In some embodiments, a method processes a first packet and generates a first copy of the first packet as a second packet. The method sends second copies of the first packet to a first group of multiple destinations defined by a first address. Also, the method sends the second packet to an interface with a loopback function. The interface recirculates the second packet for further processing. The second packet is processed where the second packet is assigned a destination of a second address. Then, the method sends copies of the second packet to a second group of multiple destinations defined by the second address.

In some embodiments, a method processes a first packet and generates a first copy of the first packet as a second packet. The method sends second copies of the first packet to a first group of multiple destinations defined by a first address. Also, the method sends the second packet to an interface with a loopback function. The interface recirculates the second packet for further processing. The second packet is processed where the second packet is assigned a destination of a second address. Then, the method sends copies of the second packet to a second group of multiple destinations defined by the second address.

Systems and methods are provided for transmitting events. The systems and methods include operations for: receiving data indicating a level of user engagement with a messaging application for a given user; determining that the level of user engagement is below a specified threshold; prioritizing a plurality of events, associated with the messaging application, for transmission to a client device of the given user in response to determining that the level of user engagement is below the specified threshold; and transmitting a first event of the plurality of events to the client device based on prioritizing the plurality of events.

Systems and methods are provided for transmitting events. The systems and methods include operations for: receiving data indicating a level of user engagement with a messaging application for a given user; determining that the level of user engagement is below a specified threshold; prioritizing a plurality of events, associated with the messaging application, for transmission to a client device of the given user in response to determining that the level of user engagement is below the specified threshold; and transmitting a first event of the plurality of events to the client device based on prioritizing the plurality of events.

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A method performed by a source edge enabler server for selecting a target edge application server in an edge computing system for a user equipment (UE) is provided.

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A method performed by a source edge enabler server for selecting a target edge application server in an edge computing system for a user equipment (UE) is provided.

Embodiments of a method are disclosed. The method includes performing a batch of decentralized deep learning training for a machine learning model in coordination with multiple local homogenous learners on a deep learning training compute node, and in coordination with multiple super learners on corresponding deep learning training compute nodes. The method also includes exchanging communications with the super learners in accordance with an asynchronous decentralized parallel stochastic gradient descent (ADPSGD) protocol. The communications are associated with the batch of deep learning training.

In an embodiment, a method comprises performing a first synchronization operation for a blockchain node of a plurality of blockchain nodes of a blockchain network, the first synchronization operation comprising the blockchain node receiving blockchain history data from one or more blockchain nodes of the plurality of blockchain nodes of the blockchain network and storing the blockchain history data in one or more digital data repositories; determining that the blockchain history data satisfies one or more validation criteria; creating a new blockchain node of the blockchain network, the new blockchain node configured to retrieve the blockchain history data from the one or more digital data repositories and replicate the blockchain history data for the new blockchain node, the new blockchain node existing in an unsynchronized state; performing a second synchronization operation for the new blockchain node of the blockchain network, the second synchronization operation comprising the new blockchain node receiving blockchain update data from one or more blockchain nodes of the plurality of blockchain nodes of the blockchain network resulting in converting the new blockchain node to a synchronized state.

In an embodiment, a method comprises performing a first synchronization operation for a blockchain node of a plurality of blockchain nodes of a blockchain network, the first synchronization operation comprising the blockchain node receiving blockchain history data from one or more blockchain nodes of the plurality of blockchain nodes of the blockchain network and storing the blockchain history data in one or more digital data repositories; determining that the blockchain history data satisfies one or more validation criteria; creating a new blockchain node of the blockchain network, the new blockchain node configured to retrieve the blockchain history data from the one or more digital data repositories and replicate the blockchain history data for the new blockchain node, the new blockchain node existing in an unsynchronized state; performing a second synchronization operation for the new blockchain node of the blockchain network, the second synchronization operation comprising the new blockchain node receiving blockchain update data from one or more blockchain nodes of the plurality of blockchain nodes of the blockchain network resulting in converting the new blockchain node to a synchronized state.

A processor determines the data to be replicated in the production site. The processor splits the data into a plurality of blocks. The processor determines one or more replication links. The processor determines an order of each one of the plurality of blocks to be sent over the one or more replication links, where the order of each one of the plurality of blocks to be sent over the one or more replication links is determined by a machine learning model.