Performance Analysis Of Cloud-Based Architectures For Real-Time Processing Of Biomedical And Sensor Data

13 Jan
Posted on Author

Authors: Ronav Shetty

Abstract: The rapid expansion of the Internet of Medical Things (IoMT) has necessitated a transition from localized medical monitoring to high-throughput, cloud-integrated analytical frameworks. However, the inherent "best-effort" nature of traditional cloud computing often conflicts with the stringent requirements of real-time biomedical applications, where processing delays can jeopardize patient safety. This review article provides a comprehensive performance analysis of various cloud-based architectures—centralized, edge-fog, and serverless—tailored for the continuous processing of high-frequency sensor data such as ECG, EEG, and PPG signals. We evaluate these architectures against a rigorous set of performance metrics, including end-to-end latency, jitter, packet loss ratio, and signal-to-noise ratio (SNR) preservation. The analysis highlights the critical role of the edge-fog-cloud hierarchy in mitigating network congestion and reducing the computational overhead of data security and interoperability protocols (e.g., HL7 FHIR). We explore specialized optimization strategies, such as lightweight virtualization using Docker, hardware acceleration through cloud-based GPUs, and adaptive task-offloading policies. Furthermore, we examine the performance impact of emerging communication standards like 5G URLLC (Ultra-Reliable Low-Latency Communications) and their potential to enable tactile internet applications like remote robotic surgery. By synthesizing empirical benchmarking data and qualitative case studies from smart ICU and telecardiology environments, this review establishes a set of design best practices for engineering "guaranteed-performance" clinical infrastructures. The findings underscore that the future of biomedical data processing lies in a decentralized, autonomous architecture capable of maintaining sub-second responsiveness within a highly scalable and secure global network.

DOI: https://doi.org/10.5281/zenodo.18228279