Understanding multi-cloud platform: innovative AI-assisted trust-aware resource allocation technique - Vaishnav, Jaimine (Springer Nature, 2025)
Resource allocation in a cloud context is a major challenge in terms of cost and quality optimization. To enhance customer assistance, cloud service providers (CSPs) need to consider quality-related factors. This study addresses this gap by integrating both trust and delay into a resource allocation approach supported by artificial intelligence (AI). By considering factors such as availability, effectiveness, stability, and data accuracy, CSP trustworthiness can be evaluated. The aim is to minimize communication delay while maximizing trust in resource allocation through a dynamic trust-aware intelligent water drop (DTIWD) optimization method. The DTIWD method is suggested utilizing a range of features that provide adaptability to different services. The method dynamically allocates resources based on the CSP’s trust attributes, promoting flexibility across different service requirements. Experimental results show that the DTIWD method achieves optimal performance, proving effective in maintaining resource allocation across metrics such as availability, data integrity, and time efficiency. When tested under different workloads, the approach demonstrates high adaptability, ensuring reliable service even with varying loads. The method also shows significant improvement in time efficiency, maintaining data integrity while effectively managing resources for different availability demands. In addition, by allocating resources effectively, incorporating trust into the resource allocation framework contributes to improved balanced weighting between trust (0.5) and delay (0.5) yields improved scalability and interoperability a reduction in communication delays during the selection procedure. Future work could explore the integration of blockchain technology to further enhance trust management and scalability in multi-cloud resource allocation systems.
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