An evolutionary algorithmic based web service composition with quality of service

Sharifara, Parvin; Yari, Alireza; Kashani, Mohammad Mansour Riahi

doi:10.1109/istel.2014.7000670

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Yao et al 16 and Sharifara et al 17 presented NSGA‐II for QoS‐aware service composition. Zhang et al 18 developed a mapreduce‐based improved discrete particle swarm optimization (PSO) for QoS‐aware large‐scale service composition.…”

Section: Related Workmentioning

confidence: 99%

QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dutta

Jatoth

Gangadharan

et al. 2021

Concurrency and Computation

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Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service-aware big service composition methodology using a distributed co-evolutionary algorithm. In our proposed model, we develop a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective Jaya algorithm for enhancing the diversity of solutions. The distributed co-evolutionary algorithm finds the near-optimal solution in a fast and scalable way. K E Y W O R D Sbig services, distributed co-evolutionary algorithm, distributed Jaya, distributed NSGA-III, quality of service, service composition INTRODUCTIONWith the onset of cloud computing, Internet of Things (IoT), and machine learning/ deep learning, manufacturing industries are undergoing a drastic change. Modern industries are incorporating these new technologies to achieve better efficiency in manufacturing products and services, with an improvement in quality control and a reduction in production costs. The Fourth Industrial Revolution (or Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using these modern smart technologies. Big services are collections of interrelated web services across the virtual and physical domains of Industry 4.0, processing Big Data. 1,2 Big services are integrated from various domains to develop a composite service that addresses the requirements of a customer. 3 Consider a fictitious company that manufactures electric vehicles. The company uses IoT, cloud computing, and machine learning/ deep learning to test for and predict possible failures and for tweaking or designing new engine parts, suspension units, and many more. Let us consider that the company has two divisions: design analysis unit and production unit. The design analysis unit analyzes the performances of the electric vehicles and suggests new design models. The lessons learned from the design analysis unit are incorporated in the design and manufacturing of electric vehicles by the production unit. Assume that both the design analysis unit and the production unit use a similar workflow. We can imagine two separate sets of service composition requirements: The design analysis unit requires high performance in terms of response time and throughput, high security, and with no restrictions on cost. The production unit has to keep the cost low and maintain the desirable quality of service (QoS) parameters and changes with respect to supply and demand throughout the year.In this scenario, we need to execute the service composition process repeatedly whenever there is a change in the requirements, with certain changes in the fitness function. This becomes computationally expensive as the number of services grows, being classified as NP-hard. 4,5 Co-evolutionary approaches offer promising solutions in this case and man...

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Section: Related Workmentioning

confidence: 99%

QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dutta

Jatoth

Gangadharan

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…It is able to support decision-making and evaluate uncertain parameters. In the existing researches [30][31][32][33], fuzzy logic technology has been used for service ranking in the process of service selection. In this paper, we propose an adaptive adjustment method for the number of quality level based on fuzzy logic (AAQL).…”

Section: General Fuzzy Logic Systemmentioning

confidence: 99%

Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

Yuan

Zhang

et al. 2019

Symmetry

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As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maximum utility and meet the global QoS constraints with low time cost. In this paper, we propose a dynamic service selection approach based on adaptive global QoS constraints decomposition. Fuzzy logic technology and Cultural Genetic Algorithm are used to adaptively decompose global QoS constraints into near-optimal local constraints. According to the near-optimal local constraints, the optimal service is selected for each service class during the running time efficiently. Experimental results show that the proposed approach not only achieves the near-optimal solution, but also significantly reduces the computation time, and has good adaptability and scalability.

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A study on evolutionary computing based web service selection techniques

Purohit

Kumar

2020

Artif Intell Rev

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An evolutionary algorithmic based web service composition with quality of service

Cited by 4 publications

References 11 publications

QoS‐aware big service composition using distributed co‐evolutionary algorithm

QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

A study on evolutionary computing based web service selection techniques

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