Amazon Technologies, Inc.

Anand Chakraborty

Bhavesh Anil Doshi

Miles Childs Kaufmann

Arvinth Ravi

Phillip H Pruett, Iv

Nikhil Menon

Suresh Kumar Golconda

Shikha Sukumaran

Leon Thrane

Thayn Moore

Michael Christopher Wenneman

An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Smart data storage tiers for data object transitioning

An object-based data storage service receives a request to store a data object in a first location corresponding to a first data storage tier. The request may specify a parameter to enable transitioning of the data object to another data storage tier. In response to the request, the object-based data storage service stores the data object in the first location and monitors access of the data object to determine usage data associated with the data object. The object-based data storage service processes the usage data to determine that the data object is to be transitioned to a second data storage tier. As a result of this determination, the object-based data storage service transitions the data object to a second location corresponding to the second data storage tier.

Automated tier-based transitioning for data objects

Technologies are described for analyzing interactions with data objects stored by a network-based storage service. The analysis of the interactions can identify patterns of the data object interactions and outcomes that can result from the patterns. Models can be developed that include the patterns and the outcomes corresponding to the patterns. As requests related to data object interactions are subsequently obtained by the system, the requests can be analyzed with respect to the models to identify an outcome that may be associated with the requests.

Analysis of interactions with data objects stored by a network-based storage service

Smart data storage hers for data object transitioning

A stream of storage requests can be received for data objects stored by a storage service. A streaming algorithm can be utilized to identify the most frequently accessed objects stored by the storage service. A statistical distribution of the most frequently accessed objects can then be generated and utilized to estimate the number of infrequently accessed objects. Machine learning can also be utilized to identify correlations between attributes of objects stored by the storage service and their associated access rates. For instance, machine learning can be utilized to determine that objects stored in a certain location or having other characteristics typically have low access rates. Information regarding the number of infrequently accessed objects and their learned attributes can be utilized to take action with regard to the infrequently accessed objects, such as moving the infrequently accessed objects to long-term storage.

Network service for identifying infrequently accessed data in a data stream

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Redmond, WA, United States of America

Anand Chakraborty