Optimising AI Workload Distribution in Multi-Cloud Environments: A Dynamic Resource Allocation Approach
DOI:
https://doi.org/10.5281/zenodo.13863194ARK:
https://n2t.net/ark:/40704/JIEAS.v2n5a10References:
40Keywords:
Multi-cloud Computing, AI Workload Optimisation, Dynamic Resource Allocation, Energy-efficient ComputingAbstract
This research presents a new resource allocation method for optimising AI task distribution in multi-cloud environments. The proposed approach addresses the challenges of managing complex AI operations across different environments, focusing on improving resource efficiency, energy efficiency, and financial efficiency. The framework includes advanced machine learning techniques, including performance measurement and performance prediction, multi-dimensional monitoring and profiling, decision-based adaptive support learning, and data transfer in different clouds.
The experimental results show a significant improvement over existing solutions, with a 9.8% increase in average resource utilisation and a 21% reduction in task completion time. Even when measured for 5000 VMs, the framework performs well, showing exceptional scalability and robustness. A cost-benefit analysis shows a 30.6% reduction in Total Cost of Ownership over a simulated 3-year period and a 30.5% reduction in energy and gas consumption—carbon emissions.
The research findings have significant implications for climate control AI in many areas, providing insight into strategies for optimising operations and energy efficiency and improving environmental trust. The proposed framework represents a paradigm shift in the cloud, providing a blueprint for next-generation AI infrastructure that can adapt to the evolving needs of complex AI applications while supporting business stability and effectiveness.
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