  With the development of Edge Computing and Artificial Intelligence (AI)
technologies, edge devices are witnessed to generate data at unprecedented
volume. The Edge Intelligence (EI) has led to the emergence of edge devices in
various application domains. The EI can provide efficient services to
delay-sensitive applications, where the edge devices are deployed as edge nodes
to host the majority of execution, which can effectively manage services and
improve service discovery efficiency. The multilevel index model is a
well-known model used for indexing service, such a model is being introduced
and optimized in the edge environments to efficiently services discovery whilst
managing large volumes of data. However, effectively updating the multilevel
index model by adding new services timely and precisely in the dynamic Edge
Computing environments is still a challenge. Addressing this issue, this paper
proposes a designated key selection method to improve the efficiency of adding
services in the multilevel index models. Our experimental results show that in
the partial index and the full index of multilevel index model, our method
reduces the service addition time by around 84% and 76%, respectively when
compared with the original key selection method and by around 78% and 66%,
respectively when compared with the random selection method. Our proposed
method significantly improves the service addition efficiency in the multilevel
index model, when compared with existing state-of-the-art key selection
methods, without compromising the service retrieval stability to any notable
level.
