Abstract: The present technology improves the operation and endurance of storage drives by adapting the amount of over-provisioning for a drive to the write profile for the particular service assigned to the drive. The amount of over-provisioning for the drive is determined based on the write profile of the service and the attributes of the drive, such as the drive's specifications and the workload history of the drive. The write profile of the service can be predicted using a model that is empirical or is based on historical data. For example, the write profile can be predicted based on the similarity of the service to services in the historical data. The actual write profile of the service can be monitored, and if it deviates from the predicted write profile the amount of over-provisioning can be dynamically adjusted based on the actual write profile.

Abstract: Systems or methods are disclosed for dynamically allocating workloads to the most suitable resources within a data center, considering the transient nature of both workload requirements and available resources. The dynamic allocation of workloads may be achieved by testing servers with synthetic workloads and deploying full workloads to the servers or the servers most similar to those that handled the test well. This approach yields more efficient deployment than simply assigning workloads to the first available server.

Abstract: A system may obtain performance signals associated with at least one of a plurality of servers used in connection with performing a task. A system may provide the performance signals to a machine learning model. A system may receive as output from the machine learning model a health metric related to the at least one of the plurality of servers. A system may determine whether the health metric meets a migration condition. A system may, responsive to the health metric meeting the migration condition, initiate a reallocation of resources for performing the task, wherein the reallocation of resources includes migration of responsibility for performing the task to at least one other server of the plurality of servers.

Abstract: This disclosure describes systems that identify one or more models (e.g., large language models and/or virtual assistants) permitted to access content items stored for user accounts within a content management system. The disclosed systems can determine a model available to a user account within the content management system from among the one or more models. For example, the disclosed systems can determine one or more relationships between the user accounts within the content management system, large language models utilized by the user accounts, virtual assistants utilized by the user accounts, and content items accessed by the user accounts. The disclosed systems can determine the model for the user account according to the one or more relationships. The disclosed systems can provide a notification corresponding to the model via a user interface of a client device associated with the user account.

Abstract: This disclosure describes systems that identify one or more models (e.g., large language models and/or virtual assistants) permitted to access content items stored for user accounts within a content management system. The disclosed systems can determine a model available to a user account within the content management system from among the one or more models. For example, the disclosed systems can determine one or more relationships between the user accounts within the content management system, large language models utilized by the user accounts, virtual assistants utilized by the user accounts, and content items accessed by the user accounts. The disclosed systems can determine the model for the user account according to the one or more relationships. The disclosed systems can provide a notification corresponding to the model via a user interface of a client device associated with the user account.

Abstract: The present technology enhances workload management of data storage systems by using an internal copy function and/or a zone-append technology. The internal copy function is used, e.g., in merge operations to move data between locations on a disk without using off-disk resources (e.g., processing or memory of a CPU). Zone-append technology uses nameless writes (e.g., write instruction without an assigned destination address on the disk) to combine IO units from different threads to be written to a common zone of a disk (e.g., a shingled magnetic recording (SMR) disk). Sequential addresses are assigned to IO units from different threads based on their order in the write queue, reducing the latency and seek time typically associated with random writes. The zone-append technology, e.g., uses sequential write operations within specified zones, allowing the disk to determine the actual write location and to report post-write logical block addresses (LBAs).

Abstract: The present technology pertains to a predictive thermal model that can be used to intelligently manage thermal events in a data center. The predictive thermal model can be used to predict future temperatures of servers to take action before the server experiences higher than desired temperatures. The present technology also includes several innovative amelioration techniques that can help to keep servers cool when it is predicted that heat in their environment is about to increase. One such amelioration technique is a heat-responsive operation change for storage servers, or at least individual hosts within a storage server. For example, a host can be switched into a mode where it can batch read and write operations to limit the amount of seeking the host needs to perform, which produces less heat.

Abstract: A system may obtain performance signals associated with at least one of a plurality of servers used in connection with performing a task. A system may provide the performance signals to a machine learning model. A system may receive as output from the machine learning model a health metric related to the at least one of the plurality of servers. A system may determine whether the health metric meets a migration condition. A system may, responsive to the health metric meeting the migration condition, initiate a reallocation of resources for performing the task, wherein the reallocation of resources includes migration of responsibility for performing the task to at least one other server of the plurality of servers.

Abstract: This disclosure describes systems that identify one or more models (e.g., large language models and/or virtual assistants) permitted to access content items stored for user accounts within a content management system. The disclosed systems can determine a model available to a user account within the content management system from among the one or more models. For example, the disclosed systems can determine one or more relationships between the user accounts within the content management system, large language models utilized by the user accounts, virtual assistants utilized by the user accounts, and content items accessed by the user accounts. The disclosed systems can determine the model for the user account according to the one or more relationships. The disclosed systems can provide a notification corresponding to the model via a user interface of a client device associated with the user account.

Abstract: A system may obtain performance signals associated with at least one of a plurality of servers used in connection with performing a task. A system may provide the performance signals to a machine learning model. A system may receive as output from the machine learning model a health metric related to the at least one of the plurality of servers. A system may determine whether the health metric meets a migration condition. A system may, responsive to the health metric meeting the migration condition, initiate a reallocation of resources for performing the task, wherein the reallocation of resources includes migration of responsibility for performing the task to at least one other server of the plurality of servers.

Abstract: The system obtains performance signals associated with respective hard disks of a volume of hard disks including a plurality of hard disks that are dedicated to activities of a service. The system determines a volume failure prediction for the volume of hard disks by, for each respective hard disk of the volume of hard disks, determining a hard disk failure prediction. The system determines a hard disk failure prediction by: inputting the respective performance signals into a supervised machine learning model; and receiving as output from the machine learning model the hard disk failure prediction for the respective hard disk. The system based on the received outputs, determines that the volume failure prediction is associated with a migration condition. The system, responsive to determining that the volume failure prediction is associated with the migration condition, migrates data from the volume of hard disks to a second volume of hard disks.