论文标题
空中辅助多访问边缘计算对6G的设计和优化
Design and Optimization of Aerial-Aided Multi-Access Edge Computing towards 6G
论文作者
论文摘要
网络覆盖中的普遍存在是5G的主要特征之一,预计将扩展到6G中的计算域。为了在通信和计算中提供这种普遍存在的整体方法,可以预见卫星,空中和地面网络的整合。特别是,诸如机上娱乐和连接服务(IFEC)和支持SDN的卫星等应用程序的增加数量使网络管理更具挑战性。此外,由于严格的服务质量(QoS)要求,对于这些应用程序而言,Edge Computing Edge Computing Incerming的收益非常重要。在这里,可以通过将航空网络的组件(如飞机)作为潜在的多访问边缘计算(MEC)节点来提高网络性能。因此,我们提出了一个空中辅助的多访问边缘计算(AA-MEC)体系结构,该体系结构为天空中的计算资源和基于Internet的服务提供了一个框架。此外,我们提出优化问题,以最大程度地减少向天空中其他飞机提供IFEC的网络延迟,并提供从卫星中卸载AI/ML任务的服务。由于卫星和空中网络的动态性质,我们提出了可重新配置的优化。对于转换网络,我们连续识别每个应用程序的最佳MEC节点以及目标MEC节点的最佳路径。总而言之,我们的结果表明,与仅将陆地MEC节点(例如在线游戏)使用延迟应用程序(例如在线游戏)相比,使用AA-MEC将网络延迟性能提高了10.43%。此外,在将我们提出的动态方法与静态方法进行比较时,我们记录了IFEC的流程潜伏期至少降低6.7%的好处,计算卸载的流量潜伏期减少了56.03%。
Ubiquity in network coverage is one of the main features of 5G and is expected to be extended to the computing domain in 6G. In order to provide this holistic approach of ubiquity in communication and computation, an integration of satellite, aerial and terrestrial networks is foreseen. In particular, the rising amount of applications such as In-Flight Entertainment and Connectivity Services (IFECS) and SDN-enabled satellites renders network management more challenging. Moreover, due to the stringent Quality of Service (QoS) requirements edge computing gains in importance for these applications. Here, network performance can be boosted by considering components of the aerial network, like aircrafts, as potential Multi-Access Edge Computing (MEC) nodes. Thus, we propose an Aerial-Aided Multi-Access Edge Computing (AA-MEC) architecture that provides a framework for optimal management of computing resources and internet-based services in the sky. Furthermore, we formulate optimization problems to minimize the network latency for the two use cases of providing IFECS to other aircrafts in the sky and providing services for offloading AI/ML-tasks from satellites. Due to the dynamic nature of the satellite and aerial networks, we propose a re-configurable optimization. For the transforming network we continuously identify the optimal MEC node for each application and the optimal path to the destination MEC node. In summary, our results demonstrate that using AA-MEC improves network latency performance by 10.43% compared to the traditional approach of using only terrestrial MEC nodes for latency-critical applications such as online gaming. Furthermore, while comparing our proposed dynamic approach with a static one, we record a benefit of at least 6.7% decrease in flow latency for IFECS and 56.03% decrease for computation offloading.