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A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud

A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud

Soumia Zertal, Mohamed Batouche, Zakaria Laboudi
Copyright: © 2020 |Volume: 12 |Issue: 4 |Pages: 22
ISSN: 1935-5688|EISSN: 1935-5696|EISBN13: 9781799805441|DOI: 10.4018/IJISSS.2020100102
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MLA

Zertal, Soumia, et al. "A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud." IJISSS vol.12, no.4 2020: pp.14-35. http://doi.org/10.4018/IJISSS.2020100102

APA

Zertal, S., Batouche, M., & Laboudi, Z. (2020). A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud. International Journal of Information Systems in the Service Sector (IJISSS), 12(4), 14-35. http://doi.org/10.4018/IJISSS.2020100102

Chicago

Zertal, Soumia, Mohamed Batouche, and Zakaria Laboudi. "A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud," International Journal of Information Systems in the Service Sector (IJISSS) 12, no.4: 14-35. http://doi.org/10.4018/IJISSS.2020100102

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Abstract

The requests of the companies for the development and deployment of their business-applications in Cloud become more complex so that, sometimes, a one single service cannot carry out the target task on its own. Hence, a user-request is provided as a composite service. On another note, the number of available services is significantly increasing. Therefore, the authors would need to find the optimal cloud service-compositions that satisfy the quality of service values as well as user requirements. The methods proposed in literature for composing cloud services do not consider the composition and deployment constraints of candidate cloud services. This paper presents a novel optimization-based approach for building business-application in Cloud. The proposed approach combines the particle swarm optimization algorithm with some principles of ant colony optimization algorithm to deal with multiple QoS parameters, but also to satisfy the composition and deployment constraints of cloud services. The experimental results show the efficiency of the method for all tests instances.

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