A resource usage platform is disclosed. The platform performs preemptive container load balancing, auto scaling, and placement in a computing system. Resource usage data is collected from containers and used to train a model that generates inferences regarding resource usage. The resource usage operations are performed based on the inferences and on environment data such as available resources, service needs, and hardware requirements.

Systems and methods for dynamically relocating pods to optimize inter-pod networking efficiency are provided. The method comprises receiving and storing inter-pod traffic data for a plurality of pods. The plurality of pods includes a first pod, a second pod, and a third pod. The method further includes receiving and storing node resource availability data for each node of a plurality of nodes, generating a queue that sorts the plurality of pods by an amount of inter-pod traffic indicated by the inter-pod traffic data, generating a hash that maps one or more parameters to the plurality of nodes, selecting, based on the generated hash, a node of the plurality of nodes, and dynamically relocating a highest ranked pod of the plurality of pods from the generated queue to the selected node.

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. Disclosed is a method of operating a MEC system, comprising the step of instantiating a first application in the MEC system, which comprises the step of a first entity transmitting an application instantiation request to a second entity, wherein the application instantiation request comprises pre-emption information concerning the first application, such that the pre-emption information may be used to pre-empt the first application in the future, if required.

The current document is directed to methods and systems that generate recommendations for resource specifications used in virtual-machine-hosting requests. When distributed applications are submitted to distributed-computer-system-based hosting platforms for hosting, the hosting requester generally specifies the computational resources that will need to be provisioned for each virtual machine included in a set of virtual machines that correspond to the distributed application, such as the processor bandwidth, memory size, local and remote networking bandwidths, and data-storage capacity needed for supporting execution of each virtual machine. In many cases, the hosting platform reserves the specified computational resources and accordingly charges for them. However, in many cases, the specified computational resources significantly exceed the computational resources actually needed for hosting the distributed application. The currently disclosed methods and systems employ machine learning to provide accurate estimates of the computational resources for the VMs of a distributed application.

A device may receive a job request associated with a data processing job, including job timing data specifying a time at which the data processing job is to be executed by a virtual computing environment. The device may receive user data associated with the job request and validate the data processing job based on the user data. In addition, the device may identify a priority associated with the data processing job, based on the user data and the job timing data. The device may provide, to a job queue, job data that corresponds to the data processing job, and monitor the virtual computing environment to determine when virtual resources are available. The device may also determine, based on the monitoring, that a virtual resource is available and, based on the determination and the priority, provide the virtual resource with data that causes execution of the data processing job.

There is disclosed in one example a computer apparatus, including: a hardware platform including a central processor unit (CPU) and a memory; and instructions encoded within the memory to instruct the CPU to: enumerate a plurality of running processes, and associate resource demands with the running processes; predict a resource starvation condition for at least one process; rank the plurality of running processes according to a dynamic ranking algorithm, wherein the ranking algorithm includes user engagement as an input for ranking a process; and according to the ranking and a safeguard algorithm, deallocate resources from a process ranked lower than the at least one process and assign the deallocated resources to the at least one process to mitigate the predicted resource starvation condition.

The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Example methods are provided to deploy an Internet of Things (IoT) solution in a hybrid environment. The methods include deploying a first agent application on a first edge gateway of a first vendor by the first edge gateway. The first agent application is configured to collect a first set of information associated with the first edge gateway. The methods include deploying a second agent application on a second edge gateway of a second vendor by the second edge gateway. The second agent application is configured to collect a second set of information associated with the second edge gateway. In response to a determination of a first virtualized computing environment on the first edge gateway or a second virtualized computing environment on the second edge gateway fulfils a first requirement of a template to deploy the IoT solution, the methods include deploying the IoT solution in the first virtualized computing environment, the second virtualized computing environment, or both.

A virtual-machine cold migration method, an electronic device and a storage medium, which relate to the field of artificial intelligence, such as cloud computing, distributed storage, or the like. The method may include: selecting a node as a target physical machine from nodes of a cluster where a to-be-migrated virtual machine is located, the target physical machine and a source physical machine where the virtual machine is located being different nodes; and migrating, by means of point-to-point data transmission, system disk data and data disk data in the virtual machine from the source physical machine to the target physical machine to obtain a migrated virtual machine, and deleting the virtual machine on the source physical machine.

Arrangements for resource optimization and control are provided. In some aspects, one or more work process requests may be received. The work process requests may be aggregated to identify a current book of work. In some examples, availability data from a variety of sources, such as bots, resource operators, and the like, may be received. In some aspects, license data associated with the one or more bot resources may be retrieved. A machine learning engine may be executed to determine an optimal number of resources, type of resources, and the like, to process the book of work. Based on the determination, one or more instructions may be generated. For instance, instructions to provision one or more bots may be generated, instructions assigning work processes to one or more resource operators may be generated, and the like. The generated instructions may be transmitted to a resource for execution.