Design and Optimization of Aerial-Aided Multi-Access Edge Computing towards 6G
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 particula...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2206.14526 |