Bypass network traffic records are generated for a web application. Sufficient statistics of network optimization parameters are calculated for network performance categories. The bypass network traffic records are partitioned for the network performance categories into network traffic buckets. Sufficient statistics and the network traffic buckets are used to generate network quality mappings. The network quality mappings are used as training instances to train a machine learner for generating network optimization policies to be implemented by user devices.

A digital data communications network that supports efficient, scalable routing of data and use of network resources by combining a recursive division of the network into hierarchical sub-networks with repeating parameterized general purpose link communication protocols and an addressing methodology that reflects the physical structure of the underlying network hardware. The sub-division of the network enhances security by reducing the amount of the network visible to an attack and by insulating the network hardware itself from attack. The fixed bandwidth range at each sub-network level allows quality of service to be assured and controlled. The routing of data is aided by a topological addressing scheme that allows data packets to be forwarded towards their destination based on only local knowledge of the network structure, with automatic support for mobility and multicasting. The repeating structures in the network greatly simplify network management and reduce the effort to engineer new network capabilities.

An improved autonegotiation approach includes determining that a negotiated rate between a first network device and a second network device exceeds data transfer capacity over a network path downstream of the second network device. In response, a configuration message is generated and transmitted to the first network device. When received by the first network device, the configuration message causes the first network device to limit data transfer between the first network device and the second network device to no more than the downstream data transfer capacity.

In one embodiment, a device includes an interface to send and receive packets of network flows, and processing circuitry to track a connection status of each of the network flows, selectively assign some network flows of the network flows having a non-terminated connection status to a flow aging process based on a statistical model of connection termination, operate the flow aging process to identify idle network flows of the some network flows, and release resources associated with the idle network flows.

In one embodiment, a device includes an interface to send and receive packets of network flows, and processing circuitry to track a connection status of each of the network flows, selectively assign some network flows of the network flows having a non-terminated connection status to a flow aging process based on a statistical model of connection termination, operate the flow aging process to identify idle network flows of the some network flows, and release resources associated with the idle network flows.

Bypass network traffic records are generated for a web application. Sufficient statistics of network optimization parameters are calculated for network performance categories. The bypass network traffic records are partitioned for the network performance categories into network traffic buckets. Sufficient statistics and the network traffic buckets are used to generate network quality mappings. The network quality mappings are used as training instances to train a machine learner for generating network optimization policies to be implemented by user devices.

Bypass network traffic records are generated for a web application. Sufficient statistics of network optimization parameters are calculated for network performance categories. The bypass network traffic records are partitioned for the network performance categories into network traffic buckets. Sufficient statistics and the network traffic buckets are used to generate network quality mappings. The network quality mappings are used as training instances to train a machine learner for generating network optimization policies to be implemented by user devices.

Systems and methods for maximizing resource credits across cloud infrastructure are disclosed herein. An example apparatus comprises: at least one memory; instructions; and processor circuitry to execute the instructions to: transmit a request for information associated with a workload executing on a cloud resource to an application programming interface associated with the cloud resource; identify the cloud resource as underutilized based on a change in an infrastructure usage pattern associated with the workload; compute an expected demand for the cloud resource based on a report of activity associated with the workload; and reconfigure the cloud resource to reduce a monetary cost associated with the workload.

A time synchronization system includes at least one time server and a plurality of time clients connected to each other via a network. The time client comprises: a communication unit configured to obtain time information of the time server by transmitting/receiving messages to/from the time server; and a time count control unit configured to synchronize time information of an internal timepiece with time information of the time server. The time count control unit controls transmission of the messages by adjusting transmission intervals of the messages to irregular intervals.

A system for question answering (QA) documents data ingestion decides to ingest the documents data through a first plurality of sub-pipelines including a first sub-pipeline having a first set of engines and a second sub-pipeline having a second set of engines being independent from the first set of engines. The system determines a subset of the documents data and decides to ingest the subset through a second plurality of sub-pipelines including a third sub-pipeline having a third set of engines and a fourth sub-pipeline having a fourth set of engines being independent from the third set of engines. A set of engines of the second plurality of sub-pipelines and a set of engines of the first plurality of sub-pipelines are in a common class. The system selects output data from the second plurality of sub-pipelines over corresponding output data from the first plurality of sub-pipelines and generates a knowledge base.