Journal
Scientific and Technical Journal of Information Technologies, Mechanics and Optics
UDK519.67
Issue:2 (166)
Generating spatiotemporal network load series in multi-access edge computing tasks using open data
Annotation
Research into decision-making systems in multi-access edge computing systems is often based on an abstract representation of a communication network without network load profiles. The aim of this work was to develop tools for generating spatio-temporal network load data depending on the communication network architecture. In our work, we used stochastic geometry methods and statistical data to form a profile of possible load. To evaluate the performance of stochastic geometry methods, we developed a tool for generating and validating spatio-temporal series with pattern search from the OpenCellID open database of cell towers. During the work, an analysis of literature and public datasets on the location and load of cell towers was conducted. Based on the analysis, it was concluded that the data quality was low for the purposes of training decision-making systems for the placement of computing services in geographically distributed data processing nodes. A tool was also developed to generate and validate spatio-temporal series with pattern search from the OpenCellID open database of cell towers. A comparative analysis of the basic and calibrated Hard-Core Poisson Process algorithms showed significant differences in the characteristics of the generated distributions. For St. Petersburg, the calibrated model provided a 99-fold increase in station density and a 52-fold reduction in inter-station distances with an effective coverage area of 0.04 km2. In the case of Novosibirsk, similar trends were observed with less intensity: a 12.5-fold increase in density and a 21-fold reduction in distances with a coverage area of 0.32 km2. The use of spatio-temporal series obtained with the help of the developed generation tools will improve the quality of training decision-making systems for the placement of computing services through pre-training on data correlated with the actual location of cell towers. In addition, the generation tool allows you to specify the coordinates of the area of the proposed communication network which can also affect the distribution patterns of towers and which in turn will allow you to generate more accurate spatio-temporal series.
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