Methods, systems and computer program products for configuring microservices platforms in one or more computing clusters. In one of the computing clusters, a request to instantiate a microservice platform is received, wherein the request is received in a computing cluster having a first node and a second node, and wherein the first node and second node comprise a first virtualized storage controller and a second virtualized storage controller, respectively. The storage controllers each manage their respective storage pools comprising local storage devices. A first microservice manager is deployed on the first node and a second microservice manager is deployed on the second node. The first virtualized storage controller on the first node performs storage management operations for a first microservice instantiated by the first microservice manager, and the second virtualized storage controller on the second node performs storage management operations for a second microservice instantiated by the second microservice manager.

One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Methods and systems for scaling computing processes within a serverless computing environment are provided. In one embodiment, a method is provided that includes receiving a request to execute a computing process in the serverless computing environment. A first node may be created within the serverless computing environment to execute the computing process. A first amount of computing resources may be assigned to the first node. It may be determined later that the first amount of computing resources are not sufficient to implement the first node. A second amount of computing resources may be determined with a vertical autoscaling process and a second node may be created within the serverless computing environment using a horizontal autoscaling process. The second node may be assigned the second amount of computing resources. The computing process may then be executed using both the first and second nodes within the serverless computing environment.

Disclosed are aspects of network function placement in virtual graphics processing unit (vGPU)-enabled environments. In one example a network function request is associated with a network function. A scheduler selects a vGPU-enabled GPU to handle the network function request. The vGPU-enabled GPU is selected in consideration of a network function memory requirement or a network function IO requirement. The network function request is processed using an instance of the network function within a virtual machine that is executed using the selected vGPU-enabled GPU.

A method, computer program product, and system for managing parallel microservices are provided. The method may include identifying information pertaining to each of a plurality of target microservices to be invoked by an issuer microservice, a predefined condition associated with the plurality of target microservices, and an action to be executed by the issuer microservice in response to the predefined condition being satisfied. The method may also include sending a first request to available target microservices of the plurality of target microservices based on the information pertaining to the respective available target microservices. The method may also include, in response to receiving a response to the first request from an available target microservice of the available target microservices, determining whether the predefined condition is satisfied, and in response to determining that the predefined condition is satisfied, causing the action to be executed by the issuer microservice.

A charged particle beam device includes a charged particle beam device main body, a computer configured to control the charged particle beam device main body, including a CPU and a DRAM, and including software for controlling the charged particle beam device main body, a monitoring unit configured to monitor a resource usage status in the computer, an allocation availability determination unit configured to determine whether or not a resource for executing processing required by the software is allocatable in the computer according to a monitoring result of the monitoring unit, and a notification unit configured to notify, when the determination of the allocation availability determination unit is negative, information indicating that the determination is negative.

A method for adjusting machine learning models in a system including a plurality of devices is suggested. The method includes providing a system including a plurality of devices, wherein the devices have computational resource capacities; providing one or more machine learning tasks; providing a repository of ML models for the one or more tasks, wherein a plurality of the ML models of a single task solve the same task with different computational resources requirements and different quality metrics; selecting a device of the plurality of devices of the system to execute a task, wherein the selected device has available computational resource capacities; and selecting, from the repository of ML models of the task to be executed, one of the ML models, wherein the computational resources requirements of the selected ML model do not exceed the available computational resource capacities of the selected device. Systems configured to perform the methods as disclosed herein are also suggested.

One example method includes evaluating code of a quantum circuit, estimating one or more runtime statistics concerning the code, generating a recommendation based on the one or more runtime statistics, and the recommendation identifies one or more resources recommended to be used to execute the quantum circuit, checking availability of the resources for executing the quantum circuit, allocating resources, when available, sufficient to execute the quantum circuit, and using the allocated resources to execute the quantum circuit.

Technologies for providing a multi-tenant local breakout switching and dynamic load balancing include a network device to receive network traffic that includes a packet associated with a tenant. Upon a determination that the packet is encrypted, a secret key associated with the tenant is retrieved. The network device decrypts a payload from the packet using the secret key. The payload is indicative of one or more characteristics associated with network traffic. The network device evaluates the characteristics and determines whether the network traffic is associated with a workload requesting compute from a service hosted by a network platform. If so, the network device forwards the network traffic to the service.

A task scheduling method includes retrieving at least first data generated by monitoring a plurality of processors and second data generated by monitoring a memory subsystem, generating task type data and processor type data according to at least the first data and the second data, dynamically estimating current capacities and maximum capacities of the plurality of processors according to the task type data and the processor type data, generating prediction data according to the task type data, the processor type data, and the current capacities and the maximum capacities of the plurality of processors, scheduling a task according to the task type data, the processor type data, the prediction data, and the current capacities and the maximum capacities of the plurality of processors.