Abstract
Efficient scheduling of data elements and computation units can help to reduce the latency of processing big IoT stream data. In many cases, moving computation turns out to be more cost-effective than moving data. However, deploying computations from cloud-end to edge devices may face two difficult situations. First, edge devices usually have limited computing power as well as storage capability, and we need to selectively schedule computation tasks. Secondly, the overhead of stream data processing varies over time and makes it necessary to adaptively adjust service deployment at runtime. In this work, we propose a heuristics approach to adaptively deploying services at runtime. The effectiveness of the proposed approach is demonstrated by examining real cases of China’s State Power Grid.
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Notes
- 1.
DAT is the Data Acquisition Terminal are deployed on the electric transmission lines across the whole country. DAT is configured with a 32-bit embedded microprocessor, DSP chip, embedded Linux operation system and embedded JDK, etc.
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Acknowledgement
This work is supported by “National Natural Science Foundation of China (No:61672042), Models and Methodology of Data Services Facilitating Dynamic Correlation of Big Stream Data”, “National Natural Science Foundation of China (No.61702014)”, and “Beijing Natural Science Foundation (No. 4192020)”.
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Zhang, S., Liu, C., Wang, J., Yang, Z., Han, Y., Li, X. (2019). Latency-Aware Deployment of IoT Services in a Cloud-Edge Environment. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_17
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