Abstract: Systems and methods are disclosed to improve disaster recovery by implementing a scalable low-loss disaster recovery for a data store. The disaster recovery system enables disaster recovery for a linearizable (e.g., externally consistent) distributed data store. The disaster recovery system also provides for a small lag on the backup site relative to the primary site, thereby reducing the data loss by providing a smaller data loss window compared to traditional disaster recovery techniques. The disaster recovery system implements a timestamp for log records based on a globally synchronized clock. The disaster recovery system also implements a watermark service that updates a global watermark timestamp that a backup node uses to apply log records.

Abstract: Systems and methods are disclosed to improve disaster recovery by implementing a scalable low-loss disaster recovery for a data store. The disaster recovery system enables disaster recovery for a linearizable (e.g., externally consistent) distributed data store. The disaster recovery system also provides for a small lag on the backup site relative to the primary site, thereby reducing the data loss by providing a smaller data loss window compared to traditional disaster recovery techniques. The disaster recovery system implements a timestamp for log records based on a globally synchronized clock. The disaster recovery system also implements a watermark service that updates a global watermark timestamp that a backup node uses to apply log records.

Abstract: User-level privacy preservation is implemented within federated machine learning. An aggregation server may distribute a machine learning model to multiple users each including respective private datasets. Individual users may train the model using the local, private dataset to generate one or more parameter updates. Prior to sending the generated parameter updates to the aggregation server for incorporation into the machine learning model, a user may modify the parameter updates by applying respective noise values to individual ones of the parameter updates to ensure differential privacy for the dataset private to the user. The aggregation server may then receive the respective modified parameter updates from the multiple users and aggregate the updates into a single set of parameter updates to update the machine learning model. The federated machine learning may further include iteratively performing said sending, training, modifying, receiving, aggregating and updating steps.

Abstract: Systems and methods are disclosed to improve disaster recovery by implementing a scalable low-loss disaster recovery for a data store. The disaster recovery system enables disaster recovery for a linearizable (e.g., externally consistent) distributed data store. The disaster recovery system also provides for a small lag on the backup site relative to the primary site, thereby reducing the data loss by providing a smaller data loss window compared to traditional disaster recovery techniques. The disaster recovery system implements a timestamp for log records based on a globally synchronized clock. The disaster recovery system also implements a watermark service that updates a global watermark timestamp that a backup node uses to apply log records.

Abstract: In a transactional memory technique, hardware serves simply to optimize the performance of transactions that are controlled fundamentally by software. The hardware support reduces the overhead of common TM tasks—conflict detection, validation, and data isolation—for common-case bounded transactions. Software control preserves policy flexibility and supports transactions unbounded in space and in time. The hardware includes 1) an alert-on-update mechanism for fast software-controlled conflict detection; and 2) programmable data isolation, allowing potentially conflicting readers and writers to proceed concurrently under software control.

Abstract: In a transactional memory technique, hardware serves simply to optimize the performance of transactions that are controlled fundamentally by software. The hardware support reduces the overhead of common TM tasks—conflict detection, validation, and data isolation—for common-case bounded transactions. Software control preserves policy flexibility and supports transactions unbounded in space and in time. The hardware includes 1) an alert-on-update mechanism for fast software-controlled conflict detection; and 2) programmable data isolation, allowing potentially conflicting readers and writers to proceed concurrently under software control.

Abstract: A system for implementing synchronized objects for software transactional memory comprises one or more processors and a memory comprising program instructions executable by the processor to implement a transactional-memory manager configured to coordinate memory access requests directed at the memory from a plurality of transactions. The transactional-memory manager records, within a collaborator record for a shared data object in the memory, identifications of a set of two or more transactions that have requested synchronization on the object. In response to a commit request from a given transaction of the set, the transactional-memory manager determines whether to commit or abort the given transaction based at least in part on the transactional states of other transactions in the set, examining the collaborator record to identify the other transactions.