Cox Communications, Inc.

Joshua Paul Bernard Sommer

Yousef Wasef Nijim

Anthony Joseph Insinga

David Hicks

Imran Lakhani

Gregory Devon Thomas

Michael Latino

Aspects of the present disclosure provide for adaptively allocating processing operations according to an edge-only processing mode, a hybrid edge-cloud processing mode, and/or cloud-only processing mode, but are not so limited. In one aspect, the disclosure describes adaptively managing processing of applications or application modules by using machine learning in part to direct how the processing operations are to be performed whether using edge-only resources, hybrid edge-cloud resources, and/or cloud-only resources. Other aspects are described in detail herein.

Adaptive edge processing

Systems and methods are described for enabling quantum computing at an edge node of a network. For example, a machine learning component residing on each of a plurality of edge nodes of the network may be implemented to distribute application processing by network location and processing type, including distribution among classical processing at a central cloud, classical processing at an edge node, and quantum processing at a quantum edge node including a quantum computing device. By distributing certain applications, such as latency-sensitive applications of a higher order of complexity, to an edge node, and particularly a quantum edge node, latency may be reduced and complex application code may be processed more quicky using quantum computations. For applications to be processed using quantum processing, the machine learning component may further identify qubits for the quantum processing and define containers based on the qubits for deployment by the quantum computing device.

Edge quantum computing

Systems, methods, and computer readable media for power outage determination. A device may determine an indication that an outage has occurred on a cable network based on a status of a network device within the cable network, wherein the network device receives power from a power network. The device may determine location information for at least a portion of a plurality of network devices, including the network device, within the cable network. The device may determine a cable topology of the cable network at an area that includes a location that corresponds to the network device. The device may determine a power topology of the power network at the area. The device may analyze the cable topology and the power topology at the area. The device may identify one or more potential failure locations on the power network.

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Atlanta, GA, United States of America

Joshua Paul Bernard Sommer