QoS Uncertainty Handling for an Efficient Web Service Selection

Etchiali, Abdelhak; Fethallah, Hadjila; Merzoug, Mohammed Amine

doi:10.1145/3361570.3361592

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…A majority interval-based heuristic was introduced in [9] in order to derive the pertinent services of a set of tasks. The main idea consists of computing the median interval of each nondeterministic QoS attribute and comparing them using rectified linear unit (ReLU) functions [30].…”

Section: Service Selection With Uncertain Qosmentioning

confidence: 99%

“…Median u∈{1,...,l} QoS ps j ju (9) By assuming that the criterion p is positive, the global constraint with respect to the median value is specified as:…”

Section: Global Qos Conformancementioning

confidence: 99%

“…-H 3 (first-order stochastic dominance) is a stochastic dominance relationship that uses the first-order terms of the QoS sample [8]; this means that H 3 uses the cumulative distribution of the sample to perform comparisons. -H 4 (the majority interval heuristic) was inspired by [9]. In this ranking, we compute the median interval of each service and perform pairwise comparisons of the services using Equation (27).…”

Section: Introductionmentioning

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%

“…Median u∈{1,...,l} QoS ps j ju (9) By assuming that the criterion p is positive, the global constraint with respect to the median value is specified as:…”

Section: Global Qos Conformancementioning

confidence: 99%

Section: Introductionmentioning

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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“…They developed an algorithm to improve referencing across the web of uncertain data and effectively respond to various user requests. More recently, Abdelhak et al [25] offers a solution to solve the problem of selecting web services with non-deterministic QoS. Indeed, to cut all the candidate services, they propose a local classification for each abstract task using the majority dominance heuristic and then proceed to a global search to maximize the stress rate.…”

Section: Related Work: Uncertainty Issues On the Webmentioning

confidence: 99%

Information Management under uncertain large-scale cloud environment

OMRI

Omri

Benslimane

et al. 2022

Preprint

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“Uncertainty” is related to working with inaccurate data, imprecise and incomplete information, and unreliable results that can lead to irrational decisions. Several approaches to managing uncertain data on the web have been proposed in the literature to resolve this problem. These approaches have failed to find the solution to this problem with accuracy and performance. Our study aims to propose a new probabilistic approach to manage web information resources in an uncertain large-scale cloud environment. Our approach is based on three main steps: (1) modeling uncertain web resources, (2) computing HTTP request model, and (3) interpretation and evaluation of uncertain web resources in a context of classic hypertext navigation. The experimental study shows that the analysis of the execution time necessary for the composition of the services, by our approach, is negligible, compared to that of the other studied approaches. The algorithm which deals with the impact of the variation in the number of nodes, which we have proposed, has also been evaluated and checks all the possibilities in polynomial time and can adapt to many possibilities of multiplexing of values.

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“…Several studies have addressed QoS of the Web services under uncertainty to achieve the appropriate service required [4,[8][9][10][11][12]. Authors in [8] proposed a robust model that can select composite services within the uncertain QoS environment.…”

Section: Introductionmentioning

confidence: 99%

Representing and Simulating Uncertainty of the Quality of Service of Web Services using Fuzzy Cognitive Map Approach

Obiedat¹,

Khasawneh²,

Banikhalaf³

et al. 2020

IJACSA

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Web services are growing rapidly to provide clients, either organizations or individuals, with multiple Internet services and to offer solutions for the integration of many applications. Quality of Service (QoS) of a Web service is the key consideration of both service providers and users. Thus, measuring the QoS requires, in addition to its normative requirements, engaging the views of clients and service providers and environmental factors. Human intervention and the environment may lead to uncertainty and result in uncertain factors in assessing QoS. In such a case, traditional computing and statistical techniques cannot provide an accurate representation of inherited uncertainties, especially when uncertain variables are connected with ambiguous (fuzzy) relationships. An alternative is to use a soft computing approach. This paper proposes a Fuzzy Cognitive Map (FCM) model as a soft computing approach that can represent and simulate the uncertainty of QoS. FCM represents the uncertain variables in the domain knowledge and their connections in the form of a signed directed graph consisting of nodes representing the variables and directed arrows representing the cause-effect relationships. In addition, it allows representing imprecise data either using numeric data, i.e. in the ranges [0, 1] and [-1, 1], or linguistic data, i.e., "low, medium, high". For calculations, FCM is converted to an adjacency matrix to find the effects of variables on each other. Scenario simulations can also be implemented to help decision makers to investigate appropriate outcomes. Finally, the proposed approach is tested by an experiment to demonstrate its reasonability and admissibility of the representation and simulation of the uncertainty of the QoS domain knowledge.

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QoS Uncertainty Handling for an Efficient Web Service Selection

Cited by 8 publications

References 17 publications

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

Information Management under uncertain large-scale cloud environment

Representing and Simulating Uncertainty of the Quality of Service of Web Services using Fuzzy Cognitive Map Approach

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