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

Wang, Limin; Shen, Jun; Luo, Junzhou; Dong, Fang

doi:10.1109/skg.2013.19

Cited by 20 publications

(21 citation statements)

References 27 publications

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“…In general, data-intensive service concretization will be cooperatively supported by three stakeholders: the service composers, the service providers, and the data providers [64]. Providers need an approach to regulate and price their resources, either services or data or their combination.…”

Section: Pso Population Sizementioning

confidence: 99%

“…A graph for the data-intensive service concretization problem was given in [64]. The lifetime of the problem framework was described in [68].…”

Section: Pso Population Sizementioning

confidence: 99%

“…The economic model described in our earlier work [64] may be extended to include the network providers, since the cost of transferring data and the response time of accessing data depend on the network bandwidth. Various providers need a standardized yet adaptive way to regulate and price their resources, which are specified by the utility of the stakeholders.…”

Section: Remaining Problemsmentioning

confidence: 99%

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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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Section: Pso Population Sizementioning

confidence: 99%

“…A graph for the data-intensive service concretization problem was given in [64]. The lifetime of the problem framework was described in [68].…”

Section: Pso Population Sizementioning

confidence: 99%

Section: Remaining Problemsmentioning

confidence: 99%

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A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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“…U is the overall utility of the best path P bs , which is given in [31]. This rule indicates that both the evaporation and the new pheromone deposit, only apply to every edge of the best path P bs .…”

Section: A An Economic Modelmentioning

confidence: 99%

Bio‐inspired cost‐aware optimization for data‐intensive service provision

Wang

Shen

Luo

2015

Concurrency and Computation

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The use of Big Data and the development of cloud computing have led to greater dependence on dataintensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss singleobjective and multi-objective data-intensive service provision problems based on bio-inspired algorithms. Further, we will propose an ant-inspired negotiation approach. Finally, this paper points out future research topics. The use of Big Data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss single-objective and multi-objective data-intensive service provision problems based on bio-inspired algorithms. Further, we will propose an ant-inspired negotiation approach. Finally, this paper points out future research topics. Disciplines Engineering | Science and Technology Studies

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“…We have been applying bio-inspired algorithms to tackle the service composition problems [15]- [24]. We investigated how the ant colony optimization was used for peer selection in service composition [16]- [18].…”

Section: Related Workmentioning

confidence: 99%

Multi-objective Ant Colony System for Data-Intensive Service Provision

Wang¹,

Shen²,

Luo³

2014

2014 Second International Conference on Advanced Cloud and Big Data

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Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering the multi-objective dataintensive features. We propose to apply ant colony optimization algorithms and implemented them with simulated workflows in different scenarios. To evaluate the proposed algorithm, we compared it with a multiobjective genetic algorithm with respect to five performance metrics Abstract-Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering the multi-objective data-intensive features. We propose to apply ant colony optimization algorithms and implemented them with simulated workflows in different scenarios. To evaluate the proposed algorithm, we compared it with a multi-objective genetic algorithm with respect to five performance metrics.

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

Cited by 20 publications

References 27 publications

A Systematic Review of Bio-Inspired Service Concretization

A Systematic Review of Bio-Inspired Service Concretization

Bio‐inspired cost‐aware optimization for data‐intensive service provision

Multi-objective Ant Colony System for Data-Intensive Service Provision

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