Western Digital Technologies, Inc.

Arne Vansteenkiste

Wim Michel Marcel De Wispelaere

Pieter Audenaert

Wim De Wispelaere

Stijn Devriendt

Bruno Keymolen

Dieter Fiems

Willem Mélange

Apoorv Saxena

Sander De Dycker

Joris Walraevens

The disclosed technology includes an example system that has a request throttling manager that is configured to receive a first file data request, queue the first file data request in a first request queue, and process the first file data request based on the first token bucket. The first token bucket includes a sufficient first quantity of first tokens to process the first file data request. The system further includes a storage manager configured to access one or more storage nodes of a plurality of storage nodes of a distributed storage system in response to the first file data request.

Request throttling in distributed storage systems

Example distributed storage systems, file system interfaces, and methods provide cache coherence management. A system receives a file data request including a file data reference and identifies a data cache location with a coherence value for the file data reference. The system queries a reference data store for a coherence reference corresponding to the file data reference and compares the coherence value to the coherence reference. In response to the coherence value matching the coherence reference, the system executes the file data request using the data cache location.

Cache coherence for file system interfaces

Multiple objects in a multi-tiered storage system are classified according to multiple metadata fields. Access histories of the classified objects are tracked. Tracked access histories for objects having given values for given metadata fields are amalgamated. Access probability distributions are determined for objects having given values for given metadata fields, based on the amalgamated access histories. Responsive to a new object being added to the system, the new object is classified according to multiple metadata fields. For each specific one of the multiple metadata fields according to which the new object is classified, the determined access probability distribution for objects having the same metadata classification value as the new object is retrieved. The predicted access probability distribution for the new object is determined, by combining the retrieved access probability distributions. The new object is assigned to and stored in a specific storage tier based on the predicted access probability distribution.

Real-time, self-learning automated object classification and storage tier assignment

Requests to access specific ones of a plurality of stored objects are processed by multiple access nodes. A separate access history is maintained for each access node. Each access history identifies stored objects most recently accessed through the specific node. A separate predicted access future is maintained for each stored object. A predicted access future associated with a specific stored object can be in the form of a listing of stored objects statistically predicted to be those most likely to be accessed within a given temporal proximity after the specific stored object is accessed. Each predicted access future is determined based on inversion of maintained access histories. Responsive to receiving an access request for a specific stored object, the predicted future associated with the requested object is read, a specific number of additional stored objects identified in the associated predicted future is pre-fetched from slower to faster storage.

Predicting future access requests by inverting historic access requests in an object storage system

Growing community of inventors

Gentbrugge, Belgium

Arne Vansteenkiste