Abstract: In an embodiment, an apparatus comprises one or more processors and one or more memories communicatively coupled to the one or more processors and storing instructions which, when processed by the one or more processors, cause: providing one or more first software services to a client computing device corresponding to a particular entity profile for a particular entity; identifying data in the particular entity profile identifying one or more second software services used by the particular entity; while executing a first workflow for the one or more first software services, identifying a trigger for the one or more second software services; in response to identifying the trigger, executing a second workflow for the one or more second software services using data extracted from the first workflow.

Abstract: Techniques are disclosed for providing method for providing an event timer for event synchronization across Kubernetes clusters. The event timer is configured to provide event synchronization on behalf of microservice instances in the cloud computing environment. In response to a request for an event timer for a timed event, it is determined whether the requested event timer has been started for a second microservice instance. If the requested event timer has been started, a state of the requested event timer is sent to the first microservice instance If the requested event timer has not been started, the requested event timer is instantiated, and a state of the instantiated event timer is stored in a database. The instantiated event timer is independent of the first and second microservice instances. In response to an expiration of the event timer, a single callback for processing of the event is generated.

Abstract: A portion of a radio-based application (RBA) is executed at a first runtime environment (RTE) launched at a server. The first RTE includes a software program for processing messages between a pair of layers of the RBA. In response to determining that the portion of the RBA is to be executed at a second RTE, at least a subset of state information pertaining to the inter-layer messages is transferred to the second RTE without pausing the RBA. After the state information is transferred, the portion of the RBA is run at the second RTE.

Abstract: A system can train a neural network model at a first edge device regarding respective amounts of time to process data at the first edge device compared to corresponding amounts of time to process the data at cloud computing equipment that is connected to the first edge device via a communications network, wherein the data is generated at the first edge device. The system can update the neural network model to produce an updated neural network model based on information received from a second edge device regarding a performance of the cloud computing equipment in processing the data, wherein the first edge device and the second edge device having respective different processing capabilities. The system can determine whether to process first data, generated at the first edge device, locally at the first edge device.

Abstract: Graphics processing systems and methods are described. A graphics processing apparatus may comprise one or more graphics processing engines, a memory, a memory management unit (MMU) including a GPU second level page table and GPU dirty bit tracking, and a provisioning agent to receive a request from a virtual machine monitor (VMM) to provision a subcluster of graphics processing apparatuses, the subcluster including a plurality of graphics processing engines from a plurality of graphics processing apparatuses connected using a scale-up fabric, provision the scale-up fabric to route data within the subcluster of graphics processing apparatuses, and provision a plurality of resources on the graphics processing apparatus for the subcluster based on the request from the VMM.

Abstract: Embodiments of systems and methods are provided to trigger migration of a workload from cloud-based resources to local resources, or vice versa. In the disclosed embodiments, an orchestration service receives telemetry data from a client system associated with a user and cloud resource usage data corresponding to the user from a plurality of cloud service providers. Before the end of each cloud computing service billing cycle, the orchestration service: uses the cloud resource usage data and/or the telemetry data to determine a cloud resource usage, which is expected for the user at the end of the cloud computing service billing cycle; generates a trigger to migrate the user's workload from cloud-based resources to local resources, or vice versa, based on the expected cloud resource usage; and initiates migration of the user's workload if a trigger is generated. As such, the orchestration service can be used to effectively manage per-user cloud resource costs.

Abstract: Methods, systems, and computer-readable storage media for executing, within the container orchestration system, the application using one or more resource units, each resource unit including an application container and an ancillary container, the ancillary container executing a set of GPU metric exporters, receiving, from the application and for each resource unit, a GPU metric including a GPU utilization associated with a respective resource unit, determining, for each resource unit, a custom GPU metric based on a respective GPU metric, the custom GPU metric aggregating values of the respective GPU metric over a time period, determining, by an autoscaler, an average GPU metric based on one or more custom GPU metrics, and selectively scaling, by the autoscaler, the application within the container orchestration system based on the average GPU metric by adjusting a number of resource units executing the application.

Abstract: Remote kernel execution in a heterogeneous computing system can include executing, using a device processor of a device communicatively linked to a host processor, a device runtime and receiving from the host processor within a hardware submission queue of the device, a command. The command requests execution of a software kernel and specifies a descriptor stored in a region of a memory of the device shared with the host processor. In response to receiving the command, the device runtime, as executed by the device processor, invokes a runner program associated with the software kernel. The runner program can map a physical address of the descriptor to a virtual memory address corresponding to the descriptor that is usable by the software kernel. The runner program can execute the software kernel. The software kernel can access data specified by the descriptor using the virtual memory address as provided by the runner program.

Abstract: Systems, methods, and apparatuses for resource bandwidth monitoring and control are described. For example, in some embodiments, an apparatus comprising a requestor device to send a credit based request, a receiver device to receive and consume the credit based request, and a delay element in a return path between the requestor and receiver devices, the delay element to delay a credit based response from the receiver to the requestor are detailed.

Abstract: Resegmenting chunks of data for load balancing is disclosed. A plurality of first chunks of data is received. The plurality of first chunks of data includes one or more entries that include raw data produced by a component of an information technology environment and that reflects activity in the information technology environment. The plurality of first chunks of data is resegmented into a plurality of second chunks of data based on a source type of the plurality of first chunks. A first subset of the plurality of second chunks of data is distributed to a first indexer of a set of indexers. An occurrence of a trigger event is determined, and in response to the trigger event, a second subset of the plurality of second chunks of data is distributed to a second indexer of the set of indexers.

Abstract: Decentralized cloud service assessment includes using a self-executing data structure, an error confirmation capsule (ECC) generated in response to a cloud service failure experienced by a cloud service client (CSC). One or more technical performance indicia corresponding to the cloud service failure are extracted from the ECC in response to the validating. The one or more technical performance indicia are compared to one or more electronically stored predefined performance norms of a cloud service provider (CSP) associated with the cloud service failure. Based on the comparing, a comparative ranking of the CSP is determined. A graphical user interface display is generated based on comparative rankings of the CSP and one or more other CSPs.

Abstract: A method to deploy a plurality of event-driven application components of an event-driven application in a distributed computing environment is described. The method includes automatically analyzing application source code of the event-driven application, using one or more processors, to identify relationships between the plurality of event-driven application components. Thereafter, a set of rules are applied to, based on the automatic analysis, generate assignment data recording assignments of event-driven application components to a plurality of computational nodes in the distributed computing environment. The set of rules is also applied to determine component requirements for each of the plurality of event-driven application components required to support execution at an assigned computational node in the distributed computing environment.

Abstract: Described embodiments provide systems and method for determining action insights to address, correct or fix application failures. A device can identify an assignment of a user to a virtual machine and the assignment can enable launch of an application with use of the same virtual machine on behalf of the user. The virtual machine can be one of a delivery group of virtual machines. A threshold can be determined for the virtual machine that indicates a likely failure of the virtual machine to launch the application based data about performance of the delivery group. The device can modify the assignment of a user from the virtual machine to another virtual machine of the delivery group based on a comparison of a load on the virtual machine and the determined threshold, so as to avoid failure of the application to launch.

Abstract: An information processing apparatus calculates a first load value indicating the load of a plurality of first jobs whose used node counts are less than or equal to a threshold among jobs waiting for execution, and a second load value indicating the load of a plurality of second jobs whose used node counts exceed the threshold among the waiting jobs. If load conditions including one where the first load value exceeds the second load value are satisfied, the information processing apparatus combines two or more first jobs among the plurality of first jobs to create a combined job whose used node count exceeds the threshold. The information processing apparatus causes a first node group to execute the remaining first jobs among the plurality of first jobs and causes a second node group to execute the combined job and the plurality of second job.

Abstract: A resource scheduling method and system, and a computer-readable storage medium are provided. The method comprises: a control instruction inputted by a user (S101) is acquired; on the basis of the control instruction, an intended device (S102) is determined; in the case where the intended device is not able to be started, with regard to each function of the intended device, a constructed knowledge graph is queried for alternative devices associated with the function, the knowledge graph having a plurality of functions stored therein as well as devices associated with the plurality of functions (S103, S105); and a device to be started is determined from all the alternative devices and starting the determined device (S106). In the resource scheduling method, in the case where an intended device specified by a user is not able to be started, alternative devices are appropriately determined.

Abstract: A processing system monitors and synchronizes parallel execution of workgroups (WGs). One or more of the WGs perform (e.g., periodically or in response to a trigger such as an indication of oversubscription) a waiting atomic instruction. In response to a comparison between an atomic value produced as a result of the waiting atomic instruction and an expected value, WGs that fail to produce a correct atomic value are identified as being in a waiting state (e.g., waiting for a synchronization variable). Execution of WGs in the waiting state is prevented (e.g., by a context switch) until corresponding synchronization variables are released.

Abstract: System and methods providing for categorizing individual virtual machines, as well as the associated application that they form by working in concert, into groups based on the feasibility of hosting the processes that occur on a virtual machine within a container, as well as the relative difficulty of doing so on a virtual machine and application level. The data used to create these scores is collected from the individual machines, at regular intervals through the use of an automated scoring engine that collects and aggregates the data. Said data is then analyzed by the system, that with the aid of passed in configuration data, is configured to generate the scores to allows for an educated and focused effort to migrate from hosting applications on virtual machines to hosting applications on containers.

Abstract: One or more computer operations are triggered and executed when multiple sets of conditions that define a split trigger and that are evaluated at multiple locations are satisfied. The operations may involve allocating or deallocating computing resources (e.g., by establishing or tearing down a persistent connection), generating a message to be displayed on a computing device, altering stored data, and/or other activity. In an illustrative client/server computing environment, a first set of client conditions of a first split trigger is evaluated at a client device, using parameters observable at the client without interaction with a server. When the first of client conditions is satisfied, the client transmits a signal to a server that then evaluates a first set of server conditions of the first split trigger. If the first sets of client conditions and server conditions are satisfied, the computer operation(s) associated with the split trigger are executed.

Abstract: An apparatus comprises a processing device configured to identify a plurality of workloads to be scheduled for execution on a storage system and to analyze the plurality of workloads to predict a magnitude of storage capacity savings achieved by applying one or more deduplication algorithms to data of the plurality of workloads. The processing device is further configured to determine a prioritization of the plurality of workloads based at least in part on the predicted magnitude of the storage capacity savings achieved by applying the one or more deduplication algorithms to the data of the plurality of workloads, and to schedule the plurality of workloads for execution on the storage system based at least in part on the determined prioritization of the plurality of workloads.

Abstract: This application provides a method and an apparatus, for accelerating cold startup of an application. The method includes after identifying an event that instructs an operating system of a terminal to cold start up an application, obtaining, from a plurality of dimensions, current status information related to the cold startup of the application, where the current status information includes a hardware configuration of the terminal, current load of the operating system of the terminal, resource overheads for cold starting up the application, and duration corresponding to each of a plurality of tasks in a process of cold starting up the application. The method also includes determining, by analyzing the current status information, a plurality of objects that need to be optimized in the current process of cold starting up the application; and then obtaining, based on the determined objects.