A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics

Rajganesh Nagarajan, Vinothiyalakshmi Palanichamy, Ramkumar Thirunavukarasu and J. Arun Pandian ()
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Rajganesh Nagarajan: Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India
Vinothiyalakshmi Palanichamy: Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India
Ramkumar Thirunavukarasu: School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
J. Arun Pandian: School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India

Future Internet, 2025, vol. 17, issue 8, 1-15

Abstract: Cloud computing offers a broad spectrum of services to users, particularly in multi-cloud environments where service-centric features are introduced to support users from multiple endpoints. To improve service availability and optimize the utilization of required services, cloud brokerage has been integrated into multi-cloud systems. The primary objective of a cloud broker is to ensure the quality and outcomes of services offered to customers. However, traditional cloud brokers face limitations in measuring service trust, ensuring validity, and anticipating future enhancements of services across different cloud platforms. To address these challenges, the proposed intelligent cloud broker integrates an intelligence mechanism that enhances decision-making within a multi-cloud environment. This broker performs a comprehensive validation and verification of service trustworthiness by analyzing various trust factors, including service response time, sustainability, suitability, accuracy, transparency, interoperability, availability, reliability, stability, cost, throughput, efficiency, and scalability. Customer feedback is also incorporated to assess these trust factors prior to service recommendation. The proposed model calculates service ranking (SR) values for available cloud services and dynamically includes newly introduced services during the validation process by mapping them with existing entries in the Service Collection Repository (SCR). Performance evaluation using the Google cluster-usage traces dataset demonstrates that the ICB outperforms existing approaches such as the Clustering-Based Trust Degree Computation (CBTDC) algorithm and the Service Context-Aware QoS Prediction and Recommendation (SCAQPR) model. Results confirm that the ICB significantly enhances the effectiveness and reliability of cloud service recommendations for users.

Keywords: cloud broker; cloud service; service registry; service ranking; service recommendation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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