###### Abstract  
In this paper, we propose models that significantly expand the scope of practical applications, namely, queueing systems with various nodes for processing heterogeneous data that require arbitrary resource capacities for their service. When a customer arrives in the system, the customer typeis randomly selected according to a set of probabilities. Then the customer goes to the server of the corresponding device type, where its service is performed during a random time period with a distribution function depending on the type of customer. Moreover, each customer requires a random amount of resources, of which the distribution function also depends on the customer type, but is independent of its service time. The aim of this research was to develop a heterogeneous queueing resource system with an unlimited number of servers and an arrival process in the form of a Markov-modulated Poisson process or stationary renewal process, and with requests for a random number of heterogeneous resources. We have performed analysis under conditions of growing intensity of the arrival process. Here we formulate the theorems and prove that under high-load conditions, the joint asymptotic probability distribution of the n-dimensional process of the total amounts of the occupied resources in the system is a multidimensional Gaussian distribution with parameters that are dependent on the type of arrival process. As a result of numerical and simulation experiments, conclusions are drawn on the limits of the applicability of the obtained asymptotic results. The dependence of the convergence of experimental results on the type of distribution of the system parameters (including the distributions of the service time and of the customer capacity) are also  
segments, leading to the complete failure of its operation. Identifying and investigating the effect of aspects of behavior on the quality of the network allows one to plan and prepare a network in advance in such a way as to reduce the loss of calls. The greatest area of interest, from a practical perspective, is the consideration of extreme working conditions, for example, overload conditions, when the state of the network differs from the normal (planned) state. The theoretical and the applied foundations of research in the field of data transmission and processing in info-communication systems are based mainly on probability theory, random process theory, queueing theory, and teletraffic theory. The initial research in the field of computer networks, presented in the works of [PERSON], [PERSON], [PERSON], and [PERSON], as well as in the works of [PERSON]. [PERSON], was based on simplified models of information systems with the Poisson arrival process and an exponential distribution of the packets' service times. The monographs of the Russian and foreign scientists [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] give a detailed overview of modern applications of the queueing models in the field of telecommunications, modern computer networks, and information systems [1; 2; 3; 4]. The analysis of modern communication systems is complicated by the fact that requests for data transfers are not homogeneous. The servicing of such applications may differ both in terms of the time of service and the volume of additional resources provided. To reduce data transmission losses, it is necessary to take into account the random amount of requested resources and service characteristics. Identifying these aspects and analyzing their impact on systems allows networks to be optimized to reduce losses. In classical queueing system (QSs), the devices and waiting places (buffer, orbit) play the role of discrete resources which are necessary for maintenance.