Quick convergence of genetic algorithm for QoS-driven web service selection

Ma, Yuchun; Zhang, Chengwen

doi:10.1016/j.comnet.2007.12.003

Cited by 119 publications

(61 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jaeger and Mühl [2007] discussed the Quality of service-based web services selection problems using GAs. Ma and Zhang [2008] proposed a GA based method for web service composition and web service selection which takes account of QoS constraints. Zhang et al Zhang et al 2006 applied GAs for web services selection with global QoS constraints.…”

Section: Design Tools and Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Search-based software engineering

2012

View full text Add to dashboard Cite

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE life cycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This paper 1 provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

show abstract

Section: Design Tools and Techniquesmentioning

confidence: 99%

“…The problem of QoS introduced by Canfora et al [2005a], also denotes an area of non-functional optimization in SE which has recently witnessed an upsurge in activity and interest [Jaeger and Mühl 2007;Ma and Zhang 2008;Su et al 2007;Zhang et al 2006.…”

Section: Sbse For Non Functional Propertiesmentioning

confidence: 99%

Search-based software engineering

2012

View full text Add to dashboard Cite

show abstract

“…Their simulation results reveal that genetic algorithms outperform linear programming with increasing numbers of candidate services and tasks. Ma and Zhang [23] further propose enhanced initial population policy and evolution policy to improve the effectiveness, convergence and stability of the genetic algorithm for QoS-aware service selection. Klein et al [24] consider that component services are generally executed repeatedly over long-term periods, and they propose a method for optimizing the QoS of candidate services to reach the long-term QoS requirement by using linear programming.…”

Section: Related Workmentioning

confidence: 99%

Predicting Service Price for Game-Theoretic Qos-Aware Service Selection

Wang¹,

Anup²,

Munindar³

2016

STSC

View full text Add to dashboard Cite

In QoS-aware service selection, a service requester seeks to maximize his utility by selecting service providers that charges the lowest service prices while meeting the requester's global QoS requirements. In existing selection approaches, a service requester focuses on finding providers based on their QoS and thereby ignores their service prices that could change with their QoS. High QoS may provide more benefits, but may require a high service price. As a result, the highest QoS may not produce the maximum utility. A service requester and service providers have a conflicting interest over service prices. We model the process of QoS-aware service selection as a price competition game. In the case where the providers' minimum prices are public knowledge, we propose an approach to compute a ε-Nash equilibrium that maximizes the service requester's utility. However, in general, a service provider would not reveal its minimum acceptable price. Thus, it is important for a service requester to predict the minimum price for a service that meets its QoS requirements. We propose a collaborative approach to predicting a provider's minimum price for a desired QoS based on prior usage experiences. Based on the prediction results of the service providers' minimum prices, we propose a multi-round mechanism where the service requester chooses a service for each task by using a Vickrey auction with reserve price. The experimental results show our approach can find the optimal service providers efficiently and effectively.

show abstract

“…[28] adopted an initial population policy and the mutation policy to promote the fitness of genetic algorithm. [9] presented an enhanced initial population policy and an evolution policy for GA. [17] focused on how fitness function and a mutation policy affected the performance of the genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In [9], [17], [28], a genetic algorithm based on a relation matrix coding scheme for QoS-aware service selection problem was proposed. The authors argued that the one dimension coding scheme cannot express all paths of composite service at the same time but the relation matrix coding schemes can.…”

Section: Related Workmentioning

confidence: 99%

An Improved Genetic Algorithm for Cost-Effective Data-Intensive Service Composition

Wang

Shen

Luo³

et al. 2013

2013 Ninth International Conference on Semantics, Knowledge and Grids

View full text Add to dashboard Cite

The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and compared against other traditional approaches, such as mix integer programming. The contributions of this paper are three folds: 1) An economical model for dataintensive service provision is proposed, 2) An extensible QoS model is also proposed to calculate the QoS values of data-intensive services, 3) Finally, a modified genetic algorithm-based approach is introduced to compose data-intensive services. A local selection method with modifications of crossover and mutation operators is adopted for this algorithm. The results of experiments will demonstrate the scalability and effectiveness of our proposed algorithm. Abstract-The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and compared against other traditional approaches, such as mix integer programming. The contributions of this paper are three folds: 1) An economical model for data-intensive service provision is proposed; 2) An extensible QoS model is also proposed to calculate the QoS values of data-intensive services; 3) Finally, a modified genetic algorithmbased approach is introduced to compose data-intensive services. A local selection method with modifications of crossover and mutation operators is adopted for this algorithm. The results of experiments will demonstrate the scalability and effectiveness of our proposed algorithm.

show abstract

Quick convergence of genetic algorithm for QoS-driven web service selection

Cited by 119 publications

References 12 publications

Search-based software engineering

Search-based software engineering

Predicting Service Price for Game-Theoretic Qos-Aware Service Selection

An Improved Genetic Algorithm for Cost-Effective Data-Intensive Service Composition

Contact Info

Product

Resources

About