Optimization of Service Addition in Multilevel Index Model for Edge Computing

TL;DR

This paper introduces a new key selection method to optimize service addition in multilevel index models for edge computing, significantly reducing update times while maintaining retrieval stability.

Contribution

The paper proposes a designated key selection technique that enhances the efficiency of updating multilevel index models in dynamic edge environments, outperforming existing methods.

Findings

Reduces service addition time by up to 84% in partial index models.

Reduces service addition time by up to 78% in full index models.

Maintains service retrieval stability despite efficiency improvements.

Abstract

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.