A Multilevel Index Model to Expedite Web Service Discovery and Composition in Large-Scale Service Repositories

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Summary There are many different index structures for service repositories, such as sequential index, inverted index, and multilevel indices that include three deployments. Different service sets maybe have different characteristics that may affect performance from different aspects. For a given service set, which index structure is the most optimal one? To address these issues, this paper analyses five indexing models and proposes expectation of traversed service count to estimate performance of service retrieval. Based on these expectation formulas, an optimal deployment method can be identified to maximize efficiency of service retrieval. Our experiments first validate correctness of the proposed formulas and then validate the effective of the optimal method.

“…However, unfortunately, there are few works addressing this issue except our previous works as far as we know.…”

Section: Introductionmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Multilevel Index Modelmentioning

confidence: 99%

“…ℝ 3 denotes a set of all key classes. К denotes a set of all key s. Wu et al prove a theorem to help to optimize key selection. Simply, S is a service repository.…”

Section: Multilevel Index Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance formula‐based optimal deployments of multilevel indices for service retrieval

Liu

et al. 2017

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Optimization of service addition in multilevel index model for edge computing

Anjum

et al. 2021

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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.

An adaptive key selection method for the multilevel index model for effective service management in the cloud

Anjum

et al. 2023

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SUMMARYThe growing number of services processed and stored in the cloud has led to difficulties in managing and discovering the required services efficiently. Multilevel index model is an efficient method to manage and retrieve services in service repositories. When adding a new service to a multilevel index model, a key needs to be selected for the service, but existing key selection methods cannot adapt to the situation that hot services change over time. To address this problem, this article proposes an adaptive key selection method to improve the efficiency of service retrieval. However, the service addition operation of the adaptive key selection method is inefficient in the multilevel index model. For this reason, this article improves the multilevel index model by introducing local equivalence partition. This indexing model improves the service addition efficiency of the adaptive key selection method without affecting the service retrieval efficiency. It is experimentally demonstrated that the retrieval and addition efficiencies of the adaptive key selection method are close to the ideal state optimum under the multilevel index model with local equivalence partitioning.