Abstract: Automated reconfiguration of real time data stream processing may be implemented. A processing function that describes one or more operations to be performed with respect to one or more data streams may be executed at one or more processing nodes. Performance metrics describing the performance of the processing function at the processing nodes may be collected and monitored. A reconfiguration event may be detected for the processing function. A different execution configuration for the processing function may be determined and initiated in response to detecting the reconfiguration event.

Abstract: A method includes identifying, by a dispersed storage (DS) processing unit of a plurality of DS processing units of a dispersed storage network (DSN), a DSN operation to be performed that will adversely interfere with processing of DSN access requests. The method further includes sending, by the DS processing unit, a suspension request to a process balancing module of the DSN where the suspension request is for temporarily suspending processing of DSN access requests. The method further includes determining, by the process balancing module, whether sufficient processing resources of active DS processing units of the plurality of DS processing units are available for processing DSN access requests on behalf of the DS processing unit. When determined that sufficient processing resources are available, the method further includes granting the suspension request, and allocating DSN access requests of the DS processing unit to one ore of the active DS processing units.

Abstract: Workload scheduling allows geospatial dependencies between workload items, which may vary in size, grouping or composition from workflow step to workflow step and can automatically adjoin existing data. By allowing dependencies between workload items in different steps, processing (and finish processing) of a dependent workload item begins in a subsequent step once its required workload items in a previous step have finished, thus enabling the processing of that dependent workload item before the previous step has completely processed all of its workload items. A dependency may be created because an item in a subsequent step intersects spatially with an item from a previous step, or because within a current step items have a buffer, necessitating that one item may depend upon spatially neighboring items. Workloads in two dimensions, three dimensions and higher may be used, and may be grid based, general polygons, or other in shape.

Abstract: A method for hierarchical workload allocation in a storage system, the method may include determining to reallocate a compute workload of a current compute core of the storage system; wherein the current compute core is responsible for executing a workload allocation unit that comprises one or more first type shards; and reallocating the compute workload by (a) maintaining the responsibility of the current compute core for executing the workload allocation unit, and (b) reallocating at least one first type shard of the one or more first type shards to a new workload allocation unit that is allocated to a new compute core of new compute cores.

Abstract: Various embodiments of the present technology can include systems, methods, and non-transitory computer readable media configured to receive request information associated with a first request. The request information comprises user information associated with a first user. A first usage threshold associated with the first user is determined based on the user information. A determination is made that the first request has exceeded the first usage threshold. The first request is automatically terminated based on the determination that the first request has exceeded the first usage threshold.

Abstract: An application may be migrated from a first to a second computing system. Configuration parameter values associated with executing the migrated application on the second computing system may be determined by computational optimization based on configuration parameter values and/or monitored performance metrics associated with the application on the first computing system. Configuration parameter values associated with executing the migrated application on the second computing system may be determined by performing simulations of the migrated application configured for execution on the second computing system based on multiple sets of configuration parameter values, monitoring performance metrics associated with the simulations, and performing computational optimization based on the multiple sets of configuration parameter values and monitored performance metrics associated with the simulations.

Abstract: A function performance trigger for a cloud computing system is disclosed. A function is to be run in response to the trigger. A template for a function in the cloud computing system is generated. The trigger is defined for the function based upon a performance parameter of the cloud computing system.

Abstract: A system includes a memory and at least one processor in communication with the memory. A processor is configured to receive a first log message denoting an event associated with a first application executing in the system. A machine learning model generates a predicted log message based at least in part on the first log message. The predicted log message represents a prediction of a subsequent log message to be received from the first application. First metric data associated with the predicted log message is determined. The first metric data describes system conditions of the system associated with the predicted log message. A tuning profile associated with the system conditions is determined and the current system configuration of the system is modified using the tuning profile.

Abstract: Embodiments described herein are directed to configuring managed virtual machines. For instance, a management service (e.g., a mobile device manager) may provide configuration settings to a parent virtual machine. Upon successful application of the configuration settings, the parent virtual machine notifies a configuration service that it is in a steady state and provides the configuration settings to the configuration service. The configuration service notifies a cloud-based service (e.g., a virtual desktop service) that it is configured to instantiate virtual machines. The notification informs the cloud-based service that it is permitted to instantiate child virtual machines. Responsive to receiving the notification, the cloud-based service instantiates child virtual machine(s) as needed.

Abstract: Embodiments of the present disclosure provide a data backup method, an electronic device and a computer program product. The method provided includes creating a workflow related to a data source, the workflow being configured to generate a copy of the data source or to replicate the copy of the data source to a remote device. The method further includes identifying a dependency of the workflow, the dependency indicating whether the workflow depends on at least one additional workflow related to the data source. The method further includes scheduling the workflow based at least on the identified dependency. According to the embodiments of the present disclosure, by use of a dependency-based scheduling mechanism, the performance of a storage system can be improved, and consumption of resources such as a storage and an internal memory can be reduced.

Abstract: Embodiments of the present invention provide an end-to-end automated process for resource allocation as a result of monitoring resource volumes required to transmit an object and determining a difference between the an object's resource volume used in a resource interaction and the current object's resource volume required to transmit the object. As a result, the present invention provides the capability to perform resource allocation absent any input or other actions on behalf of the entity to whom the resources are being allocated.

Abstract: Systems and methods of the disclosure include: receiving, by a destination host computer system, a source hardware configuration data structure describing a hardware configuration of a source host computer system; receiving at least a part of a state of a virtual machine being migrated from the source host computer system to the destination host computer system; generating a destination hardware configuration data structure describing a hardware configuration of the destination host computer system; starting the virtual machine on the destination host computer system; comparing the source host configuration data structure and the destination host configuration data structure; responsive to detecting a difference between the source host configuration data structure and the destination host configuration data structure, notifying the virtual machine of a hardware configuration change; receiving, from the virtual machine, a hardware configuration request; and providing the destination hardware configuration data

Abstract: An apparatus and method for intelligently scheduling threads across a plurality of logical processors.

Abstract: A method for resource allocation, a terminal device, and a non-transitory computer readable storage medium are provided. The method includes the following. A photography application of a terminal device determines a running scenario. The photography application determines a performance improvement strategy for the running scenario. The photography application sends a scenario data packet to an operating system of the terminal device, where the scenario data packet includes the performance improvement strategy and is configured for the operating system to adjust allocation of system resources for the photography application according to the performance improvement strategy.

Abstract: At least one embodiment of a method of setting a module of a set of image acquisition and processing modules in a video system, the modules sharing one same resource, the method comprising: obtaining, for each module, a score function depending on an efficiency value to perform a task to which the module is assigned, a level of consumption of the resource, and a trade-off value characterizing the importance of the efficiency of performing a task relative to resource consumption, determining an updated value of the trade-off value of the resource, so that the resource consumption level reaches a threshold; identifying settings of the module optimizing a result of the score function of the module when considering the updated trade-off value; and setting the module according to the identified settings.

Abstract: An automated hardware resource optimization system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to identify computing hardware for hosting a neural network based application, determine, based on a first performance parameter of the computing hardware, a batch size for performing data processing using the neural network based application, and tune, using a data batch having the determined batch size, a second performance parameter of the computing hardware to enable substantially continuous loading of its hardware processor memory.

Abstract: A computing server includes a hardware platform with hardware resources, with at least a portion of the hardware resources to be allocated as virtualized resources. A hypervisor platform is provided based on execution of code instructions by the hardware platform. A virtual machine operates as an independent guest computing device, with at least a portion of the virtualized resources being allocated by the hypervisor platform to the virtual machine. The hypervisor platform includes a snapshot function to save the state of the virtual machine, a virtual machine activity monitor to monitor activity of the virtual machine, and an activity-based snapshot policy engine to activate the snapshot function based on the monitored activity of the virtual machine exceeding an activity threshold metric.

Abstract: An example method facilitates adjusting or enhancing performance of a process of as graphics program or animation software package, and includes providing a first User Interface (UI) control for allowing user assignment of or selection of one or more processor types, e.g., Graphics Processing Unit (GPU) or Central Processing Unit (CPU), and/or associated memory types, e.g., GPU memory and/or CPU memory, to one or more computing resources, such as variables and/or associated functions or evaluators. A drop-down menu or other control may be provided in a first UI to allow for user specification of or assignment of one or more computing resources, e.g., CPU or GPU processors and/or memory to one or more variables, data structures, associated functions or other executable code. In a specific implementation, one UI control facilitates user specification of one or more evaluators of a plugin, wherein the one or more evaluators are usable by a host application of the plugin.

Abstract: A solution is proposed for managing a computing environment. A corresponding method comprises detecting critical commands and applying each critical command and possible following commands to the computing environment by maintaining an unchanged image thereof; a command effect of the application of the critical command on the computing environment is verified according to one or more operative parameters thereof, and the application of the critical/following commands is integrated into the computing environment in in response to a positive result of the verification. A computer program and a computer program product for performing the method are also proposed. Moreover, a system for implementing the method is proposed.

Abstract: A method of thermally managing an information handling system includes executing a security scan to simulate a maximum workload consuming an electrical power, and receiving sensory signals representing temperatures associated with the information handling system. A system ambient capability above an ambient temperature when operating the information handling system at the maximum workload is then determined.