Towards an approach for cloud service composition in Multi-Cloud environment based QoS using deep Q-learning

Boutarfa, Manel; Maamri, Ramdane; Lacheheub, Mohammed Nassim

doi:10.1109/icaase56196.2022.9931591

2022 International Conference on Advanced Aspects of Software Engineering (ICAASE) 2022

DOI: 10.1109/icaase56196.2022.9931591

|View full text |Cite

Towards an approach for cloud service composition in Multi-Cloud environment based QoS using deep Q-learning

Manel Boutarfa

Ramdane Maamri

Mohammed Nassim Lacheheub

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An improved ACO based service composition algorithm in multi-cloud networks

Bei,

Wenlin,

Xin

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

In recent years, with the rapid development of mobile communication networks, some new services such as cloud virtual reality, holographic communication, and etc. continue to emerge. Service composition has been researched in cloud computing. however, as the fast development of edge clouds, the service components can be deployed on the edge clouds to reduce the composition latency, so the more flexible and intelligent service composition algorithms are urgently need to study. Based on this, we propose a service composition strategy under the multi-cloud environment, and we propose an ant colony optimization algorithm (ACO) based on the multi-pheromone mechanism to optimize the quality of service (QoS). To avoid the occurrence of local optima, we further introduce the mutation operation of the genetic algorithm. Finally, the simulation results show that the proposed algorithm can achieve better QoS parameters such as latency and response time while ensuring the stability of services.

show abstract

An improved ACO based service composition algorithm in multi-cloud networks

Bei,

Wenlin,

Xin

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Towards an approach for cloud service composition in Multi-Cloud environment based QoS using deep Q-learning

Cited by 1 publication

References 0 publications

An improved ACO based service composition algorithm in multi-cloud networks

An improved ACO based service composition algorithm in multi-cloud networks

Contact Info

Product

Resources

About