International Business Machines Corporation

National Security Agency

The United States of America As Represented by the National Security Agency

The United States of America As Represented by National Security Agency

Elizabeth Suzanne Richards

Sandeep Madhav Uttamchandani

Frederick Douglis

Joel Leonard Wolf

David Allan Pease

John Davis Palmer

William H Tetzlaff

David Tao

Edward Gustav Chron

Philip Shi-lung Yu

Kirsten Weale Hildrum

Li Yin

Nikhil Bansal

Lisa Karen Fleischer

Kristal T Pollack

Joseph P McCloskey

Akshay Kumar Reddy Katta

Nagui Halim

John Michael Tracey

Kay Schwendimann Anderson

Linda Marie Duyanovich

A storage system includes a plurality of data vats, and a processor including an optimizing unit that optimizes a value of data stored in the storage system. The optimizing unit optimizes the value by computing and implementing an optimal decision for allocating new data to a first data vat of the plurality of data vats, moving existing data from at least a second data vat of the plurality of data vats to the first data vat, and deleting existing data from the first data vat, based on an amount of data in each of the plurality of data vats.

System and method for optimizing data in value-based storage system

Disclosed are system and method embodiments for determining the root-causes of a performance objective violation, such as an end-to-end service level objection (SLO) violation, in a large-scale system with multi-tiered applications. This determination is made using a hybrid of component-level snapshots of the state of the system during a period in which an abnormal event occurred (i.e., black box mapping) and of known events and their causes (i.e., white-box mapping). Specifically, in response to a query about a violation (e.g., why did the response time for application aincrease from rto r), a processor will access and correlate the black-box and white-box mappings to determine a short-list of probable causes for the violation.

Diagnostic system and method

A method (and system) of storing data in a value-based storage system, includes optimizing a value of data stored in the value-based storage system.

The embodiments of the invention provide methods, computer program products, etc. for complaint-based service level objectives. More specifically, a method of deducing undefined service level objectives receives complaints regarding behavior of a system. The complaints could include a severity parameter, an entity parameter, a nature-of-complaint parameter, a timestamp parameter, and/or an identification parameter. Next, system details representing a current state of the system are recorded for each of the complaints. The method then automatically analyzes a history of the system details and the complaints to produce a historical compilation of the system details. The analyzing can include weighing each of the system details by a severity parameter value.

Compliant-based service level objectives

A method, apparatus, and article of manufacture are provided to support dynamic assignment of data from a continuous stream of data to one or more storage devices in a storage network. The storage network is configured with one or more tiers in a hierarchy, with at least one storage device in each tier. Similarly, the storage network is in communication with both a storage manager and a data manager. The storage manager sorts the storage devices, maintains a demand function of each device, and calculates a burn rate for each storage device. The data manager is in communication with the storage manager and assigns data from the received stream of data to at least one of the storage devices.

Method for self optimizing value based data allocation across a multi-tier storage system

A system and method for optimizing a storage system to support short data object lifetimes and highly utilized storage space are provided. With the system and method, data objects are clustered based on when they are anticipated to be deleted. When an application stores data, the application provides an indicator of the expected lifetime of the data, which may be a retention value, a relative priority of the data object, or the like. Data objects having similar expected lifetimes are clustered together in common data structures so that clusters of objects may be deleted efficiently in a single operation. Expected lifetimes may be changed by applications automatically. The system automatically determines how to handle these changes in expected lifetime using one or more of copying the data object, reclassifying the container in which the data object is held, and ignoring the change in expected lifetime for a time to investigate further changes in expected lifetime of other data objects.

Optimizing a storage system to support short data lifetimes

The embodiments of the invention provide methods, computer program products, etc. for autonomic retention classes when retaining data within storage devices. More specifically, a method of determining whether to retain data within at least one storage device begins by storing data items in at least one storage device. Furthermore, the method maintains access statistics for each of the data items, an age of each of the data items, and an administrator-defined importance value of each of the data items. Following this, a retention value is calculated for each of the data items based on the access statistics for each of the data items, the age of each of the data items, and the administrator-defined importance value of each of the data items.

Autonomic retention classes

The embodiments of the invention provide a method, computer program product, etc. for risk-modulated proactive data migration for maximizing utility. More specifically, a method of planning data migration for maximizing utility of a storage infrastructure that is running and actively serving at least one application includes selecting a plurality of potential data items for migration and selecting a plurality of potential migration destinations to which the potential data items can be moved. Moreover, the method selects a plurality of potential migration speeds at which the potential data items can be moved and selects a plurality of potential migration times at which the potential data items can be moved to the potential data migration destinations. The selecting of the plurality of potential migration speeds selects a migration speed below a threshold speed, wherein the threshold speed defines a maximum system utility loss permitted.

Risk-modulated proactive data migration for maximizing utility in storage systems

Growing community of inventors

Columbia, MD, United States of America

Elizabeth Suzanne Richards