快速内存:MEC中任务卸载和资源分配的快速ai辅助解决方案

Tongyu Song, Wenyu Hu, Xuebin Tan, Jing Ren, Sheng Wang, Shizhong Xu
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引用次数: 1

摘要

MEC作为5G网络中的关键概念之一,通过将计算和存储容量广泛部署到网络边缘的基站,可以支持延迟敏感和计算密集型业务。由于这些业务对时延比较敏感,需要在短时间内解决任务卸载和资源分配的联合优化问题。在本文中,我们提出了一种用于MEC任务卸载和资源分配的快速人工智能辅助解决方案(Fast - ram),它可以利用深度神经网络直接解决联合优化问题。FAST-RAM可以在毫秒内生成卸载策略和资源分配方案。同时,我们的方案在不同的网络环境下具有接近最优的性能和足够的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FAST-RAM: A Fast AI-assistant Solution for Task Offloading and Resource Allocation in MEC
As one of the key concepts in the 5G network, MEC can support the latency-sensitive and compute-intensive services by widely deploying computing and storage capacity to the base stations at the network edge. Because these services are sensitive to latency, the joint optimization problem of task offloading and resource allocation needs to be solved in a short time. In this paper, we propose a Fast AI-assistant Solution for Task Offloading and Resource Allocation in MEC (FAST-RAM), which can directly solve the joint optimization problem leveraging a deep neural network. FAST-RAM can produce the offloading policy and resource allocation scheme in milliseconds. Meantime, our solution has near-optimal performance and sufficient feasibility under different network environments.
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