Abstract
In many Internet of Things systems, it is required to process a good supply of real-time data from the physical world. An important goal when designing such systems is to maintain data temporal consistency while consuming less power. In this paper, we propose, to our knowledge, the first solution to the energy-efficient temporal consistency maintenance problem on Dynamic Voltage and Frequency Scaling (DVFS)-capable multicore platforms. We consider the problem of how to minimize the overall total power consumption on multicore, while the temporal consistency of real-time data objects can be maintained. To end this, firstly, we propose an efficient per-CPU DVFS solution, under which the transaction set can be scheduled to meet the temporal consistency requirement while resulting in significant energy savings. Next, by adopting the proposed unicore DVFS techniques on each core, we further propose new energy-efficient mapping techniques to explore energy savings for multicore platforms. Finally, extensive simulation experiments are conducted and the results demonstrate the proposed solutions outperforms existing methods in terms of energy consumption (up to \(55\%\)).
The work was partially supported by the State Key Program of National Natural Science of China under Grant No. 61332001, National Natural Science Foundation of China under Grant Nos. 61572215, 61672252, Wuhan Youth Science and Technology Plan under Grant No. 2017050304010287, and the Fundamental Research Funds for the Central Universities, HUST-2016YXMS076.
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Li, G., Zhou, C., Li, J., Guo, B. (2018). Energy-Efficient Data Temporal Consistency Maintenance for IoT Systems. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_39
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