Apparatus, comprising means configured for: - receiving, from an access node associated with a radio cell and comprising computing resources for executing radio resources management algorithms, data specifying cell level parameters and computing resources availability, - selecting, based on the cell level parameters and computing resources availability, a set of radio resources management algorithms, - sending, to the access node, data specifying the selected set of radio resources management algorithms, wherein selecting the set of radio resources management algorithms is based on predicted performance metrics and a comparison between computing resources needs of the selected set of radio resources management algorithms and computing resources availability, wherein selecting the set of radio resources management algorithms comprises: - determining the performance metrics for a plurality of the sets of radio resources management algorithms, - determining computing resources needs for said plurality of the sets, - selecting one of the sets which has computing resources needs lower than available computing resources, based on the performance metrics, wherein determining the performance metrics is based on a first machine learning model configured to predict the performance metrics, and determining computing resources needs is based on a second machine learning model configured to predict computing resources needs, wherein said means are further configured for: - receiving, from the access node, data specifying cell performance metrics and computing resources consumption for respective radio resources management algorithms, - updating said first machine learning model and said second machine learning model based on the received performance metrics and computing resources consumption. - receiving, from an access node associated with a radio cell and comprising computing resources for executing radio resources management algorithms, data specifying cell level parameters and computing resources availability, - selecting, based on the cell level parameters and computing resources availability, a set of radio resources management algorithms, - sending, to the access node, data specifying the selected set of radio resources management algorithms, wherein selecting the set of radio resources management algorithms is based on predicted performance metrics and a comparison between computing resources needs of the selected set of radio resources management algorithms and computing resources availability, wherein selecting the set of radio resources management algorithms comprises: - determining the performance metrics for a plurality of the sets of radio resources management algorithms, - determining computing resources needs for said plurality of the sets, - selecting one of the sets which has computing resources needs lower than available computing resources, based on the performance metrics, - determining the performance metrics for a plurality of the sets of radio resources management algorithms, - determining computing resources needs for said plurality of the sets, - selecting one of the sets which has computing resources needs lower than available computing resources, based on the performance metrics, wherein determining the performance metrics is based on a first machine learning model configured to predict the performance metrics, and determining computing resources needs is based on a second machine learning model configured to predict computing resources needs, wherein said means are further configured for: - receiving, from the access node, data specifying cell performance metrics and computing resources consumption for respective radio resources management algorithms, - updating said first machine learning model and said second machine learning model based on the received performance metrics and computing resources consumption. - receiving, from the access node, data specifying cell performance metrics and computing resources consumption for respective radio resources management algorithms, - updating said first machine learning model and said second machine learning model based on the received performance metrics and computing resources consumption.