A Container‐Based Cloud Broker for Effective Service Provisioning in Multicloud Environment

Container‐based cloud brokers are third‐party services that act as an intermediate entity between users and multiple cloud providers. The cloud brokers intended to perform discovery and provisioning of cloud services with an affordable pricing scheme. As cloud services can be provisioned on‐demand basis for multiple users, the cloud brokers are unable to provide the most suited services to the users on time. To address this issue, the proposed work introduces a novel approach for efficient cloud service provisioning by utilizing container‐based cloud service brokerage and implementing service arbitrage across various cloud providers. A microservice architecture‐based service discovery mechanism is developed which incorporates a service registry for tracking newly available services from the providers. Docker containers are employed to orchestrate the services, which ensures streamlined management and deployment of offered services. Further, the proposed system recommends and evaluates the services to the cloud users based on probability matrices, mapping matrices, and user feedback. The performance of the proposed model is compared with existing techniques, namely, rough multidimensional matrix (RMDM) and similarity‐enhanced hybrid group recommendation approach (HGRA). Experimental results show that the proposed model outperforms the existing models in terms of clustering accuracy and execution time.

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Publication Details

Journal
International Journal of Intelligent Systems
Published
2025-01-01
DOI
https://doi.org/10.1155/int/1009713
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
3.25
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article

A Container‐Based Cloud Broker for Effective Service Provisioning in Multicloud Environment

Rajganesh Nagarajan, P. Vinothiyalakshmi, Ramkumar Thirunavukarasu, J. Arun Pandian et al.
1 citations
International Journal of Intelligent Systems
Cloud Computing and Resource Management
3.25
article

A Container‐Based Cloud Broker for Effective Service Provisioning in Multicloud Environment

Rajganesh Nagarajan, P. Vinothiyalakshmi, Ramkumar Thirunavukarasu, J. Arun Pandian, Evans Kotei
article en
1 citations

Abstract

Container‐based cloud brokers are third‐party services that act as an intermediate entity between users and multiple cloud providers. The cloud brokers intended to perform discovery and provisioning of cloud services with an affordable pricing scheme. As cloud services can be provisioned on‐demand basis for multiple users, the cloud brokers are unable to provide the most suited services to the users on time. To address this issue, the proposed work introduces a novel approach for efficient cloud service provisioning by utilizing container‐based cloud service brokerage and implementing service arbitrage across various cloud providers. A microservice architecture‐based service discovery mechanism is developed which incorporates a service registry for tracking newly available services from the providers. Docker containers are employed to orchestrate the services, which ensures streamlined management and deployment of offered services. Further, the proposed system recommends and evaluates the services to the cloud users based on probability matrices, mapping matrices, and user feedback. The performance of the proposed model is compared with existing techniques, namely, rough multidimensional matrix (RMDM) and similarity‐enhanced hybrid group recommendation approach (HGRA). Experimental results show that the proposed model outperforms the existing models in terms of clustering accuracy and execution time.

International Journal of Intelligent SystemsVol. 2025(1)
Sri Venkateswara University (IN), Kumasi Technical University (GH), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 8%
Cloud Computing and Resource Management
3.25
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A Container‐Based Cloud Broker for Effective Service Provisioning in Multicloud Environment — Rajganesh Nagarajan, P. Vinothiyalakshmi, et al. · International Journal of Intelligent Systems (2025) | TGRS Research Map | TGRS