Abstract
As an emerging and promising technique, mobile edge computing (MEC) can significantly speed up the execution of tasks and save device energy by offloading the computation-intensive tasks from resource-constrained mobiles to the MEC servers. Besides, technological advances have promoted the emergence of novel applications task with a model framework. These model frameworks are indispensable and reusable: if the model task wants to execute on MEC, both the model and data need to offload; the model can store in the cache for the later execution of the same type of tasks. What’s more, consider the limited capacity cache, it is a great challenge to replace the model dynamically to meet long-time requirements. In this paper, we jointly consider radio frequency (RF) energy capturing, computation offloading, and task scheduling in a multi-user cache-assisted MEC system. We formulate a replace algorithm and a global replacement scheduling algorithm (GRSA) to solve the mixed discrete-continuous optimization problem, which minimizes the total execution time subject to energy consumption and channel conflict. The simulation results show that our algorithm can effectively reduce computation latency.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant No. 61672465 and No. 61772472) and Zhejiang Provincial Natural Science Foundation of China (LY15F020027).
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Tian, X., Chen, J., Zhao, Z., Meng, H. (2021). Dynamic Edge Computation Offloading and Scheduling for Model Task in Energy Capture Network. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_34
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DOI: https://doi.org/10.1007/978-3-030-85928-2_34
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