A computer-implemented method for capturing system memory dumps includes receiving, by a diagnostic data component, an instruction to capture a system memory dump associated with a computer process being executed by a computing system comprising one or more processing units, the system memory dump comprising data from a plurality of memory locations associated with the computer process. In response to determining that the system memory dump satisfies a predetermined criterion, the diagnostic data component sends a request for a computing resource boost from the computing system. Further, in response to the request for the computing resource boost being granted, the diagnostic data component uses additional computing resources from the one or more processing units to store the data from the plurality of memory locations in the system memory dump and executing the backlogged operations that were halted due to the system memory dump capture.

Methods, systems, computer-readable media, and apparatuses for query-based interest in a simulation are presented. An entity comprising one or more components may be simulated. The entity may be modified to include an interest component indicating, for each component in the one or more components of the entity, a query subscription to an entity database. The query subscription may comprise one or more queries. Each query of the one or more queries may comprise a component value that qualifies another entity for inclusion in a query result, and a frequency for receiving, from the entity database, updates on the query result.

Systems and apparatuses for provisioning computer services or resources and methods for making and using the same. In one embodiment, an exemplary method for performing an iterative search can include selecting a service from a group of available services for adjustment. An application associated with the selected service can be run, and an amount of resources consumed while the application is run can be captured. A provision level for the selected service, a provision type for the selected service or both can be adjusted based upon the captured amount of resources consumed. The method then can determine whether provision levels of the available services, provision types of the available services or both require further adjustment to be most performant. The approaches described herein advantageously can be applied, for example, to “right-size” or “scale” multiple resources.

In an approach to improve mobile computation while traveling by dynamically generating one or more routes base on computing resource requirements of one or more endpoint devices. Embodiments identify, in real time, a plurality of autonomous vehicles, wherein the plurality of autonomous vehicles are traveling along a common route. Further embodiments, adjust, in real time, relative positions and speeds of the plurality of autonomous vehicles to maintain the plurality of autonomous vehicles within a predetermined geographic area while traveling along the common route, and wherein the predetermined geographic area is sufficient to collectively provide an amount of edge computing resources to satisfy one or more computing resource requirements of the one or more endpoint devices located within a first autonomous vehicle. Additionally, embodiments adjust, in real time, a route of the first autonomous vehicle based on the common route of the plurality of autonomous vehicles providing the edge computing resources.

In accordance with an embodiment, described herein is a system and method for use in a distributed computing environment, for automatically scaling a cluster based on metrics being monitored. A cluster that comprises a plurality of nodes or brokers and supports one or more colocated partitions across the nodes, can be associated with an exporter process and alert manager that monitors metrics associated with the cluster. Various metrics can be associated with user-configured alerts that trigger or otherwise indicate the cluster should be scaled. When a particular alert is raised, a callback handler associated with the cluster, for example an operator, can automatically bring up one or more new nodes, that are added to the cluster, and then reassign a selection of existing colocated partitions to the new nodes/brokers, such that computational load can be distributed within the newly-scaled cluster environment.

Scaling of an ordered event stream (OES) based on a characteristic of one or more writer groups is disclosed. Scaling a portion of an OES contemporaneous to writing events into that portion can conserve computing resources in contrast to more conventional scaling techniques. Moreover, scaling an OES contemporaneously with writing events thereto can enable improved management of OES scaling for applications that can both read events from an input portion of an OES and, via interim events and interim portions of an OES, write events to an output portion of an OES. An application instance can therefore simultaneously act as both a reader group and writer group and can manage data via interim OESs, such that effects of passing the data through the interim OESs can be cascaded into a scaling of the output portion of an OES based on the writer group characteristic.

A method for allocating a plurality of virtual machines (51-55) provided on at least one host (11-15) to a virtualized network function is provided, which provides a defined functional behavior in a network and requires a total application capacity for the functional behavior, the functional behavior being provided by needed virtual machines from the plurality of virtual machines, wherein each of the at least one host has an available processing capacity which can be assigned to the virtual machines provided on the corresponding host, and each virtual machine has at least one flavor which indicates a used processing capacity of the available processing capacity of the corresponding host and which corresponds to a partial application capacity of the total application capacity provided by the corresponding virtual machine, the method comprising: —determining the total application capacity of the virtualized network function, —determining, for each of the virtual machines, the at least one flavor taking into account the available processing capacity of the host on which the corresponding virtual machine is provided, and the corresponding at least one partial application capacity, —determining the needed virtual machines from the plurality of virtual machines and needed flavors of the needed virtual machines that are required to provide the total application capacity, wherein determining the needed virtual machines and needed flavors comprises: performing an iterative process in which the needed virtual machines are dynamically determined from the plurality of virtual machines based on the total application capacity, and in which the needed flavor for each of the needed virtual machines is dynamically determined taking into account the total application capacity and the available processing capacity provided on the host on which the corresponding needed virtual machine is provided.

A method for controlling an electronic apparatus includes storing a plurality of artificial intelligence models in a first memory, based on receiving a control signal for loading a first artificial intelligence model among the plurality of stored artificial intelligence models into a second memory, identifying an available memory size of the second memory, and based on a size of the first artificial intelligence model being larger than the available memory size of the second memory, obtaining a first compression artificial intelligence model by compressing the first artificial intelligence model based om the available memory size of the second memory, and loading the first compression artificial intelligence model into the second memory.

[Problem] Available resources are efficiently used even in a case in which continuous available resources cannot be secured on a cloud. [Solution] A resource reservation management apparatus 10 includes: a storage unit that stores a resource capacity and resource reservation information of a computing machine; a reservation notification unit 11 that receives, from a user terminal, reservation request information including an operating requested time period, an operating time, and a requested specification as a reservation of a master lease; a scheduling unit 12 that creates slave leases by splitting the operating time of the master lease in accordance with times corresponding to available resources indicated in the resource reservation information and sets the slave leases in the resource reservation information; a reservation management unit that detects occurrence of predetermined events including stop, shift, restart, and deletion of the created instances by referring to the resource reservation information; and an instance management unit 15 that transmits instruction information in accordance with an instance creation instruction and the detected predetermined events to the computing machine 15.

A method and system for processing media content in Moving Picture Experts Group (MPEG) Network Based Media Processing (NBMP) includes receiving, from an NBMP source, a first message including a workflow descriptor document corresponding to a workflow for processing the media content; obtaining, based on the workflow, a task having a task template; obtaining, based on the task, a function having a function template; and managing the processing of the media content by transmitting, to a media processing entity, a second message instructing the media processing entity to perform the function based on the task. The first message, the workflow descriptor document, the task template, the function template, and/or the second message may be used to signal a scheme for processing the media content.