Cloud storage provides accessible interfaces, near-instant elasticity, scalability, multi-tenancy, and metered resources in a distributed framework providing fault tolerant solutions with high data durability. Stored data may have legal or compliance requirements defining retention periods ensuring the data is preserved without modification for a period of time. However, data privacy rules such as the European Union's General Data Protection Regulation can require modification or destruction of records at any point. Further, many retention structures are user driven but users make mistakes requiring a change to the record's associated retention period. Retention period mechanism enforced with immutable storage can satisfy compliance requirements but run contrary to data privacy rules as well as blocking adjustments. Accordingly, processes, methods and systems are required allowing retention policy application to data being stored within network based storage as well as allowing retention policies to be applied to stored data thereby facilitating retention period adjustments.

An information processing apparatus includes at least one processor and stores an object to be stored in any of a plurality of storage nodes in accordance with a distribution rule set in advance. The processor is configured to, in a case where the object to be stored is a new object that is newly created, distribute the new object in accordance with a distribution rule set at a point in time at which a storage request of the new object is made. The processor is configured to, in a case where the object to be stored is an update object that is an update of an existing object stored in any of the plurality of storage nodes, distribute the update object to the same storage node as a storage node in which the corresponding existing object is stored, in accordance with a distribution rule applied to the corresponding existing object.

The described technology is generally directed towards managing data retention policy for stream data stored in a streaming storage system. When a request to truncate a data stream from a certain position (e.g., from a request-specified stream cut) is received, an evaluation is made to determine whether the requested position is within a data retention period as specified by data retention policy. If any data prior to the stream cut position (corresponding to a stream cut time) is within the data retention period, the truncation request is blocked. Otherwise truncation from the stream cut point is allowed to proceed/is performed. Also described is handling automated (e.g., sized based) stream truncation requests with respect to data retention.

Provided are a computer program product, system, and method for dynamic determination of retention periods for digital content. Metadata is generated for instances of digital content including an access pattern of the digital content by a user of the computing device, attributes of the digital content, and a retention period during which the digital content stored is retained in the storage. A machine learning module is trained with input comprising the metadata for instances of the digital content to produce the retention period of the digital content. Input, comprising metadata determined from digital content, received after training the machine learning module, is provided to the machine learning module to produce an output retention period for the digital content received after the training. The output retention period is used to determine when to delete the digital received after the training content from the storage.

The disclosed technology relates to a system configured to receive operations data from a content management system, wherein the operations data comprises a log of operations, execute the log of operations, and update, based on execution of the log of operations, a remote tree representing a server state for content items stored on the content management system.

A system and method are disclosed associated with at least one physical data store instance adapted to contain electronic records; and a shared cluster platform, coupled to the data store, including: a computer processor, and a computer memory, coupled to the computer processor, storing instructions that, when executed by the computer processor cause the shared cluster platform to: receive an eviction policy for a first tenant for a data store; map a first container for the first tenant to a physical data store instance; generate a unique first key element for the first tenant, wherein the eviction policy for the first tenant is linked to the unique first key element; and transmit a first endpoint of the first container as a proxy for the selected physical data store instance Numerous other embodiments are provided.

A method for utilizing shared memory spaces for data lifecycle management in a conversational AI system includes receiving one or more storage requests directing conversational data to multiple nodes of a conversational data object and storing the conversational data that is directed to each different one of the multiple nodes within a different one of multiple discrete memory spaces. Each of the multiple discrete memory spaces is governed by a scope policy defining a lifetime for data stored within the memory space. The method further provides for erasing data of each node of the multiple different nodes of the conversational data object according to the lifetime defined by the scope policy governing the memory space storing the node.

Disclosed is a method, a device, a system and/or a manufacture of testing software and/or computing hardware design through test fragmentation into one or more discrete computing environments. In one embodiment a method for efficient testing with a test fileset includes initiating a new instance of an operation filesystem and copying a software application, a script, a computer hardware design, and/or a circuit design to be tested into the operation filesystem to define a workspace data. The method defines a discrete environment that is a computing container and/or a virtual computer, the assigns the discrete environment a processing power and/or memory allocation from a computing resources pool and clones a workspace master to generate a workspace clone. The method then extracts a test script from the test fileset, executes the test script within the workspace clone, and returns the resources to the computing resources pool.

Example aspects include techniques for ordering operations in a file system having a hierarchical namespace including determining, by a first service that manages file system operations for a first entity in the hierarchical namespace, to perform a file system operation for the first entity, generating, by the first service, a first counter record for the file system operation, obtaining, based on determining to perform the file system operation and from a second service that manages file system operations on a second entity that is a parent of the entity in the hierarchical namespace, a second counter record for a last file system operation performed on the second entity, wherein the second counter record is generated by the second service, and storing, in a change feed, an indication of the file system operation along with the first counter record and the second counter record.

A data storage management system is enhanced to accommodate, and moreover to optimize, the storing and retention of deduplicated secondary copies at write-once read-many (WORM) enabled storage platforms. Enhancements include without limitation: user interface (UI) options to enable WORM functionality for secondary storage, whether used for deduplicated or non-deduplicated secondary copies; enhancements to secondary copy (e.g., deduplication copy, backup) operations; and pruning changes. The storage manager is generally responsible for managing the creation, tracking, and deletion of secondary copies, with and without deduplication. Media agents that store secondary copies to and prune them from the WORM-enabled storage platforms also are enhanced for communicating and interoperating with both bucket-level and object-level WORM-enabled storage platforms to implement the features disclosed herein.