面向5G低时延的拥塞感知节能MEC模型

Alshimaa H. Ismail, Tarek Abed Soliman, Gerges M. Salama, Nirmeen A. El-Bahnasawy, H. Hamed
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引用次数: 4

摘要

低延迟、高能效、大规模连接和高数据速率是学术界和工业界对5g的主要追求。最近的研究大多致力于利用边缘计算概念来解决延迟问题,而其他研究则致力于通过最小化云中的能耗来实现绿色云。多接入边缘计算(MEC)在过去十年中出现,作为5g网络中确定的毫秒级延迟的有前途的解决方案。在本文中,我们提出了一种基于主动队列管理的绿色云模型(AGCM),这是一种节能的云模型,通过缓解云中的拥塞来减少云和移动设备的能源浪费和延迟。我们还提出了与MEC的交接策略,以获得潜在收益,并调查其对云计算基本约束的影响。我们通过仿真测试了我们提出的模型,显示了延迟、能耗和吞吐量方面的显著改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Congestion-Aware and Energy-Efficient MEC Model with Low Latency for 5G
Low latency, energy efficiency, massive connectivity, and high data rate are the main characteristics that academia and industry are seeking for 5 G. The majority of the recent studies were dedicated to solving the delay issue by leveraging the edge computing concept while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Multi-access edge computing (MEC) emerged in the past decade as a promising solution for the determined millisecond-scale latency in 5 G networks. In this paper, we present an active queue management-based green cloud model (AGCM), an energy-efficient cloud model to reduce energy waste at both the cloud and mobile devices and the latency by relieving the congestion in the cloud. We also propose a hand-off strategy with MEC to acquire the potential gain and investigate its impact on the cloud's fundamental constraints. We tested our proposed models with simulation showing considerable improvement for the latency, energy consumption, and throughput.
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