods and apparatus providing an extensible manageable entity model for a network

A decision support system including a query generation tool provides a Graphical User Interface (GUI) for generating a query of a Storage Area Network (SAN) database using interactive screen displays identifying manageable entities in the SAN and queryable statistics thereof. Configurations herein provide a query mechanism, or interface, that generates an executable query via a GUI having intuitive mnemonic labels corresponding to the real-world (i.e. SAN manageable entities) represented in the query database, rather than the technical database table and attribute names which tend to be more cryptic, particularly to an uninitiated query operator. The GUI presents a sequence of screens for selecting SAN manageable entities, report fields, selection criteria, and output format. The query logic computes SQL syntax for identifying corresponding database tables and attributes, performing the required joins between the tables, and selecting the report set of records for output in the resulting SQL query.

System and methods for querying a report database

Described are techniques used in connection with management and processing of configuration data and performance data about a data storage system. The data is collected by one or more agents and forwarded to store components. The store components are responsible for maintaining persistent data objects in a repository. A server may process user requests issued from one or more consoles by retrieving data from the repository and displaying information based thereon on the consoles. The data collected by the agents may be in accordance with a first object model or data representation different from another object model or data representation of the repository. The store components update the repository using data collected by the agents.

Population of discovery data

A Decision Support System (DSS) coalescer offloads and transforms data from an online statistical gathering database to a report query database adapted for report generation. The coalescer generates and populates a query database structured for query data retrieval, in contrast to realtime access transaction storage as in the online database. Configurations transform the online database into a star join schema (table structure) that centralizes database 'facts' in a central table and forms associations to database 'dimensions' upon which a query is likely to be based. The facts refer to items which tend to change regularly over time, such as sales volumes and unit shipped. Conversely, the dimensions include more static values such as time (e.g. days, weeks, and quarters) and geographic regions, such as states and countries. The resulting query model (schema) includes relatively small dimensions tables associated with (i.e. having a schema association to) a relatively larger fact table.

System and methods for modeling a report query database

A storage area network (SAN) management application generates device allocation reports displaying foundation variables, device specific parameters, and computed, derived fields for different types of storage arrays, without burdening the allocation report with extraneous parameters through the use of a layout indicative of the information included on the report, providing a streamlined and seamless allocation report. The SAN management application defines a layout indicative of the foundation variables, device attributes, and derived fields requested in an allocation report. The user selected layout indicates the requested allocation parameters for a report, indicative of the foundation variable, device attributes, and derived fields, and also indicates the device usage metrics for computing the derived fields from the foundation variables and device attributes. In this manner, a SAN operator requests an allocation report indicative of only the information sought, and need not correlate multiple reports or manually synthesize report output for determining derived fields.

System and methods for reporting device allocation

Collecting data for a storage area network includes providing a plurality of agents that collect data from objects of the storage area network, providing a plurality of data collection policies, where each of the policies indicates a type of data to be collected and a frequency of collection, and applying the policies to the agents, where the agents collect data according to the type of data and the frequencies indicated by the data collection policies. At least one of the agents may have a plurality of data collection policies applied thereto to cause the at least one agent to collect the types of data at the frequencies indicated by the merge of all of the data collection policies applied to the at least one agent. Merging the data collection policies may be performed by the at least one of the agents that receives the data collection policies or may be performed by an entity other than the at least one of the agents.

Data collection policy for storage devices

Storage arrays are subdivided into a plurality of physical and logical entities to provide a framework of the available storage. In a large SAN, the network of storage arrays may define a complex arrangement of such physical and logical entities to partition the total storage volume. Therefore, the storage area network classifies a series of storage categories corresponding to different levels of the physical and logical entities representing subdivisions of the storage. In the storage array, designated storage is often not immediately employed for storing active data. In the enumerated storage categories, storage is apportioned from the previous category level in a progressive manner. Storage utilization reports which simultaneously display a plurality of storage category levels, and compute designated and free storage capacity at each level, indicate underutilized or potential available storage which may be obscured by imprecise or overly generous storage allotment estimates. The storage categories therefore identify a manner in which the available storage area is allotted, or apportioned.

System and methods for reporting storage utilization

Historical access information identifies which resources in a storage area network access portions of shared storage in the storage area network. Based on an analysis of the historical access information, a management report generator application analyzing such information can infer the existence of a cluster of resources that access a common portion of shared storage. When the management report generator identifies that two or more resources access the same storage resources during approximately the same timeframe, the management report generator knows that there is a high likelihood that the two or more resources are part of a cluster sharing access to common storage resources. Thus, when reporting a tally of storage resources used in the storage area network, it is possible for the management report generator to take into account the cluster and indicate that two resources share access to a common portion of storage.

Methods and apparatus for detecting use of common resources

Mechanisms and techniques are disclosed that maintain managed object data associated with components in a network such as a storage area network by receiving management data associated with components operating in a storage area network and matching the management data to existing objects in a managed object database. For each existing managed object that matches the management data, the system updates object data within the existing managed objects with the matching management data. For management data not matching the existing managed objects in the managed object database, the system creates at least one new managed object containing the non-matching management data. The system further analyzes the management data to infer an existence of additional components operating in the storage area network that do not have corresponding managed objects in the managed object database and creates new managed objects in the managed object database for components within the storage area network whose existence is inferred.

Methods and apparatus for maintaining object data for components in a network

Database performance assessment typically entails logging and capturing utilities which tend to gather large quantities of unwieldy data leading to a cumbersome and indefinite analysis. A system and method for database performance analysis includes periodic sampling of pending database requests, rather than exhaustive monitoring and capturing all database access traffic, to identify areas of contention. A database access queue is periodically sampled, or scanned, to gather a snapshot of pending requests of database transactions. Pending requests are aggregated by an aggregating process which aggregates, the samples with previous samples corresponding to the same transaction. Correlating the aggregated samples identifies transactions which have been pending the longest and identifies tables and segments in the database which have a relatively high number of pending transactions. By periodically sampling, rather than exhaustively logging all requests, CPU intrusiveness is minimized, trivial, benign transactions are eliminated from the output, and the most burdened tables and segments are identified to enable a database administrator to make informed decisions about remedial actions.

Growing community of inventors

Upton, MA, United States of America

Serge Marokhovsky