Abstract: A system for providing computer implemented services using information handling systems includes a composed information handling system that provides, at least in part, the computer implemented services and a system control processor manager. The system control processor manager instantiates a utilization monitor in a system control processor of the composed information handling system; and monitors, using the utilization monitor, a use rate of computing resources of the composed information handling system by a client.

Abstract: A system and method for securing virtual cloud assets in a cloud computing environment against cyber threats. The method includes: determining a location of a snapshot of at least one virtual disk of a protected virtual cloud asset, wherein the virtual cloud asset is instantiated in the cloud computing environment; accessing the snapshot of the virtual disk based on the determined location; analyzing the snapshot of the protected virtual cloud asset to detect potential cyber threats risking the protected virtual cloud asset; and alerting detected potential cyber threats based on a determined priority.

Abstract: The invention relates to a method for adjusting a performance of a computing system, the method comprising receiving by the computing system at least one command to execute a computational workload from a client, applying by the computing system an identification method identifying a first system resource of the system resources which limits an execution performance of the computational workload, performing a temporary adjustment of an allocation of the first system resource to the client and evaluating whether this improves an execution performance of the computational workload, and, if this is the case, offering to the client to permanently adjust the allocation of the first system resource to the client and adjusting the allocation of the first system resource to the client permanently in case of receiving a corresponding instruction from the client. The invention also relates to a computing system and a computer program product which implement the method.

Abstract: A computer-implemented method and system for receiving, at a first computing device, an application programming interface (API) request from a second computing device, wherein the API request includes at least a first request field and a second request field, evaluating at least the first request field to determine a first complexity measure, assigning a first field score to at least the first request field based on the first complexity measure, evaluating at least the second request field to determine a second complexity measure, assigning a second field score to at least the second request field based on the second complexity measure, and combining the first field score and the second field score to generate a total field score for the API request for use in an API request complexity model for constraining a processing of the received API request from the second computing device.

Abstract: Embodiments of the present invention provide a system for intelligently optimizing the utilization of clusters. The system is configured to continuously gather real-time hardware telemetric data associated with one or more entity systems via a hardware telemetric device, continuously convert the real-time hardware telemetric data into a first color coded representation, receive one or more tasks associated with one or more entity applications, queue the one or more tasks associated with the one or more entity applications, determine hardware requirements associated with the one or more tasks, determine one or more attributes associated with the one or more tasks, convert the hardware requirements and the one or more attributes of the one or more tasks into a second color coded representation, and allocate the one or more tasks to the one or more entity systems based on the first color coded representation and the second color coded representation.

Abstract: A task pool may be created and maintained, in association with a container management service, which stores a snapshot of a one or more tasks, the performance of which is suspended or paused. Tasks may be defined as applications executable in one or more software containers instantiated on a virtual machine or compute instance, where the snapshot of a task may include an image of the one or more containers associated with the task container and state information of the one or more containers. A task may be initially provisioned in a paused state or paused after executing at least a portion of the task. Paused tasks in the task pool may be readily restarted or resumed by loading the snapshot onto a compute instance. The task pool may manage provisioning new tasks and pause tasks based on changes in demand.

Abstract: Provided is a resource allocation determination method for a VM/container, volume, and the like created as a new VM/container or volume without exceeding an upper limit of a computer resource of a node in an HCI environment. In order to determine allocation of at least one of a virtual machine, a container, and a volume in a system of the HCI environment, a use state of a computer resource shared by a virtual machine and a storage controller operating on each node is managed, and an allocation destination node of the new virtual machine, container, or volume is determined based on the use state without exceeding an upper limit of a computer resource of the allocation destination node.

Abstract: A resource use method, an electronic device, and a computer program product are provided in embodiments of the present disclosure. The method includes determining a plurality of jobs requesting to use accelerator resources to accelerate data processing. The plurality of jobs are initiated by at least one virtual machine. The method further includes allocating available accelerator resources to the plurality of jobs based on job types of the plurality of jobs. The method further includes causing the plurality of jobs to be executed using the allocated accelerator resources. With the embodiments of the present disclosure, accelerator resources can be dynamically allocated, thereby improving the overall performance of a system.

Abstract: A method and system for on-premises to cloud workload migration through cyclic deployment and evaluation. Existing processes for transferring on-premises workloads onto the public cloud are often painstakingly manual and laborious, in order to ensure proper workload interoperability between the different infrastructures. To address this existing dilemma in inter-infrastructure workload migration, the disclosed method and system employs a cyclic monitoring, deploying, and evaluating scheme to automate and implement optimal strategies for migrating on-premises workloads onto public and/or hybrid cloud computing environments.

Abstract: A snapshot handler is registered with an event monitoring subsystem of a process virtual machine, the registering specifying a trigger event of the snapshot handler, wherein the trigger event comprises execution of a specified portion of an application executing in the process virtual machine, the trigger event specified externally from a source code of the application. Responsive to the event monitoring subsystem detecting an occurrence of the trigger event, the snapshot handler is executed, storing data of an execution state of the process virtual machine at a time of occurrence of the trigger event.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for an automatic recovery job execution through a scheduling framework in a cloud environment. One or more recovery jobs are scheduled to be performed periodically for one or more registered service components included in a service instance running on a cluster node of a cloud platform. Each recovery job is associated with a corresponding service component of the service instance. A health check operation is invoked at a service component based on executing a recovery job at the scheduling framework corresponding to the service component. In response to determining that the service component needs a recovery measure based on a result from the health check operation, a recovery operation is invoked as part of executing a set of scheduled routines of the recovery job. Implemented logic for the recovery operation is stored and executed at the service component.

Abstract: User input is obtained at a central monitoring device to deploy a monitoring agent at a host computing node. Based on the user input, a job is identified to be executed by a deployment server to deploy the monitoring agent at the host computing node. An execution time window is identified to execute the job at the host computing node. The deployment server is accessed using a service account that does not require human intervention and the execution of the job is triggered within the execution time window to cause the deployment server to deploy the monitoring agent at the host computing node.

Abstract: A real-time data processing method and an apparatus thereof are provided. The method includes collecting real-time data received by one or more nodes in a distributed system; aggregating real-time data that satisfies a same predetermined condition on all the nodes to obtain a total amount of real-time data corresponding to the same predetermined condition, wherein the predetermined condition is used for representing a time at which the real-time data is collected; and calculating a degree of completeness of the real-time data that satisfies the same predetermined condition, wherein the degree of completeness is used for indicating a ratio between processed real-time data that satisfies the same predetermined conditions and the real-time data aggregated on all the nodes. Using the present disclosure, problems caused by failing to know a progress of real-time data processing in existing technologies are resolved, thereby adding a dimension of evaluation of the real-time data processing.

Abstract: Techniques are disclosed for efficient utilization worker threads in a workflow-as-a-service (WFaaS) environment. A client device may request a workflow for execution by the client device. The client device may receive the requested workflow and initialize a set of worker threads to execute the workflow and a set of heartbeater threads to monitor the set of worker threads. Upon receiving an indication of a processing delay, the client device may capture the state of the workflow, suspend execution of the workflow, and store the workflow in a temporary queue. While the processing delay persists, the client device may use the set of worker threads to execute other tasks. When the processing delay terminates, the client device may resume execution of the workflow.

Abstract: Technologies for composing a managed node with multiple processors on multiple compute sleds to cooperatively execute a workload include a memory, one or more processors connected to the memory, and an accelerator. The accelerator further includes a coherence logic unit that is configured to receive a node configuration request to execute a workload. The node configuration request identifies the compute sled and a second compute sled to be included in a managed node. The coherence logic unit is further configured to modify a portion of local working data associated with the workload on the compute sled in the memory with the one or more processors of the compute sled, determine coherence data indicative of the modification made by the one or more processors of the compute sled to the local working data in the memory, and send the coherence data to the second compute sled of the managed node.

Abstract: Computer systems and methods for managing resources are described.

Abstract: 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.

Abstract: A network of systems used for tracking of performance of resources and components thereof using resource information. The resource information may include resource performance information (e.g., operation of the resource or components thereof, or the like) that is stored within a distributed computing network. The resource performance information may be compared to resource thresholds and used to determine and distribute resource suggestions for the resource. The distributed computing network may comprise a plurality of nodes that host a distributed register for storing, updating, and allowing access to resources and resource performance information, resource thresholds, and resource suggestions.

Abstract: An information handling system may include at least one processor; and a non-transitory memory coupled to the at least one processor. The information handling system may be configured to: execute a plurality of virtual machines having workloads associated therewith; during selected times, apply a plurality of configuration settings relating to the at least one processor while executing the workloads of the plurality of virtual machines; track a plurality of performance metrics relating to the at least one processor during the selected times; and predictively determine a selected one of the plurality of configuration settings that is predicted to improve performance of the workloads.

Abstract: A method for creating a virtual machine includes: receiving a virtual machine creation request to create a plurality of virtual machines; dividing the plurality of virtual machines into a plurality of virtual machine groups; determining a home physical rack for each virtual machine group, where one virtual machine group corresponds to one home physical rack; and creating each virtual machine group on the home physical rack of each virtual machine group. Because each virtual machine group is created on a home physical rack to which each virtual machine group belongs, each virtual machine group is equivalent to one physical rack.