A Penalty-Based Approach for QoS Dissatisfaction Using Fuzzy Rules

Pernici, Barbara; Siadat, Seyed Hossein; Ouziri, Mourad

doi:10.1007/978-3-642-25535-9_43

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…Program at line 7 will achieve the adaptation. The work [13] proposes to assess penalties according to the degree of QoS violation based on fuzzy logic.…”

Section: B Automata-based Adaptive Algorithmmentioning

confidence: 99%

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Learning automata-based adaptive web services composition

Song²,

Liao

et al. 2014

2014 IEEE 5th International Conference on Software Engineering and Service Science

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Service-oriented computing is a widely adopted paradigm in real applications. Considering the continuous evolution of services, adaptive service composition has always been a major concern. It is a big challenge to adjust the composition to be optimal in real-time. In this paper, a learning automata-based approach is proposed to attack this problem. It consists of two important components: random environment and a learning automaton. The former can be mapped to the service's execution environment. The latter is responsible for the adaptation achievement using reward and penalty functions, while we take the service composition structures into account to compute the usefulness value of all services. At last, simulation study has shown that our approach is efficient to find the optimal (sub-optimal) composition.

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“…Program at line 7 will achieve the adaptation. The work [13] proposes to assess penalties according to the degree of QoS violation based on fuzzy logic.…”

Section: B Automata-based Adaptive Algorithmmentioning

confidence: 99%

“…Specially, it is usually NPhard. In [13], a soft constraint-based framework is used to seamlessly express QoS properties reÀecting both customer preferences and penalties applies to un¿tting situations. But in our approach, the reward is considered and the service which performs the best will be chosen.…”

Section: Related Workmentioning

confidence: 99%

Learning automata-based adaptive web services composition

Song²,

Liao

et al. 2014

2014 IEEE 5th International Conference on Software Engineering and Service Science

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“…Soft constraints-based algorithms are adopted in order to allow constraint violations in less likely feasible compositions when the requirements of the global QoS constraints are overly strict [19], [39], [40]. They are proposed to predict run-time service level agreement violations for composite services.…”

Section: Global Optimization Approachesmentioning

confidence: 99%

A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bio-inspired algorithms are sub-optimal methods. This paper will firstly present a hierarchical taxonomy of Web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the data-intensive service concretization problems. Abstract-In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bioinspired algorithms are sub-optimal methods. This paper will first present a hierarchical taxonomy of web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the dataintensive service concretization problems.

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“…Different types of cost functions have been identified in the literature (e.g., see [18,24,30]). These cost functions share two main characteristics [18]: (1) Cost functions are monotonically increasing; e.g., the penalty for a longer delay is never smaller than the penalty for a shorter delay; (2) Cost functions have a point discontinuity.…”

Section: Costs Functionsmentioning

confidence: 99%

“…In practice, however, costs may differ depending on the magnitude of the problem (e.g., see [17,24,30]). As an example, a longer delay in the freight transport process may result in higher penalties.…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Proactive Process Adaptation

Metzger

Bohn

2017

Service-Oriented Computing

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Abstract. Proactive process adaptation facilitates preventing or mitigating upcoming problems during process execution, such as process delays. Key for proactive process adaptation is that adaptation decisions are based on accurate predictions of problems. Previous research focused on improving aggregate accuracy, such as precision or recall. However, aggregate accuracy provides little information about the error of an individual prediction. In contrast, so called reliability estimates provide such additional information. Previous work has shown that considering reliability estimates can improve decision making during proactive process adaptation and can lead to cost savings. So far, only constant cost functions have been considered. In practice, however, costs may differ depending on the magnitude of the problem; e.g., a longer process delay may result in higher penalties. To capture different cost functions, we exploit numeric predictions computed from ensembles of regression models. We combine reliability estimates and predicted costs to quantify the risk of a problem, i.e., its probability and its severity. Proactive adaptations are triggered if risks are above a pre-defined threshold. A comparative evaluation indicates that cost savings of up to 31%, with 14.8% savings on average, may be achieved by the risk-based approach.

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A Penalty-Based Approach for QoS Dissatisfaction Using Fuzzy Rules

Cited by 7 publications

References 4 publications

Learning automata-based adaptive web services composition

Learning automata-based adaptive web services composition

A Systematic Review of Bio-Inspired Service Concretization

Risk-Based Proactive Process Adaptation

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