Method for operating a network and a corresponding network

A method for operating a network, wherein an occupation level of at least one switch queue of at least one network switch is estimated, comprising the following steps:

• providing or collecting data regarding an association between at least one path delay and a corresponding switch queue occupation level of the at least one switch queue;
• feeding the data to a one or more machine learning models associated with the at least one switch queue;
• training the one or more machine learning models on a basis of the at least one path delay or the data to predict a switch queue occupation level of the at least one switch queue;
• and
• using information resulting from the one or more machine learning models or using the predicted switch queue occupation level for making a real-time traffic steering decision for load balancing between network paths, wherein the one or more machine learning models are disposed at an assigned candidate executor host in the network, wherein the one or more machine learning models are binarized or quantized to transform weights and activations of the one or more machine learning models from real numbers to numbers in a {0, 1} set.

A system comprising one or more hardware processors which, alone or in combination, are configured to provide for execution of a method comprising:

• providing or collecting data regarding an association between at least one path delay and a corresponding switch queue occupation level of the at least one switch queue, wherein the data includes path delays and/or switch queue occupations or switch queue occupation levels that are quantized by reducing a precision according to a statistical distribution;
• feeding the data to a machine learning model associated with the at least one switch queue;
• training the machine learning model on a basis of the at least one path delay or the data to predict a switch queue occupation level of the at least one switch queue;
• and
• using information resulting from the trained machine learning model or using the predicted switch queue occupation level for making a real-time traffic steering decision for load balancing between network paths, wherein the trained machine learning model is disposed at an assigned candidate executor host in the network.