Certain embodiments disclosed herein provide application-specific launch optimization. Aspects of the present disclosure include one or more cost functions for each application, where each cost function corresponds to a likelihood that a particular application should be placed into a particular pre-activation state. For each of the inactive applications, a respective one of the pre-activation states is selected based on comparing cost values obtained by evaluating the cost functions. Each of the inactive applications can be moved to or maintained in the respectively-selected pre-activation state to more efficiently provide an expedited application launch experience for a user.

Examples described herein relate to a method and a system, for example, a workload controller, for accessing purged workloads. An alert indicative of an attempt to access a purged workload of workloads deployed in a workload environment may be received by the workload controller. The purged workload may include one or both of a deactivated workload or an archived workload. The workload controller may detect the attempt to access the purged workload based on port mirrored data traffic. Further, in some examples, the workload controller may activate the purged workload based on the alert.

A method for distributed neural network processing, the method may include detecting, by a local neural network that belongs to a local device, and based on sensed information, an occurrence of a triggering event for executing or completing a classification or detection process; sending to a remote device, a request for executing or completing the classification or detection process by a remote device that comprises a remote neural network; wherein the remote neural network has more computational resources than the local neural network; determining by the remote device whether to accept the request; and executing or completing, by the remote device, the classification or detection process when determining to accept the request; wherein the executing or completing involves utilizing the remote neural network.

A method is provided that includes: identifying a plurality of processing modules configured to provide a service, dependency information among the plurality of processing modules, and a plurality of performance parameters corresponding to the plurality of processing modules, respectively, wherein the plurality of processing modules are implemented as computer program instructions stored in one or more computer-readable storage mediums and are configured to be executed by one or more processors; determining thread configuration information based on the plurality of performance parameters, wherein the thread configuration information includes a plurality of thread numbers corresponding to the plurality of processing modules, respectively, wherein each thread number of the plurality of thread numbers is a number of threads included in a thread pool configured to execute the corresponding processing module; and packaging the plurality of processing modules, the dependency information, and the thread configuration information to generate an image for providing the service.

Aspects of the disclosure include an electronic device, comprising a processor. The processor is to receive a request to allocate computing resources, the request indicating a content type to use with the computing resources, determine available computing resources, determine a scoring of the computing resources according to the content type, and allocate a portion of the available computing resources according to the request, the content type, and the scoring.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for executing efficient and reliable techniques for multi-tenant software testing an using available agent allocation scheme that comprises one or more agent-tenant allocation recommendations, where each agent-tenant allocation recommendation associates an automated testing execution agent in an available agent subset with a test automation tenant in a throttled tenant subset.

This application provides an application program management method and apparatus. The method is performed in a database cluster system including at least two database nodes, at least one database object is stored in each database node, and the method includes: running an application program on a first database node in a first time period; determining a target database node based on at least one historical database object accessed by the application program in the first time period, where the target database node stores the historical database object; and running the application program on the target database node in a second time period. According to this application, a database node on which an application program runs can be dynamically adjusted, to avoid overload of the database node.

Systems and methods for scheduling usage time for programs that can be executed on a hybrid computing system including a quantum processing unit (QPU) and a central processing unit (CPU). Programs can comprise both QPU-executable tasks and CPU-executable tasks. Some programs can be considered high performance programs that are intolerant of interruptions to QPU-executable tasks and some programs can be considered low performance programs that are tolerant of interruptions to QPU-executable tasks. After a high performance program finishes executing QPU-executable tasks on a QPU, a low performance program may execute QPU-executable tasks on the QPU while the high performance program executes CPU-executable tasks on a CPU. Execution of QPU-executable tasks of a low performance program on a QPU can pause or stop if a high performance program is queued.

The present disclosure describes methods, devices, and storage mediums for adjusting computing resource. The method includes obtaining an expected pooling time of a target pooling layer and a to-be-processed data volume of the target pooling layer; obtaining a current clock frequency corresponding to at least one computing resource unit used for pooling; determining a target clock frequency according to the expected pooling time of the target pooling layer and the to-be-processed data volume of the target pooling layer; and in response to that the convolution layer associated with the target pooling layer completes convolution and the current clock frequency is different from the target clock frequency, switching the current clock frequency of the at least one computing resource unit to the target clock frequency, and performing pooling in the target pooling layer based on the at least one computing resource unit having the target clock frequency.

Implementations described herein relate to methods, systems, and computer-readable media to provide an alert based on a release of a software application implemented in a distributed computing system. In some implementations, the method includes receiving, at a processor, an indication of the release of the software application, obtaining a first set of metric values for each metric of a list of metrics for a first time period preceding a time of release of the release, obtaining a second set of metric values for each metric of the list of metrics for a second time period following the time of release, comparing the first set of metric values to the second set of metric values to determine a deviation score, generating an alert based on the deviation score, and transmitting the alert via one of a user interface and a communication channel.