Comparative study evaluates performance of cloud, edge, and on-device computing for real-time image processing, highlighting latency and scalability trade-offs.
With the growing demand for real-time image and video processing, selecting an efficient computing architecture has become increasingly important. This study conducts a comparative analysis of three paradigms—cloud computing (CC), edge computing (EC), and on-device edge computing (ODEC)—to determine the most suitable method for real-time applications. By evaluating their performance across varying data volumes and network conditions, we identify the trade-offs in latency, scalability, and responsiveness. The findings reveal that cloud computing suffers from increased latency due to transmission bottlenecks, while edge and on-device computing significantly reduce latency through decentralized processing. Among the three, ODEC demonstrates the most consistent performance, particularly in environments requiring large-scale data handling and minimal network dependence. These results suggest that on-device AI offers a promising direction for future real-time systems by addressing the limitations of traditional architectures.
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Choi et al. (2025) studied this question.
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