Abstract
Recent advances in communication and sensor network technologies make Industrial Internet of Things (IIoT) a major driving force for future industry. Various devices in wide industry fields generate diverse computation tasks with their distinct service requirements. Note that the distribution of such tasks has essential intrinsic patterns and varies according to factors like region, season and time. Different from previous efforts to develop algorithms in specific scenarios for reducing task execution latency without considering the task generation patterns of IIoT, we propose a DRL-based Task Offloading algorithm (DRLTO) to learn such generation patterns and maximize the task completion rate. A SDN-enabled multi-layer heterogeneous computing framework is also introduced to efficiently assign tasks according to the obtained knowledges towards their features. Extensive experiments validate that our algorithm can not only significantly improve the average task completion rate, but also achieve near-optimal results in lots of IIoT scenarios.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, J., Cao, Y., Liu, J., Zhang, Y. (2019). Deep Reinforcement Learning Based Task Offloading in SDN-Enabled Industrial Internet of Things. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_37
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DOI: https://doi.org/10.1007/978-3-030-22971-9_37
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