A system includes a processing system and a memory system. The memory system stores instructions for identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The instructions when executed by the processing system further result in determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.

A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

Embodiments provide a fast multi-agent reinforcement learning (RL) pipeline that runs the full RL workflow end-to-end on a single GPU, using a single store of data for simulation roll-outs, inference, and training. Specifically, simulations and agents in each simulation are run in tandem, taking advantage of the parallel capabilities of the GPU. This way, the costly GPU-CPU communication and copying is significantly reduced, and simulation sampling and learning rates are in turn improved. In this way, a large number of simulations may be concurrently run on the GPU, thus largely improving efficiency of the RL training.

The present disclosure discloses a task processing method and a distributed computing framework. A specific embodiment of the method includes: parsing an expression corresponding to a distributed computing task, and constructing task description information corresponding to the distributed computing task, the task description information being used to describe a corresponding relationship between an operator and a distributed dataset, and the operator acting on at least one of the distributed dataset or distributed datasets obtained by grouping the distributed dataset; determining, based on the task description information, a distributed dataset the operator acting on; and performing distributed computing on the distributed dataset the operator acting on using the operator. In the distributed computing, the acting scope and nesting relationship of the operator is described by constructing a topology.

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.

Method and apparatus are provided for managing a distrusted system. The method comprises receiving, by a cluster management agent, a cluster specification update that includes a container image manifest content that describes an infrastructure of the distributed system; converting, by a runtime container engine of the cluster management agent, the container image manifest content into an operating system bootloader consumable disk image for rebooting one or more nodes in the distributed system; and initiating, by the cluster management agent, a system reboot using the operating system bootloader consumable disk image for a node in the distributed system to update the node to be in compliance with the cluster specification update. The cluster specification update is received via a local API of the cluster management agent in the absence of internet access or via a communication channel through an internet connection with the cluster management agent.

One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

A computerized-method to conduct a staggered maintenance activity based on tenants' prioritization for tenants of a cloud-based Software as a Service (SaaS) platform contact-center, is provided herein. The computerized-method includes operating a Staggered-Maintenance-Activity (SMA) module. The SMA module includes: (i) selecting a plurality of tenants from the data store of one or more tenants, for a migration activity; (ii) allocating each tenant in the plurality of tenants for migration activity, to a maintenance-window of one or more maintenance-windows; (iii) prioritizing the allocated plurality of tenants to yield an ordered list of tenants, in each maintenance-window of the one or more maintenance-windows; (iv) operating a migration activity during each maintenance-window, of the one or more maintenance-windows, according to the yielded ordered list of tenants; and (v) sending a notification with details of each migration activity to a corresponding tenant administrator, to be displayed, via a display unit.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for using a virtual desktop. A method in one embodiment includes receiving, at a first edge node in a plurality of edge nodes, an instruction from a first set of input devices in a plurality of peripheral devices. The instruction is for use of a first virtual desktop deployed on the first edge node. The method further includes: using the first virtual desktop based on the instruction by using resources at the first edge node. The method further includes: sending data to an output device in the plurality of peripheral devices, wherein the data is associated with the use of the first virtual desktop. The solution for using a virtual desktop of the present application enables the use of a virtual desktop using resources at an edge node without requiring a client.

A method for managing information handling system updates includes identifying topological groups within a managed domain of information handling system devices and identifying one or more homogeneous subgroups, each of which corresponds to a single device type, within each of the identified topological groups. Device updates may then be performed for the managed domain based on the homogeneous subgroups. All instances of a particular device type within the managed domain are updated by performing subgroup-aware update operations that include transmitting a single update image for the particular device type to each topological group that includes a homogeneous subgroup corresponding to the particular device type. The single update image is then distributed to each instance of the particular device type within the topologic group.