部分卸载移动边缘计算网络中的信息平均年龄
Mean Age of Information in Partial Offloading Mobile Edge Computing Networks
AI总结：
本文利用排队理论和随机几何推导了大规模部分卸载MEC网络中平均信息年龄的闭式表达式，证明联合优化计算卸载比率和任务生成速率可使部分卸载方案在MAoI性能上较本地和远程计算分别提升51%和61%。
摘要：
信息年龄（AoI）性能分析对于评估大规模移动边缘计算（MEC）网络中的信息新鲜度至关重要。本文提出了对大规模部分卸载MEC网络平均信息年龄（MAoI）性能的最早分析。首先，我们利用排队理论和随机几何推导并验证了MAoI的闭式表达式。基于这些表达式，我们分析了计算卸载比率（COR）和任务生成速率（TGR）对MAoI性能的影响，并比较了本地计算、远程计算和部分卸载方案下的MAoI性能。结果表明，通过联合优化COR和TGR，部分卸载方案在MAoI方面优于本地和远程计算方案，分别可提升高达51%和61%。这鼓励MEC网络采用部分卸载方案以改善MAoI性能。
英文摘要：
The age of information (AoI) performance analysis is essential for evaluating the information freshness in the large-scale mobile edge computing (MEC) networks. This work proposes the earliest analysis of the mean AoI (MAoI) performance of large-scale partial offloading MEC networks. Firstly, we derive and validate the closed-form expressions of MAoI by using queueing theory and stochastic geometry. Based on these expressions, we analyse the effects of computing offloading ratio (COR) and task generation rate (TGR) on the MAoI performance and compare the MAoI performance under the local computing, remote computing, and partial offloading schemes. The results show that by jointly optimising the COR and TGR, the partial offloading scheme outperforms the local and remote computing schemes in terms of the MAoI, which can be improved by up to 51% and 61%, respectively. This encourages the MEC networks to adopt the partial offloading scheme to improve the MAoI performance.
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