Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for managing a storage system. The method includes: based on respective task types of a plurality of tasks to be executed, allocating the plurality of tasks to a plurality of accelerator resources in a storage system for processing; at least for a first accelerator resource in the plurality of accelerator resources, determining a first polling interval based on an average task size of a first group of tasks allocated to the first accelerator resource; and scheduling the execution of the first group of tasks at the first accelerator resource at the first polling interval. The embodiments of the present disclosure can optimize the scheduling of the tasks to be executed on the plurality of accelerator resources, thereby optimizing system performance.

Abstract: The present disclosure relates to a method, a device, and a program product for managing a computing resource in a storage system. In one method, a processing request for processing a task using a computing resource is received. A length of time required for processing the task is acquired based on a usage state of the computing resource. A workload of the computing resource for processing a future data access request for the storage system within a future time period is determined based on a load model of the computing resource and a current workload of the computing resource. The load model describes an association relationship between a previous load and a subsequent load of the computing resource for processing a historical data access request for the storage system. A target time period matching the length of time is selected from the future time period based on the workload for processing the task. A corresponding device and a corresponding computer program product are provided.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for latency processing. The method includes: in response to that a data persistence operation occurs in a system, acquiring a record for the operation, wherein the record includes an actual latency of the operation and a group of measurements of a group of states of the system within a predetermined time period of the occurrence of the operation. The method further includes: in response to that the actual latency of the operation is greater than a first threshold, generating an estimated latency of the operation by using a trained predictor on the basis of the group of measurements. The method further includes: determining a difference between the actual latency and the estimated latency, and in response to that the difference is less than a second threshold, identifying one or more states from the group of states on the basis of the record and the estimated latency.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for failure handling. This failure handling method includes determining a sector set failure type associated with at least one failed sector set of a disk; if the sector set failure type indicates that the number of failed sector sets in the at least one failed sector set is greater than a first threshold number, generating an instruction for replacing the disk; and otherwise performing at least one of the following: migrating data from a failed sector set in which the number of failed sectors is greater than a second threshold number to a spare sector set, and performing a failure recovery for a failed sector set in which the number of failed sectors is less than or equal to the second threshold number.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for processing a system latency. The method includes obtaining a group of records in a system for a group of data persistence operations of a particular type, and estimating a group of estimated latencies in the group of data persistence operations based on the group of records. Each record in the group of records includes a group of metrics for a group of states of the system within a predetermined period when each data persistence operation occurs. The method also includes determining corresponding contributions of each state in the group of states to latencies of the group of data persistence operations based on the group of records and the group of estimated latencies, and determining one or more states from the group of states based on the corresponding contributions.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for latency processing. The method includes: in response to that a data persistence operation occurs in a system, acquiring a record for the operation, wherein the record includes an actual latency of the operation and a group of measurements of a group of states of the system within a predetermined time period of the occurrence of the operation. The method further includes: in response to that the actual latency of the operation is greater than a first threshold, generating an estimated latency of the operation by using a trained predictor on the basis of the group of measurements. The method further includes: determining a difference between the actual latency and the estimated latency, and in response to that the difference is less than a second threshold, identifying one or more states from the group of states on the basis of the record and the estimated latency.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for failure handling. This failure handling method includes determining a sector set failure type associated with at least one failed sector set of a disk; if the sector set failure type indicates that the number of failed sector sets in the at least one failed sector set is greater than a first threshold number, generating an instruction for replacing the disk; and otherwise performing at least one of the following: migrating data from a failed sector set in which the number of failed sectors is greater than a second threshold number to a spare sector set, and performing a failure recovery for a failed sector set in which the number of failed sectors is less than or equal to the second threshold number.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for managing a storage system. The method includes: based on respective task types of a plurality of tasks to be executed, allocating the plurality of tasks to a plurality of accelerator resources in a storage system for processing; at least for a first accelerator resource in the plurality of accelerator resources, determining a first polling interval based on an average task size of a first group of tasks allocated to the first accelerator resource; and scheduling the execution of the first group of tasks at the first accelerator resource at the first polling interval. The embodiments of the present disclosure can optimize the scheduling of the tasks to be executed on the plurality of accelerator resources, thereby optimizing system performance.

Abstract: Embodiments of the present disclosure relate to a model training method, a failure determining method, an electronic device, and a computer program product. The model training method includes: acquiring a plurality of disk failure data sets collected in a first time period; acquiring another disk failure data set that is collected at a predetermined time point after the first time period and indicates failure information of at least one failed sector set; and training a failure determining model based on the plurality of disk failure data sets and the failure information, so that a probability of matching of predicted failure information at a predetermined time point determined by the trained failure determining model based on the plurality of disk failure data sets and the failure information is greater than a first threshold probability.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for training a model. The method includes: acquiring a test set and a training set for training models, the test set and the training set each including workload data associated with normal storage devices and workload data associated with exceptional storage devices; training a device detection model using the training set, the device detection model being used to classify storage devices as normal storage devices or exceptional storage devices according to a threshold degree, with the threshold degree being within a range; determining a test result by applying the test set to the device detection model; and updating the range of the threshold degree if it is determined that the test result indicates that the performance of the device detection model does not reach a threshold performance. With this method, storage devices can be accurately detected by the trained model.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for managing a storage system. The method includes: if it is determined based on current values of a plurality of attribute parameters of a storage device in a storage system that the storage device will have a fault, determining whether the fault of the storage device will not occur again based on reference values of attribute parameters in a first set in the plurality of attribute parameters and current values of attribute parameters in a second set in the plurality of attribute parameters. The method further includes: if it is determined that the fault of the storage device will not occur again, determining a cause of the fault based on the attribute parameters in the first set. The embodiments of the present disclosure can analyze a cause of a fault of a storage device, thereby avoiding the misjudgment of a storage device having no internal fault as a storage device having an internal fault.

Abstract: Embodiments of the present disclosure provide a computer-implemented method, an electronic device and a computer program product. The method comprises: obtaining historical data of recoverable errors that occurred in a storage disk during a first period in the past. The method also comprises: determining, based on the historical data, a predicted number of recoverable errors to occur in the storage disk during a second period. The first period has a same duration as that of the second period. The method further comprises: in response to the predicted number exceeding a threshold for identifying a slow disk, performing an operation for handling a slow disk on the storage disk.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for managing a storage system. The method includes: if it is determined based on current values of a plurality of attribute parameters of a storage device in a storage system that the storage device will have a fault, determining whether the fault of the storage device will not occur again based on reference values of attribute parameters in a first set in the plurality of attribute parameters and current values of attribute parameters in a second set in the plurality of attribute parameters. The method further includes: if it is determined that the fault of the storage device will not occur again, determining a cause of the fault based on the attribute parameters in the first set. The embodiments of the present disclosure can analyze a cause of a fault of a storage device, thereby avoiding the misjudgment of a storage device having no internal fault as a storage device having an internal fault.

Abstract: The present disclosure relates to a method, a device, and a program product for managing a computing resource in a storage system. In one method, a processing request for processing a task using a computing resource is received. A length of time required for processing the task is acquired based on a usage state of the computing resource. A workload of the computing resource for processing a future data access request for the storage system within a future time period is determined based on a load model of the computing resource and a current workload of the computing resource. The load model describes an association relationship between a previous load and a subsequent load of the computing resource for processing a historical data access request for the storage system. A target time period matching the length of time is selected from the future time period based on the workload for processing the task. A corresponding device and a corresponding computer program product are provided.

Abstract: Embodiments of the present disclosure provide a computer-implemented method, an electronic device and a computer program product. The method comprises: obtaining historical data of recoverable errors that occurred in a storage disk during a first period in the past. The method also comprises: determining, based on the historical data, a predicted number of recoverable errors to occur in the storage disk during a second period. The first period has a same duration as that of the second period. The method further comprises: in response to the predicted number exceeding a threshold for identifying a slow disk, performing an operation for handling a slow disk on the storage disk.