Web service selection and composition based on uncertain quality of service

Yasmina, Remaci Zeyneb; Fethallah, Hadjila; Fadoua, Lahfa

doi:10.1002/cpe.6531

Cited by 12 publications

(4 citation statements)

References 46 publications

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“…Finally, an improved version of NSGA-II was used to derive the non-dominated service compositions. The framework proposed in [38] involved two steps: the first one retains the pertinent services of the local tasks using majority grades, and the second step performs a constraint programming search to keep the optimal compositions. In the same line of thought, the work by [39] proposed a heuristic for filtering the desirable services of each local task using hesitant fuzzy sets and cross-entropy, then a metaheuristic termed grey wolf optimization was applied to retain the Top-K near-optimal service compositions.…”

Section: Service Selection With Uncertain Qosmentioning

confidence: 99%

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An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

Etchiali

Fethallah

Bekkouche

2023

BDCC

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Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-complete. The search time is mainly dependent on the number of requested tasks, the size of the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the task services and a bat algorithm metaheuristic for selecting the final near-optimal compositions. The fitness used by the metaheuristic aims to fulfil all the global constraints of the user. The experimental study showed that the ranking heuristics, termed “fuzzy Pareto dominance” and “Zero-order stochastic dominance”, are highly effective compared to the other heuristics and most of the existing state-of-the-art methods.

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Section: Service Selection With Uncertain Qosmentioning

confidence: 99%

“…It is worth noting that the majority grade principle was initially presented by [45] for ranking the candidates of an election. After that, it was adapted by [38] to web service selection. According to Figure 7, we observed that all methods had almost the same CPU time up to n = 5.…”

Section: Experimental Studymentioning

confidence: 99%

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

Etchiali

Fethallah

Bekkouche

2023

BDCC

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“…Recent approaches have tackled uncertainty on the web using quality of service as an uncertainty modeling metric. The authors in Reference 14 proposed a heuristic based on majority judgment that aims to reduce the search space. Besides, in order to select the best top‐K compositions that respond to the QOS global constraints they performed a set of programming search.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic approach: Uncertain navigation of the uncertain web

Boulaares

Sassi

Benslimane

et al. 2022

Concurrency and Computation

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Summary In the era of Internet Technology (IT), uncertainty management is a challenge in many fields. These include e‐commerce, social and sensor networks, scientific data production and mining, object tracking, data integration, geo‐located services, and recently Internet and Web of Things.3$$ {}^3 $$ Due to the uncertain data published on the web, web resources are diverse. Hence, identical resources could be available from heterogeneous platforms and heterogeneous resources could represent the same objects. These resources are hugely heterogeneous, conflict, inconsistent, or have incompatible formats. This uncertainty is inherently related to many facts, such as information extraction and integration. Hence, with web resources proliferation on the web, referencing through the uncertain web has become increasingly difficult. The traditional techniques used for the classical web could not handle uncertain navigation. Generally, it's implicitly represented, decided randomly, or even neglected. Harnessing these uncertain resources to their full potential in order to handle the uncertain navigation, raises major challenges that relate to each phase of their life cycle: creation, representation, and navigation. In this article, we establish a probabilistic approach to model and interpret uncertain web resources. We present operators to compute response uncertainty. Finally, we create algorithms in order to validate resources and achieve uncertain hypertext navigation.

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“…Web services operate in dynamic network environments where factors such as network bandwidth, access point locations, and time can cause fluctuations in service performance, primarily reflected in the dynamic changes in QoS [6,7]. Even for the same user in the same network environment, uncertainties in server loads from service providers can lead to QoS fluctuations in Web services for different invocation times [8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Service Composition Method Based on Zero-Sum Game Integrated Inverse Reinforcement Learning

Yuan,

Guo,

2023

IEEE Access

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Automatically generating service composition solutions that meet user application requirements is one of the hot research topics in the field of service composition in the context of Web service big data. To address the challenges of accurately obtaining reward function values and significant increase in time complexity when dealing with large-scale data in the context of reinforcement learning-based service composition, this paper proposes a novel approach that combines zero-sum game and inverse reinforcement learning. The proposed method models the service composition problem as a Markov Decision Process (MDP) and dynamically adjusts the service composition solution by solving for the optimal policy. By leveraging runtime records of service composition operations, we generate an expert experience dataset and develop a novel inverse reinforcement learning algorithm based on the integration of zero-sum game principles. Experimental results demonstrate that the proposed method effectively reduces the dependence of the inverse reinforcement learning algorithm on the quality of expert experience data and reduces the time cost of service composition.INDEX TERMS Service composition, inverse reinforcement learning, zero-sum game, quality of service.

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Web service selection and composition based on uncertain quality of service

Cited by 12 publications

References 46 publications

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

A probabilistic approach: Uncertain navigation of the uncertain web

Dynamic Service Composition Method Based on Zero-Sum Game Integrated Inverse Reinforcement Learning

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