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

Wang, Lijuan; Shen, Jun; Luo, Junzhou

doi:10.1002/cpe.3589

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…In addition, we proposed a multi-objective ant colony system for data-intensive service concretization [75], where we focused on the scalability and adaptability of service concretization, and particular attention is paid to multiobjective optimization related to cost and QoS attributes. Summary of our current work can be found in [76].…”

Section: Pso Population Sizementioning

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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Section: Pso Population Sizementioning

confidence: 99%

A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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“…. , r}) are the maximum and minimum possible aggregated values of the k-th QoS attributes for the composite service CS, which can be estimated by (6).…”

Section: Utility Functionmentioning

confidence: 99%

“…5 already proved that it was useful for service management and discovery to add biological mechanisms to services. In our previous work, 6 we have presented a hierarchical taxonomy of Web service composition approaches and provided a detailed analysis of each approach. Then we found that the bio-inspired algorithms could overcome the challenging requirements of the data-intensive service provision.…”

Section: Introductionmentioning

confidence: 99%

Data-Intensive Service Provision Based on Particle Swarm Optimization

Wang¹,

Shen²

2018

IJCIS

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The data-intensive service provision is characterized by the large of scale of services and data and also the high-dimensions of QoS. However, most of the existing works failed to take into account the characteristics of data-intensive services and the effect of the big data sets on the whole performance of service provision. There are many new challenges for service provision, especially in terms of autonomy, scalability, adaptability, and robustness. In this paper, we will propose a discrete particle swarm optimization algorithm to resolve the data-intensive service provision problem. To evaluate the proposed algorithm, we compared it with an ant colony optimization algorithm and a genetic algorithm with respect to three performance metrics.

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“…Wang et al . discussed single‐objective and multi‐objective data‐intensive service provision problems based on bio‐inspired algorithms . They also propose an ant‐inspired negotiation approach.…”

mentioning

confidence: 99%

“…Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. Wang et al discussed single-objective and multi-objective data-intensive service provision problems based on bio-inspired algorithms [9]. They also propose an ant-inspired negotiation approach.…”

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confidence: 99%

Recent research advances in cloud computing and big data

Dong

Malloy

2015

Concurrency and Computation

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Cloud computing, often referred to as simply 'the cloud', is the delivery of on-demand computing resources-everything from applications to data centers-over the Internet on a pay-for-use basis [3]. Cloud computing also promises to accommodate a huge volume of data that is regarded as a promising methodology to deal with efficient big data processing. Integration of cloud and big data ecosystems thereby not only allows improved cloud resource utilization and efficiency but also brings optimized performance for big data applications underpinned by cloud infrastructures. Based on the trend that cloud infrastructures of traditional way in which big data applications are executed are undergoing radical change, cloud has evolved in many aspects including cloud architectures, cloud applications, cloud security, and cloud services. However, there are still many other challenges to be resolved to offer better support for big data applications.Research advances in big data and cloud computing continue to be major focus areas in research and industry and we expect that to continue for the next few years. This special issue focuses on new strategic research area that addresses 'Recent Research Advances in Cloud Computing and Big Data'. From the submitted papers for the 2nd

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

Cited by 7 publications

References 49 publications

A Systematic Review of Bio-Inspired Service Concretization

A Systematic Review of Bio-Inspired Service Concretization

Data-Intensive Service Provision Based on Particle Swarm Optimization

Recent research advances in cloud computing and big data

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