A service processing method, performed by a cloud application management server, includes: upon receiving an allocation request from a target terminal, acquiring N pieces of selection reference information corresponding to a pending edge server and related to the target terminal and running reference information, the pending edge server being one of P edge servers connected to the cloud application management server; upon determining that the pending edge server meets a requirement of providing a running service of a target cloud application for the target terminal, determining a connection reference score corresponding to the pending edge server; storing the connection reference score and identification information about the pending edge server into a candidate set; and transmitting the candidate set to the target terminal.

Techniques are disclosed for scheduling artificial intelligence model partitions for execution in an information processing system. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A reversed computation graph corresponding to a computation graph generated based on the intermediate representation is obtained. Nodes in the reversed computation graph represent functions related to the artificial intelligence model, and one or more directed edges in the reversed computation graph represent one or more dependencies between the functions. The reversed computation graph is partitioned into sequential partitions, such that the partitions are executed sequentially and functions corresponding to nodes in each partition are executed in parallel.

The present disclosure relates to a method performed by a process control node (210) configured to allocate resources shared by a plurality of tenant applications, wherein each tenant application comprises a selection of non real-time processes and real-time processes, the method comprising receiving a first resource request, from a tenant application, indicative of resources requested to be allocated, by the process control node, for one or more real-time processes of the tenant application, evaluating a scheduling test to determine if the set of processing resources can be allocated from the shared resources by determining if resources requested by the first resource request can be allocated, and if it is determined that the requested resources can be allocated from the shared resources, the method further comprises performing the steps starting the one or more real-time processes of the tenant application within a resource partition of the tenant application, calculating updated resource quotas and priorities for non real-time processes comprised by the tenant application, transmitting a first resource response to the tenant application.

An approach for a hypervisor to throttle CPU utilization based on a CPU utilization throttling request received for a data flow is presented. A method comprises receiving a request for a CPU utilization throttling. The request is parsed to extract a CPU utilization level and a data flow identifier of the data flow. Upon receiving a data packet that belongs to the data flow identified by the data flow identifier, a packet size of the data packet is determined, and a rate limit table is accessed to determine, based on the CPU utilization level and the packet size, a rate limit for the data packet. If it is determined, based at least on the rate limit, that the CPU utilization level for the data flow would be exceeded if the data packet is transmitted toward its destination, then a recommendation is generated to drop the data packet.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for job management in a distributed network include a prioritizer that determines a priority level for a job and inserts the job into a priority queue based on the priority level, a scheduler that, for each element in the distributed network, requests the priorities of one or more jobs scheduled for execution, evaluates, based on the priorities of the one or more jobs scheduled for execution, the priority of a particular job with respect to the element, determines, based on the priorities, that the network element is free to perform job processes, and upon determining that a network element is free, scheduling a particular job for execution, and an executor that determines that all local and remote resources required for the scheduled job are available and executes the job according to processes defined within the distributed network.

A computer based system and method for multilateral computing resource reallocation and asset transaction migration may include: receiving a resource transaction request; determining a policy for the request; identifying, in a resource monitoring database, resources to service the request and choosing resources matching the policy determined for the request; and documenting the choosing of resources in the monitoring database. Embodiments may further include automatically reallocating occupied resources to alternative transactions and/or migrating currently-running tasks to idle resources, for example according to predefined conditions. Embodiments of the invention may allow performing various dynamic, granular computational resource and/or asset reallocation and/or transaction migration procedures which may involve dynamic composition granular individual resources and/or assets (e.g. of multiple types and/or sizes) into functional resources (to be used by, e.g., various workload execution instances) by a resource reallocation hub, which may further include various dedicated modules and/or engines and/or components.

Disclosed are various embodiments for optimizing the migration of pages of memory servers in cluster memory systems. To begin, a computing device can mark in a page table of the computing device that a page stored on a first memory host is not present. Then, the computing device can flush a translation lookaside buffer of the computing device. Next, the computing device can copy the page from the first memory host to a second memory host. Moving on, the computing device can update a page mapping table to reflect that the page is stored in the second memory host. Then, the computing device can mark in the page table of the computing device that the page stored in the second memory host is present. Subsequently, the computing device can discard the page stored on the first memory host.

Systems and methods related to integrated memory pooling and direct swap caching are described. A system includes a compute node comprising a local memory and a pooled memory. The system further includes a host operating system (OS) having initial access to: (1) a first swappable range of memory addresses associated with the local memory and a non-swappable range of memory addresses associated with the local memory, and (2) a second swappable range of memory addresses associated with the pooled memory. The system further includes a data-mover offload engine configured to perform a cleanup operation, including: (1) restore a state of any memory content swapped-out from a memory location within the first swappable range of memory addresses to the pooled memory, and (2) move from the local memory any memory content swapped-in from a memory location within the second swappable range of memory addresses back out to the pooled memory.

A real-time computational device includes a programmable real-time processor, a communications input port which is connected to the programmable real-time processor through a first broadside interface, and a communications output port which is connected to the programmable real-time processor through a second broadside interface. Both broadside interfaces enable 1024 bits of data to be transferred across each of the broadside interfaces in a single clock cycle of the programmable real-time processor.