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 provide a method, an electronic device, and a computer program product for storage performance expansion. The method includes acquiring, from a client, backup settings of a user for backing up data in a storage system and the user's priority. The method further includes determining, based on the user's priority, an input/output (I/O) specification of the storage system that is able to be allocated to the client. The method further includes determining, based on the backup settings, a time period in which a peak value of an I/O load of the storage system occurs. The method further includes increasing I/O performance of the storage system in response to that the peak value is greater than an upper limit of the I/O specification. The embodiments of the present disclosure can solve the I/O peak problem more effectively without interrupting a data backup or restoration service.

Abstract: Targeted recommendations may be provided to specific user segments. The users may be segmented on or more user parameters that facilitate targeted provision of recommendations to the individual segments of users. A recommendation may prompt a user to take a recommended action in the game.

Abstract: Upgrade of a virtual system is described. An example method includes recording access data of the virtual system that uses a cloud service, and in response to receiving an attribute set of a group of candidate cloud services, determining a predicted mode of the workload of the virtual system based on the access data. An upgrade preference is determined for the virtual system based on the access data and/or user settings of the virtual system. A cloud service for upgrading the virtual system is selected from the group of candidate cloud services based on the predicted mode and the upgrade preference, and a recommendation report is generated indicating the cloud service. Personalized cloud service recommendations can be created according to access habits of a user for the virtual system and other, user desired performance aspects so that upgrading the virtual system with the recommended cloud service improves user experience.

Abstract: The present disclosure relates to a method that includes acquiring a use status of a storage space of the virtual storage system. The method further includes determining whether to upgrade the virtual storage system based on the use status. The method further includes determining a targeted instant based on the workload of the virtual storage system in response to the determination to upgrade the virtual storage system. The method further includes upgrading the virtual storage system at the targeted instant. In this way, it can be accurately determined whether to upgrade the virtual storage system based on the storage status of the virtual storage system, and when upgrade is needed, the optimum upgrade time is selected for upgrade of the system based on the workload of the virtual storage system, thereby effectively ensuring normal use of the virtual storage system.

Abstract: The present disclosure relates to a method, a device, and a computer program product for upgrading a virtual storage system. The method for upgrading the virtual storage system is applied to a serverless platform, and includes receiving an upgrading instruction for the virtual storage system. The method further includes determining an upgrading type corresponding to the upgrading instruction and a preset execution file matched with the upgrading type, the preset execution file being pre-deployed in the serverless platform. The method further includes upgrading the virtual storage system by running the preset execution file in the serverless platform. In this way, a preset execution function is run in the serverless platform to upgrade the virtual storage system, which can save resource costs and operational costs for users. In addition, because the task processing efficiency of the serverless platform is high, the upgrading efficiency of the virtual storage system can further be improved.

Abstract: A start-up power supply is provided that includes a battery, a supercapacitor module, and a switch module coupling an output of the supercapacitor module to an output connected to terminals of a vehicle battery. A charging circuit is coupled between the battery and the supercapacitor module and also to the output. A control module is coupled to the charging circuit, the switching module, the battery, and the supercapacitor module. The control module configured to first charge the supercapacitor module until the voltage of the supercapacitor module is higher than a pre-charge voltage of the supercapacitor module. Then to connect the battery and the supercapacitor module in parallel enabling the voltage of the supercapacitor module to charge to a voltage of the battery. Finally, to charge the supercapacitor module until the voltage of the supercapacitor module reaches a voltage of a full threshold of the supercapacitor module.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a disk controller of a plurality of disks of a data storage system, collecting usage telemetry from the plurality of disks and initiating determination of one or more candidate time slots for performing a battery learn cycle using a machine learning model. The machine learning model is trained using the usage telemetry from the plurality of disks. The method also includes, by the disk controller, presenting one or more of the one or more candidate time slots for performing a battery learn cycle.

Abstract: Techniques for intelligently routing IO to a storage class memory (SCM) namespace are disclosed. A configuration for a namespace is determined, where the configuration indicates a type of IO that the namespace is structured to handle. Details about the configuration of the namespace are stored in a repository. A forwarding rule is generated based on the namespace's stored configuration. When incoming IO having attributes similar to that type is received, implementation of the forwarding rule causes the incoming IO to be directed to the namespace. Attributes of a particular incoming IO are determined. As a result of the attributes satisfying a similarity threshold relative to the type, the forwarding rule is implemented such that the particular incoming IO is directed to the namespace.

Abstract: The present application discloses a method for detecting cigarette appearance defects based on variational Bayesian inference. In a cigarette appearance defect detection scenario, in order to solve the problem that a current point estimation-based machine learning algorithm leads to an overconfidence decision in a data scarcity area, the present application proposes a variational inference-based Bayesian method to improve a SSD backbone network, preferably including calculating a real posterior probability by the variational inference method, replacing a point estimation mechanism of a convolution layer weight in the original backbone network by using a form of probability distribution, then detecting the improved extracted features by an SSD target detection algorithm, and furthermore, accurately determining whether the appearance of the cigarette is defective and the type and location of the defect.

Abstract: One example method includes accessing a dataset, selecting a list of attributes of the dataset, each of the attributes being selected based on a determination that the attribute is affecting growth of the dataset and affecting an amount of data storage space consumed by the dataset, assigning a SHAP score to each attribute, using the SHAP scores to assign respective weights to each attribute, deriving drift and shock information for the dataset, and based on the drift and shock information, calculating a risk score that a storage capacity of an asset where the dataset is stored will be exhausted within a particular time interval.

Abstract: A method includes: generating a machine learning model based on historical operation data of the storage system, the storage system being used for backing up and restoring data in a client. The method further includes: determining, by the machine learning model, input/output (I/O) utilization of the storage system for a future time period. The method further includes: determining, in response to determining that the I/O utilization exceeds a predetermined threshold at a first time instance in the future, an I/O capacity corresponding to the determined I/O utilization based on the determined I/O utilization. The method further includes: adjusting the performance of the storage system based on the determined I/O capacity. The embodiments of the present disclosure enable more effective resolution of the I/O spike problem without interrupting data backup or restoration services.

Abstract: One example method includes identifying a source of a performance issue in a virtualized environment. Telemetry data is collected relative to the flow of a request/response in the virtualized environment. The collected telemetry data can be compared to normal data. A probability can be generated for each layer to identify which of the layers is the most likely source of the performance issue. The layers can be prioritized based on their probability. The most likely layer or virtual machine is recommended for analysis to determine the cause of the performance issue.

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 this application provide an electronic device and an electronic device assembly. The electronic device includes at least a casing and an antenna assembly. At least one outer side wall of the casing is provided with a first groove that is configured to connect at least the electronic device and an external component, a first receiving cavity is further provided inside the casing, at least a part of a radiating element of the antenna assembly is located in the first receiving cavity, and a projection of the first receiving cavity in a thickness direction of the electronic device at least partially overlaps with a projection of the first groove in the thickness direction of the electronic device. In this way, a size of the electronic device can be reduced, so that the entire electronic device is small. This can improve wearing experience of a user.

Abstract: Targeted recommendations may be provided to specific user segments. The users may be segmented on or more user parameters that facilitate targeted provision of recommendations to the individual segments of users. A recommendation may prompt a user to take a recommended action in the game.

Abstract: The present disclosure relates to a method, a device, and a program product for managing multiple computing tasks on a batch basis. A method includes: identifying a task type of the multiple computing tasks in response to receiving a request to use a computing unit in a computing system to perform the multiple computing tasks; acquiring a scheduling time overhead incurred for scheduling the multiple computing tasks for execution by the computing unit; determining, based on the task type and the scheduling time overhead, a batch size for dividing the multiple computing tasks; and dividing the multiple computing tasks into at least one batch based on the batch size. A corresponding device and a corresponding computer program product are provided. With the example implementations of the present disclosure, the batch size for dividing multiple computing tasks can be dynamically determined, so that the performance of the computing system can meet user demands.

Abstract: A multi-target kinase inhibitor is shown in formula (I), in which R is selected from formula (a), formula (b), formula (c), formula (d), formula (e) and formula (f). The multi-target kinase inhibitor can effectively inhibit the enzymatic activities of RET, VEGFR3 and PDGFRA, and can effectively treat diseases that are regulated and controlled by multi-target kinases and are related to abnormal signal transduction pathways of the multi-target kinases, including cancers of breast, respiratory tract, brain, reproductive organ, digestive tract, urinary tract, eye, liver, skin, head and/or neck and distant metastatic cancers thereof, and lymphoma, sarcoma, leukemia and the like. The active ingredients of the pharmaceutical composition of the present invention comprise a multi-target kinase inhibitor, which accounts for 1-50 wt % of the composition.