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Evaluating data analytics architectures performance: a comparative study on the continuum
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DOI：10.1186/s13677-026-00960-z.png)
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Abstract
En 中文
Over the last few years, we have experienced a growing avalanche of information generated by devices connected to the Internet derived from the widespread acceptance of technology in society. These devices have enabled the development of IoT applications that facilitate and improve the quality of life of people in different areas such as industry or healthcare. As applications advance, their complexity increases due to the interconnection with more devices and the data they generate. This places greater demands on the architecture responsible for transferring and processing information, which can have a negative impact on performance. This implies that Quality of Service (QoS) has to be managed with precision. Therefore, it is essential to select a suitable architecture for data processing and analysis that meets QoS requirements such as response time or latency. In this paper, we present a set of parameters for evaluating and comparing analytics performance between two architecture alternatives: Cloud Computing and the emerging paradigm of the Computing continuum. These parameters influence QoS in evaluating and comparing these architectures; as validation, we provide a case study where the suitability of each architecture can be observed and detected depending on the application context. Based on the empirical analysis, we find that Cloud computing excels in environments with smaller data sets and limited devices; conversely, the Computing continuum proves superior in scenarios with larger data sets and numerous end devices.
Keywords:
Cloud computing
Computing continuum
Data analytics performance comparison
Evaluation
Journal
J
IF:
4.3
Papers:
724
Citations:
2.2K