Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a plurality of performance indicators comprising device performance indicators for a plurality of communication devices on a cellular network and network performance indicators for the cellular network. The method also includes obtaining historical data regarding the plurality of performance indicators for each of a series of time points during a past time period; the historical data for each of the plurality of performance indicators form an array of values for that performance indicator. The method further includes generating from each array a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period; the set of inputs comprises quantiles of the array, and the algorithm comprises a machine learning algorithm. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a plurality of performance indicators comprising device performance indicators for a plurality of communication devices on a cellular network and network performance indicators for the cellular network. The method also includes obtaining historical data regarding the plurality of performance indicators for each of a series of time points during a past time period; the historical data for each of the plurality of performance indicators form an array of values for that performance indicator. The method further includes generating from each array a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period; the set of inputs comprises quantiles of the array, and the algorithm comprises a machine learning algorithm. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a plurality of performance indicators comprising device performance indicators for a plurality of communication devices on a cellular network and network performance indicators for the cellular network. The method also includes obtaining historical data regarding the plurality of performance indicators for each of a series of time points during a past time period; the historical data for each of the plurality of performance indicators form an array of values for that performance indicator. The method further includes generating from each array a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period; the set of inputs comprises quantiles of the array, and the algorithm comprises a machine learning algorithm. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a plurality of performance indicators comprising device performance indicators for a plurality of communication devices on a cellular network and network performance indicators for the cellular network. The method also includes obtaining historical data regarding the plurality of performance indicators for each of a series of time points during a past time period; the historical data for each of the plurality of performance indicators form an array of values for that performance indicator. The method further includes generating from each array a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period; the set of inputs comprises quantiles of the array, and the algorithm comprises a machine learning algorithm. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a plurality of performance indicators comprising device performance indicators for a plurality of communication devices on a cellular network and network performance indicators for the cellular network. The method also includes obtaining historical data regarding the plurality of performance indicators for each of a series of time points during a past time period; the historical data for each of the plurality of performance indicators form an array of values for that performance indicator. The method further includes generating from each array a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period; the set of inputs comprises quantiles of the array, and the algorithm comprises a machine learning algorithm. Other embodiments are disclosed.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.

Abstract: A method, computer-readable storage device and apparatus for routing traffic in a reconfigurable optical add-drop multiplexer layer of a dense wavelength division multiplexing network are disclosed. For example, the method determines the reconfigurable optical add-drop multiplexer layer has asymmetric traffic, and routes the asymmetric traffic in the reconfigurable optical add-drop multiplexer layer over a plurality of asymmetrical optical connections, wherein the plurality of asymmetrical optical connections is provided with only uni-directional equipment in the reconfigurable optical add-drop multiplexer layer.