A file system and a related method are presented. The file system includes an object storage configured to store file data for one or more files and a plurality of namespace entries corresponding to file data and/or metadata of the one or more files as one or more objects. Each namespace entry of the plurality of namespace entries includes an operation type conducted on the file data and/or metadata captured in a particular snapshot and a version number corresponding to the particular snapshot. The file system further includes an indexing system configured to generate the plurality of namespace entries; store the plurality of namespace entries as one or more objects in the object storage; and identify, in response to a search query, one or more files for retrieval from the object storage based on a list of the plurality of namespace entries sorted on the version numbers.

Automated methods and systems for compressing log messages stored in a log message databased are described herein. The automated methods and systems perform lossy compression of an original set of log messages by identifying log messages that represent each of the various types of events recorded in the original set. The log messages in the original set are overwritten by corresponding representative log messages. Source coding is used to construct a source coding scheme and variable length binary codewords for each of the representative log messages. The representative log messages are replaced by the codewords, which occupies significantly less storage space than the original set. The lossy compressed set of log messages can be decompressed to obtain the representative log messages using the source coding scheme.

A device configured to identify a file in a network device, to generate a first set of block hash codes for data blocks for a first instance of the file, and to generate a second set of block hash codes for data blocks for a second instance of the file. The device is further configured to determine the first set of block hash codes matches the second set of block hash codes and to generate an entry in a file list for the instances of the file. The device is further configured to count the number of entries that are associated with the file and to determine the number of entries is greater than the redundancy threshold value. The device is further configured to delete one or more instances of the file in response to determining that the number of entries is greater than the redundancy threshold value.

A predictive method for scheduling of the operations is described. The predictive method utilizes data generated from computing an expected lifetime of the individual files or objects within the container. The expected lifetime of individual files or objects can be generated based on machine learning techniques. Operations such as garbage collection are scheduled at an epoch where computational efficiencies are realized for performing the operation.

Systems, methods, and computer-readable media are provided for generating a unique ID for a sensor in a network. Once the sensor is installed on a component of the network, the sensor can send attributes of the sensor to a control server of the network. The attributes of the sensor can include at least one unique identifier of the sensor or the host component of the sensor. The control server can determine a hash value using a one-way hash function and a secret key, send the hash value to the sensor, and designate the hash value as a sensor ID of the sensor. In response to receiving the sensor ID, the sensor can incorporate the sensor ID in subsequent communication messages. Other components of the network can verify the validity of the sensor using a hash of the at least one unique identifier of the sensor and the secret key.

A computer-implemented method, computer system, and computer program product for deleting a file from a storage medium. A file that is marked for automatic deletion is identified. A deletion time at which the file is to be deleted from the storage medium is identified. A visual indicator associated with the file is displayed. The visual indicator changes over time to provide an indication as to a nearness of the deletion time.

Techniques for using erasure coding across multiple regions to reduce the likelihood of losing objects in a cloud object storage platform are provided. In one set of embodiments, a computer system can upload each of a plurality of data objects to each of a plurality of regions of the cloud object storage platform. The computer system can further compute a parity object based on the plurality of data objects, where the parity object encodes parity information for the plurality of data objects. The computer system can then upload the parity object to another region of the cloud object storage platform different from the plurality of regions.

A graph tracks the lineage of customer data, including when it was originally extracted from a customer computing system, and any transformation results indicating transformations that were performed on the customer data. The graph is traversed to identify nodes in the graph that have expired based upon data removal policies. The customer data represented by the expired nodes in the graph is deleted and the graph is modified to delete the expired nodes. The modified graph is then stored in persistent memory until data removal is next triggered.

A processing apparatus includes a moving image file restoration unit configured to restore a moving image file and an additional information file restoration unit configured to restore an additional information file associated with the moving image file. The additional information file restoration unit starts restoration processing on the additional information file associated with the moving image file after the moving image file restoration unit starts restoration processing on the moving image file and before the restoration processing on the moving image file is completed. The moving image file restoration unit completes the restoration processing on the moving image file after the restoration processing on the additional information file is completed.

Provided are techniques for optimized content object storage service for large scale content. A content object file is created. An index entry for the content object file is created with a content object key and a content object location. The content object file is appended to an aggregated file on a storage node. In response to a request to retrieve the content object file from the aggregated file, the content object key is used to access the content object location that describes the storage node, a name of the aggregated file, an offset into the aggregated file, and a size of the content object file to retrieve the content object file.