The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

TL;DR

This paper introduces a least-used key selection method to enhance service retrieval efficiency in large-scale Cloud-based repositories by optimizing multilevel index models considering parameter usage probabilities.

Contribution

It proposes a novel least-used key selection method that reduces search scope and improves retrieval efficiency in service indexing models for large-scale Cloud repositories.

Findings

Significant improvement in service retrieval efficiency

Effective in scenarios with unequal parameter usage probabilities

Applicable across different indexing models

Abstract

As the number of devices connected to the Internet of Things (IoT) increases significantly, it leads to an exponential growth in the number of services that need to be processed and stored in the large-scale Cloud-based service repositories. An efficient service indexing model is critical for service retrieval and management of large-scale Cloud-based service repositories. The multilevel index model is the state-of-art service indexing model in recent years to improve service discovery and combination. This paper aims to optimize the model to consider the impact of unequal appearing probability of service retrieval request parameters and service input parameters on service retrieval and service addition operations. The least-used key selection method has been proposed to narrow the search scope of service retrieval and reduce its time. The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.