Abstract
Most data in the real world have spatial and temporal attributes, and some other essential data attributes also have temporal and spatial variability. In the research field of cloud and edge computing, these features always have great impact on data placement and task scheduling. However, these critical spatiotemporal features have been largely ignored by existing studies. To this end, this work firstly synthesizes data popularity, geographical location distribution and other spatiotemporal features to abstract the definition of data temperature, and a temperature calculation model is proposed to reflect the spatiotemporal correlation and variation trend. Then, we put forward a multi-cloud dynamic storage strategy considering data temperature to improve service quality and reflect the value of data temperature. Experiments are performed to evaluate the proposed strategy, which can effectively reduce the total cost and improve data availability.
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Wang, P., Wei, Y., Zhang, Z. (2021). Optimizing Data Placement in Multi-cloud Environments Considering Data Temperature. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_14
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DOI: https://doi.org/10.1007/978-3-030-78612-0_14
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