A method and apparatus for differentially optimizing a quality of service (QoS) includes: establishing a system model of a multi-task unloading framework; acquiring a mode for users executing a computation task, executing, according to the mode for users executing the computation task, the system model of the multi-task unloading framework; and optimizing a quality of service (QoS) on the basis of a multi-objective optimization method for a multi-agent deep reinforcement learning. According to the present invention, an unloading policy is calculated on the basis of a multi-user differentiated QoS of a multi-agent deep reinforcement learning, and with the differentiated QoS requirements among different users in a system being considered, a global unloading decision is performed according to a task performance requirement and a network resource state, and differentiated performance optimization is performed on different user requirements, thereby effectively improving a system resource utilization rate and a user service quality.

Systems and methods for presence-based application invocation are disclosed. For example, presence of a personal device and/or user associated with a personal device is detected at a primary device. When account data associated with the personal device and the primary device correspond, checks are performed to determine if automatic invocation of an application associated with the personal device should be performed. Those checks include determining whether one or more conditions are satisfied, determining current usage of the application and/or the primary device indicates desirability to automatically invoke the application, and/or whether the application has been enabled for use on the primary device.

A method for executing a task by a scheduling device, belonging to the technical field of electronics. The method includes: acquiring a target algorithm corresponding to a target task to be executed; acquiring an execution environment condition for a target algorithm, and current execution environment information of various execution devices; in the execution devices, determining a target execution device of which the execution environment information satisfies the execution environment condition; and sending a control message for executing the target task to the target execution device.

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.

An apparatus for use in a communication system sets, in a search request serving to retrieve, from a storage entity, records that match filters, an indication which indicates if, for one or more matching records of the records that match the filters, a content of the one or more matching records is to be received in a response to the search request along with references to the records that match the filters, and transmits the search request including the indication.

A system identifies strongly connected components of a circuit design. The system receiving a circuit design represented as a graph including a set of vertices and a set of edges. The system marks each vertex of the set of vertices void. The system executes multiple threads, where each thread performs following steps concurrently. The thread selects a vertex from the set of vertices with void state. The thread performs a depth first search starting from the selected vertex. The thread marks a vertex as processed once the depth first search started from that vertex is completed. The depth first search skips vertices marked as processed. The thread determines a candidate SCC based on the nodes traversed by the depth first search. Once a set of candidate SCCs is determined, the system eliminates some of the candidate SCCs and stores the remaining candidate SCCs as SCCs of the graph.

Metadata includes runtime dependencies for services that form an application. Each runtime dependency includes one or more of the one or more services, unless it has no runtime dependencies. A first of the services is selected, based on the metadata, that does not have runtime dependencies or those runtime dependencies are operational. The selected service is started. Other embodiments are described and claimed.

A thread may be de-activated (terminated or hibernated) or activated (e.g., re-activated or create anew if allowed) on a processing node, in response to which it may be desirable to redistribute the I/O jobs among the now active threads. Redistributing the I/O jobs may involve re-associating one or more active threads resulting from the activation or de-activation with one or more of the bin groups and/or re-assigning one or more job bins with one or more bin groups, for example, as will now be described. The bin groups may be re-associated with remaining active threads. I/O jobs may be redistributed among the active threads re-assigning job bins to bin groups. One or more queued I/O jobs may be moved to the thread that now owns the I/O job.

[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.