Abstract: Techniques for determining reliability of a workload migration activity are disclosed. In one embodiment, sub-tasks associated with the workload migration activity may be determined. Further, statistical data associated with an execution of the sub-tasks corresponding to different instances of the workload migration activity may be retrieved. Furthermore, a reliability model may be trained through machine learning using the statistical data to determine reliability of the workload migration activity. Then, the reliability of a new workload migration activity may be determined using the trained reliability model.

Abstract: A method, a device, and a non-transitory storage medium are described in which an elastic platform virtualization service is provided in relation to a virtual device. The elastic platform virtualization service includes logic that provides for the management of a virtualized device during its life cycle. The creation or reconfiguration of the virtualized device is based on a tertiary choice between using dedicated hardware and dedicated kernel; common hardware and common kernel; or a combination of the dedicated hardware, dedicated kernel, common hardware, and common kernel.

Abstract: Techniques are described for performing browser-driven application capture of application installations. When the browser on the client machine detects a request to begin an application capture session, it downloads an orchestrator binary from an origin server. The orchestrator is a self-extracting executable that decompresses components responsible for preparing the client machine for the application capture session. Preparing the client machine includes starting a local web server, executing a registry script to create the necessary registry state, mounting a virtual disk, and deploying an agent that will record state changes on the client machine. Once the client machine has been prepared, the application installation can begin. During the installation process, the agent intercepts state changes occurring on the client machine and redirects them to the virtual disk.

Abstract: Methods and systems for searching a frozen index are provided. Exemplary methods include: a method may comprise: receiving an initial search and a subsequent search; loading the initial search and the subsequent search into a throttled thread pool, the throttled thread pool including; getting the initial search from the throttled thread pool; storing a first shard from a mass storage in a memory in response to the initial search; performing the initial search on the first shard; providing first top search result scores from the initial search; and removing the first shard from the memory when the initial search is completed.

Abstract: In some examples, a system generates a neural network comprising logical identifiers of compute resources. For executing the neural network, the system maps the logical identifiers to physical addresses of physical resources, and loads instructions of the neural network onto the physical resources, wherein the loading comprises converting the logical identifiers in the neural network to the physical addresses.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for balancing loads, and a computer-readable storage medium. The method includes: for each data processing unit in a set of data processing units in a data processing system, acquiring current input data of the data processing unit for a current clock cycle and next input data of the data processing unit for a next clock cycle; and determining a first metric value indicating changes in input data of said data processing unit in the next clock cycle based on a comparison between the current input data and the next input data. The method further includes controlling an operating state of the set of data processing units in the next clock cycle based on the first metric value determined for the set of data processing units.

Abstract: The present invention provides a method of operating a heterogeneous computing system comprising a plurality of computation nodes and a plurality of booster nodes, at least one of the plurality of computation nodes and plurality of booster nodes being arranged to compute a computation task, the computation task comprising a plurality of sub-tasks, wherein in a first computing iteration, the plurality of sub-tasks are assigned to and processed by ones of the plurality of computation nodes and booster nodes in a first distribution; and information relating to the processing of the plurality of sub-tasks by the plurality of computation nodes and booster nodes is used to generate a further distribution of the sub-tasks between the computation nodes and booster node for processing thereby in a further computing iteration.

Abstract: Technologies for providing efficient migration of services include a server device. The server device includes compute engine circuitry to execute a set of services on behalf of a terminal device and migration accelerator circuitry. The migration accelerator circuitry is to determine whether execution of the services is to be migrated from an edge station in which the present server device is located to a second edge station in which a second server device is located, determine a prioritization of the services executed by the server device, and send, in response to a determination that the services are to be migrated and as a function of the determined prioritization, data utilized by each service to the second server device of the second edge station to migrate the services. Other embodiments are also described and claimed.

Abstract: Certain embodiments described herein are generally directed to handling a hypervisor restart event in a distributed network system. Embodiments include receiving, by a central controller, a session identifier from a first hypervisor. Embodiments further include comparing, by the central controller, the session identifier to a stored session identifier associated with the first hypervisor. Embodiments further include determining, by the central controller based on the session identifier not matching the stored session identifier associated with the first hypervisor, that the first hypervisor has restarted. Embodiments further include updating, by the central controller, the stored session identifier associated with the first hypervisor to match the session identifier. Embodiments further include identifying, by the central controller, a second hypervisor that is associated with the first hypervisor.

Abstract: Cascading job scheduling in guests is disclosed. For example, first, second, third, and fourth nodes, each execute respective first, second, third, and fourth pluralities of guests each of which executes respective first, second, third, and fourth pluralities of jobs. A scheduler executes on a processor to receive a current capacity update of the first node. A respective quantity of jobs executing on each of the first, second, third, and fourth nodes is tracked. A first, second, third, and fourth estimated capacity of the respective first, second, third, and fourth nodes is calculated. The first, second, third, and fourth nodes are ranked in a list based on the respective estimated capacities. A request to execute a job is received. The first, second, and third nodes are selected as a schedulable set based on the list. A schedulable set notice and the job are sent to the first node to be executed.

Abstract: The present invention provides a method for allocating and managing a cluster resource on a cloud platform, the method comprising the steps of: generating service group information, by means of a cloud platform system, when a cluster resource allocation request is input; adding/deleting a user of the service group; selecting a cluster to be allocated to the user of the service group and inputting allocation information; querying a cluster resource availability; registering resource allocation information in the service group and allocating a resource to complete cluster resource allocation, when the resource is available as a result of the querying of the cluster resource availability; checking whether a cluster resource can be added when the resource is insufficient as a result of the querying of the cluster resource availability; registering the further cluster resource when the cluster resource can be added; registering resource allocation information in the service group and allocating the resource to com

Abstract: Embodiments herein provide an analytics-based solution for recommending for the initial placement of one or more virtual desktop (VD) users in a multi-cloud environment and/or for the subsequent migration of one or more users in a multi-cloud environment. In one or more embodiments, a placement (initial or migratory) recommendation may be based one or more metrics related to the cloud deployment and user conditions/requirements. In one or more embodiments, a placement recommendation is based on assessing functionality requirements of a user or users and a correlation analysis with other functionality or functionalities as it relates to functionality that is available at specific cloud deployments. In one or more embodiments, the recommendation may alternatively or additionally be based upon latency analytics, in which end-to-end latency from the user to an application or applications may be considered as metric(s) in the recommendation determination.

Abstract: This application provides a resource pool management method and apparatus, a resource pool control unit, and a communications device. The method is applied to a resource pool system including a plurality of communications devices, and one resource pool control unit is deployed on each communications device. A first resource pool control unit that is responsible for managing a resource pool at a current moment receives a resource application request of an application program on any communications device, allocates, from the resource pool according to a preset rule, a first resource including one or more logical hardware devices, and sends a resource configuration request to a second resource pool control unit, so that the second resource pool control unit completes configuration of the first resource based on the resource configuration request, to provide a required hardware device resource for the application program.

Abstract: A computer-implemented method for scheduling a series of recurring events including: receiving one or more requests to allocate resource(s) to a series of recurring events, wherein the one or more requests specify, for each event, a corresponding desired time period over which the resource(s) are to be allocated, and the one or more requests further specify one or more adjustment criteria for defining, for one or more of the events, one or more permissibly adjusted time periods from the desired time period; obtaining, for each event, resource availability data indicative of an availability of the resource(s) during the desired time period; and, for each event: determining, based on the resource availability data, a viable time period, wherein the viable time period is either the desired time period or a permissibly adjusted time period that satisfies the one or more adjustment criteria; and allocating the resource(s) to the viable time period.

Abstract: Methods and systems are provided for the deployment of machine learning based processes to public clouds. For example, a method for deploying a machine learning based process may include developing and training the machine learning based process to perform an activity, performing at least one of identifying and receiving an identification of a set of one or more public clouds that comply with a set of regulatory criteria used to regulate the activity, selecting a first public cloud of the set of one or more public clouds that complies with the set of regulatory criteria used to regulate the activity, and deploying the machine learning based process to the first public cloud of the set of one or more public clouds.

Abstract: A workload compliance governor system includes a management system coupled to a computing system. A workload compliance governor subsystem in the computing system receives a workload performance request associated with a workload, exchanges hardware compose communications with the management system to compose hardware components for the workload, and receives back an identification of hardware components. The workload compliance governor subsystem then determines that the identified hardware components satisfy hardware compliance requirements for the workload, and configures the identified hardware components in the computing system based on the software compliance requirements for the workload in order to cause those identified hardware components to provide an operating system and at least one application that operate to perform the workload.

Abstract: Neural network workload re-allocation in a system-on-chip having multiple heterogenous processors executing one or more neural network units may be based on measurements associated with the processors' conditions and on metadata associated with the neural network units. Metadata may be contained in an input file along with neural network information. Measurements characterizing operation of the processors may be obtained and compared with one or more thresholds. A neural network unit executing on a processor may be identified as a candidate for re-allocation based on metadata associated with the neural network unit and results of the comparisons. A target processor may be identified based on the metadata and results of the comparisons, and the candidate neural network neural network unit may be re-allocated to the target processor.

Abstract: A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.

Abstract: Techniques described herein can optimize usage of computing resources in a data system. Dynamic throttling can be performed locally on a computing resource in the foreground and autoscaling can be performed in a centralized fashion in the background. Dynamic throttling can lower the load without overshooting while minimizing oscillation and reducing the throttle quickly. Autoscaling may involve scaling in or out the number of computing resources in a cluster as well as scaling up or down the type of computing resources to handle different types of situations.

Abstract: Embodiments of the present disclosure relate to a method for managing backup jobs, an electronic device, and a computer program product. The method includes: determining expected execution durations of a group of to-be-executed backup jobs; dividing the group of to-be-executed backup jobs into a plurality of backup job subsets based on the expected execution durations, wherein a difference between the expected execution durations of every two backup jobs in each backup job subset does not exceed a predetermined threshold duration; and adjusting an execution plan of the group of to-be-executed backup jobs to cause the backup jobs in at least one backup job subset in the plurality of backup job subsets to simultaneously begin to be executed.