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Abstract
Latency-sensitive applications such as autonomous vehicles, augmented reality, and real-time analytics require near-instantaneous data processing and decision-making. Cloud computing, while powerful and scalable, often suffers from high latency due to the physical distance between data centers and end devices. Edge computing addresses this limitation by bringing computation closer to the data source, thereby reducing response times. This paper presents a comparative analysis of edge and cloud computing paradigms, focusing on their performance for latency-sensitive applications. The study explores architectural differences, latency benchmarks, and cost-performance trade-offs, supplemented by a literature review of key studies.
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