Distributed coordination between concurrent ML functions

A method of a machine learning function of a fully automated network system based on an open radio access network (ORAN) domain, comprising:

• one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to perform:
• receiving, from a communications network, an update information on an updated value of a parameter related to a resource of interest of a network function of the fully automated network system, and an identifier of a managing function of the fully automated network system responsible for the updating of the value of the parameter;
• defining a favorable range of values of the parameter based on the update information and a history of previous values of the parameter, wherein the history comprises, for each of the previous values of the parameter, the identifier of a respective managing function responsible for updating the value of the parameter to the respective previous value;
• calculating a new value of the parameter by optimizing a utility function and taking the favorable range as a constraint for the new value;
• checking whether updating the value of the parameter of the network function due to the new value is required;
• inhibiting the updating the value of the parameter of the network function by the new value in response to checking that the updating the value of the parameter of the network function due to the new value is not required;
• and
• subscribing to the update information;
• wherein the update information is received by receiving a notification in response to the subscribing, and
• wherein the notification comprises the update information.