Abstract
Internet of Things is an emerging paradigm to enable easy data collection and exchange among a wide variety of devices. When the scale of Internet of Things enlarges, the cloud computing system could be applied to mine these big data generated by Internet of Things. This paper proposes a task scheduling approach for time-critical data streaming applications on heterogeneous clouds. The proposed approach takes the tasks in critical stages into consideration, and re-schedules these tasks to appropriate resources to shorten their processing time. In general, selecting the time-critical task to give more resources may remove the execution bottleneck. A small-scale cloud system including 3 servers is built for experiments. The performance of the proposed approach is evaluated by three micro-benchmarks. Preliminary experimental results demonstrate the performance improvement of the critical task scheduling approach.
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Acknowledgement
This study was sponsored by the Ministry of Science and Technology, Taiwan, R.O.C., under contract numbers: MOST 103-2218-E-007-021 and MOST 103-2221-E-142-001-MY3.
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Kuo, YH., Lee, YH., Huang, KC., Lai, KC. (2018). Critical Task Scheduling for Data Stream Computing on Heterogeneous Clouds. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2017. Lecture Notes in Electrical Engineering, vol 464. Springer, Singapore. https://doi.org/10.1007/978-981-10-7398-4_19
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DOI: https://doi.org/10.1007/978-981-10-7398-4_19
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