Laso, S., Berrocal, J., Fernandez, P., Garcia, J. M., Garcia-Alonso, J., Murillo, J. M., Ruiz-Cortes, A., & Dustdar, S. (2022). Elastic Data Analytics for the Cloud-to-Things Continuum. IEEE Internet Computing, 26(6), 42–49. https://doi.org/10.1109/MIC.2021.3138153
Cloud-to-Things Continuum; Elasticity Analytics; Data Analytics
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Abstract:
The massive deployment of Internet-connected devices has led to an increase in the collection of data that are then used by companies to improve their decision-making processes. This growing trend demands more and more cloud and communications infrastructure. The limited resources, the need for sharing them, and the fact that many consumers are interested in the same data, call for an efficient management of the available resources. The cloud-to-things continuum can be used to execute different analytics closer to the data source so that infrastructure consumption and data circulation can be optimized. In this article, different dimensions for achieving elastic analytics and a framework for dynamically modifying their behavior are proposed.
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Project (external):
MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU/PRTR" MCIU/AEI/FEDER, UE MCIU/AEI/FEDER, UE Interreg V-A Espana-Portugal 2014-2020 Department of Economy, Science and Digital Agenda of the Government of Extremadura Department of Economy, Science and Digital Agenda of the Government of Extremadura Junta de Andalucía Junta de Andalucía European Regional Development Fund RCIS Network
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Project ID:
Grant DIN2020-011586 Project RTI2018-094591-B-I00 Project RTI2018- 101204-B-C21 Project 0499_4IE_ PLUS_4_E Grant GR18112 Grant IB18030 Project APOLO (US-1264651) Project EKIPMENTPLUS (P18-FR-2895) Grant TIN2016-81978-REDT