The analysis of the models performance indicators by cloud and post-cloud computing paradigms

The work describes cloud and post-cloud computing paradigms, on the basis of which it is possible to model and build intelligent video surveillance systems. Based on the analysis of literary sources, it was concluded that performance indicators largely depend on the paradigm of the computational model, but the degree of importance of each performance indicator is different for different paradigms. Taking into account the peculiarities of different paradigms, the performance indicators characteristic of each of them are described. It has been found that the common indicators for evaluating all models developed on the basis of cloud and post-cloud computing paradigms (with varying degrees of importance) are: Throughput, Network Congestion, SLA Violation, Fault Tolerance, Statistical Analysis Measurements, The Number of Orchestration Decisions, The number of Contradictory Decisions, Time to Adaptation, Competition Ratio, Cache Hit Ratio, The number of Contradictory Actions, Oscillation Mitigation, Cost, Technique’s Overhead, Privacy. Based on the analysis of cloud and post-cloud computing simulators, it is established that the metrics of resource usage, energy, response time, and latency are those that can be used by researchers when conducting a comparative analysis of models built on the basis of the following paradigms: fog, edge, fog, and cloud computing. The practical significance of the conducted research is that, depending on the tasks that will be set before the future video surveillance system, researchers will be able to evaluate the effectiveness of using different architectures (Cloud, FOG, Mist, etc.) with the help of certain simulators. In the future, it is advisable to conduct a study of the degree of importance of common metrics for evaluating all models developed on the basis of cloud and post-cloud computing paradigms, and conduct their normalization for the possibility of conducting an adequate comparative analysis. It is also relevant in future research that it is advisable to conduct research using different types of sensor devices (surveillance cameras with different technical characteristics, unmanned aerial vehicles, etc.).