A Novel QoS Monitoring Approach Sensitive to Environmental Factors

Zhang, Pengcheng; Zhuang, Yuan; Leung, Hareton; Song, Wei; Zhou, Yu

doi:10.1109/icws.2015.29

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…Bashari et al [20] proposed an automated approach to reconfigure the service composition strategy from the changing environment, the reconfiguration policies were derived from software product line techniques. Zhang et al [8] recognised the necessity of an explicit environment model during service selection and proposed a QoS-aware monitoring approach. However, this approach is restricted to QoS monitoring purposes instead of service composition.…”

Section: Related Workmentioning

confidence: 99%

“…During composition, quality-of-service (QoS) usually emerges as the primary concern, since it is often the case that multiple services provide similarly or the same functionality, but with significantly different QoS measures. Supporting QoS-aware service composition is a challenging task due to the inherent uncertainty from the environment [6][7][8]. For instance, it is conceivable that services may exhibit fluctuating performance because of the vibrating network bandwidth.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic analysis of QoS‐aware service composition with explicit environment models

et al. 2020

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In service composition, quality-of-service (QoS) represents a crucial indicator for the policy adoption. Existing composition strategies rarely address the influence of the environment, which may influence QoS and thus lead to sub-optimal composition policies in a dynamic environment. In this study, a model-based service composition approach is proposed. Given the user request, it is possible to first find a set of matching abstract web services (AWSs), and then pull relevant concrete web services (CWSs) based on the AWSs. The set of CWSs can be modelled as a Markov decision process (MDP). In addition, the authors model the environment as a fully probabilistic system, capturing changes of environment probabilistically. The environment model can be further composed of the MDP from the service models, obtaining a monolithic MDP. They demonstrate how the probabilistic verification techniques can be used to find the optimal service selection strategy against their QoS and the environment change. A distinguishing feature of their approach is that the QoS, as well as the dynamic of environment change, is made parametric so that the formal analysis is adaptive to the environment which is of paramount importance for autonomous and self-adaptive systems. Examples and experiments confirm the feasibility of their approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic analysis of QoS‐aware service composition with explicit environment models

et al. 2020

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“…Web services are self-contained, self-describing, modular applications, and reusable software components that are distributed and programmatically accessible over the Internet [1]. The success of Web service invocation largely depends on its QoS (Quality of Service) [2]. The QoS has become a significant factor in Web service management tasks since it indicates the critical features of a Web service such as reliability, throughput, and response time [3].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent QoS Identification for Untrustworthy Web Services via Two-Phase Neural Networks

Wang

Lü

2016

2016 IEEE International Conference on Web Services (ICWS)

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Abstract-QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.

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