Towards minimizing cost for composite data-intensive services

IEEE Trans. Serv. Comput.

2017

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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.

Section: Pso Population Sizementioning

confidence: 99%

Section: Pso Population Sizementioning

confidence: 99%

A Systematic Review of Bio-Inspired Service Concretization

IEEE Trans. Serv. Comput.

2017

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“…Bio-inspired optimization algorithms have been proposed to solve the service provision problem, because of the simplicity of the algorithms and the rapid convergence to optimal or near-optimal solutions [20], [22]. Biological entities can learn from their environment.…”

Section: Related Workmentioning

confidence: 99%

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

2013 Ninth International Conference on Semantics, Knowledge and Grids

Luo³

et al. 2013

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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.

“…Bio-inspired algorithms offer many advantages for dealing with data-intensive service provision problems [26], [32]. Bio-inspired optimization algorithms have been proposed to solve the service provision problem, because of the simplicity of the algorithms and the rapid convergence to optimal or near-optimal solutions [15], [31], [33]. Biological entities can learn from their environment.…”

Section: Related Workmentioning

confidence: 99%

Cost and Time Aware Ant Colony Algorithm for Data Replica in Alpha Magnetic Spectrometer Experiment

Luo

2013 IEEE International Congress on Big Data

et al. 2013

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Huge collections of data have been created in recent years. Cloud computing provides a way to enable massive amounts of data to work together as data-intensive services. Considering Big Data and the cloud together, which is a practical and economical way to deal with Big Data, will accelerate the availability and acceptability of analysis of the data. Providing an efficient mechanism for optimized data-intensive services will become critical to meet the expected growth in demand. Because the competition is an extremely important factor in the marketplace, the cost model for data-intensive service provision is the key to provide a sustainable service market. As data play the dominant role in execution of data-intensive service composition, the cost and access response time of data sets influence the quality of the service that requires the data sets. In this paper, a data replica selection optimization algorithm based on an ant colony system is proposed. The performance of the data replica selection algorithm is evaluated by simulations. The background application of the work is the Alpha Magnetic Spectrometer experiment, which involves large amounts of data being transferred, organized and stored. It is critical and challenging to be cost and time aware to manage the data and services in this intensive research environment. Abstract-Huge collections of data have been created in recent years. Cloud computing provides a way to enable massive amounts of data to work together as data-intensive services. Considering Big Data and the cloud together, which is a practical and economical way to deal with Big Data, will accelerate the availability and acceptability of analysis of the data. Providing an efficient mechanism for optimized data-intensive services will become critical to meet the expected growth in demand. Because the competition is an extremely important factor in the marketplace, the cost model for data-intensive service provision is the key to provide a sustainable service market. As data play the dominant role in execution of data-intensive service composition, the cost and access response time of data sets influence the quality of the service that requires the data sets. In this paper, a data replica selection optimization algorithm based on an ant colony system is proposed. The performance of the data replica selection algorithm is evaluated by simulations. The background application of the work is the Alpha Magnetic Spectrometer experiment, which involves large amounts of data being transferred, organized and stored. It is critical and challenging to be cost and time aware to manage the data and services in this intensive research environment.