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Introduction . At present, the concepts of fog and edge computing are used in a wide range of applications of various kinds. One of the key problems in the organization of computing in groups of mobile devices that make up the edge/fog layer is the mission assurance based on battery power availability. In this context, a lot of developments aimed at energy saving of device systems have been presented to date. However, one important aspect remains beyond the consideration of the problem of resource saving, namely, the issue of saving the residual resource of a computing device. The aim of this research is to formalize the workload distribution problem as two-criteria optimization problem, and to develop the basic solution technique. Materials and Methods . Within the framework of this article, an approach to resource saving is proposed. It is based on the evaluation of two device criteria: battery life and residual resource of a computing device. The residual resource of a computing device can be estimated using the probability of failure-free operation of the device, or as the reciprocal of the failure rate, taking into account that the exponential law of failure distribution is used in the simulation. From this, a model of the problem of two-criteria optimization is formulated, taking into account the dynamics of the network topology in the process of performing a user mission. The topology dynamics is reflected in the model as a sequence of topologies, each of which corresponds to a certain period of time of the system operation. Results . На основе предложенной модели задачи двухкритериальной оптимизации предложен метод экономии ресурсов в граничном и туманном слоях сети. Он отражал специфику динамических слоев сети, а также учитывал важность критериев оценки потребления ресурсов устройства. Проведен эксперимент по оценке влияния способа распределения задач по сетевому кластеру на вероятность безотказной работы устройств и средний остаточный ресурс. Обсуждение и выводы . Проведенный эксперимент продемонстрировал целесообразность использования разработанного метода, так как распределение задач по исполняющим устройствам оказывало существенное влияние (до 25 % по результатам эксперимента) на средний остаточный ресурс вычислительного устройства.