Abstract <p>In accordance with the technical requirements, distributed computing systems functioning is determined by where a network service is placed and on what equipment it runs. Network users are provided with a set of information services with a given level of quality and reliability: (1) access to information ‘‘at any time, in any place,’’ i.e., to any participant in the management process, provided they have access rights when the need arises and regardless of their location; (2) information interaction between automated systems; (3) timely monitoring and analysis of data from various types of sources; (4) transition from a centralized ‘‘data loading with cleaning–analysis–distribution’’ scheme to a ‘‘distributed data placement and preprocessing, and (if necessary) subsequent loading, analysis and distribution’’ scheme. The key condition for providing the above services with required probabilistic and temporal parameters is predicting the periods of executing network services on various equipment and virtualization platforms. This work analyzes ways of predicting the temporal performance of network services. Our approaches are based on data from their operation in the current infrastructure, and also allow for the current and predicted state of hardware and software resources. Among the considered solutions are machine learning models that include random forest,multilayer perceptrons, and convolutional neural networks.</p>