Abstract
AbstractWith 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 while 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 article 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.
Highlights
The rapid development of the concept of Artificial Intelligence (AI) and Big Data has transformed the way services are being offered over the Internet
Our work proposed in this paper extended the multilevel index model in the edge environment, which can effectively manage big data and proposes a designated key selection method, with the motivation of narrowing down the search space of service retrieval process and to reduce the time overheads without affecting the stability of the service retrieval process
The multilevel index model is built based on the equivalence relation, with the motivation of reducing the redundancies resulting in the service retrieval process.[20,21]
Summary
The rapid development of the concept of Artificial Intelligence (AI) and Big Data has transformed the way services are being offered over the Internet. An increasing number of edge devices alongside the deployments of millions of industrial sensors in the physical and artificial environment is generating large volumes of complex real-time streaming data, leading to ever-changing ways of service discovery and composition, and the types of services invoked by users continue to expand.[8,9] Such an unprecedented data generation is posing several challenges in acquiring and processing sensor generated data.[10] The real-time processing requirements of big data applications are making the traditional Cloud-based services models unsuitable for latency sensitive applications. Efficient service composition and discovery are becoming a challenging problem in service applications, since such an application usually comprises a large number of different services with complex interactions.[16] It is worthy of note that the mentioned services are usually distributed in the edge devices, and identifying the required services is an important task so that the processing cluster can be formed with the right capacity.
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