Abstract
Currently, Mobile Edge Computing (MEC) is widely used in different smart application scenarios such as smart health, smart traffic and smart home. However, smart end devices are usually constrained in battery and computing power, and hence how to optimize the energy consumption of end devices with intelligent task offloading and scheduling strategies under constraints such as deadlines is a critical yet challenging topic. Meanwhile, most existing studies do not consider the mobility of end devices during task execution but in reality end devices may need to be constantly moving in a MEC environment. In this paper, motivated by a patient health monitoring scenario, we propose a Mobility-Aware Workflow Offloading and Scheduling Strategy (MAWOSS) for MEC which provides a holistic approach that covers the workflow task offloading strategy, the workflow task scheduling algorithm and the workflow task migration strategy. Comprehensive experimental results show that compared with others, MAWOSS is able to achieve the optimal fitness with lower energy consumption and smaller workflow makespan under the deadlines.
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Acknowledgement
This work is the partially supported by the Humanities and Social Sciences of MOE Project No. 16YJCZH048, the National Natural Science Foundation of China Project No. 61972001, the Key Natural Science Foundation of Education Bureau of Anhui Province Project KJ2016A024, and the Nature Science Foundation of Hubei Province Project 2019CFB172.
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Xu, J. et al. (2020). Mobility-Aware Workflow Offloading and Scheduling Strategy for Mobile Edge Computing. In: Wen, S., Zomaya, A., Yang, L.T. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_17
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DOI: https://doi.org/10.1007/978-3-030-38961-1_17
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