Optimization of Generative AI Costs in Multi-Agent and Multi-Cloud Systems

Sankara Reddy Thamma

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 6, 953-965

Abstract: The generative AI system is being adopted across the several fields to provide novel solutions for text generation, image synthesis, and decision-making. But when they are used in multi-agent and multi-cloud systems, they are expensive in terms of computation and finance. Regarding the aforementioned factors, this paper aims to examine methods of reducing such costs while achieving system efficiency. Such measures as dynamic workload distribution, resource scaling, as well as cost-conscious model selection is described. Through the examples of case studies and simulations, we show that incorporating these strategies can drastically decrease expenses and ensure immediate and accurate scalability across clouds of different ecosystems.

Keywords: Generative AI; Cost Optimization; Multi-Agent Systems; Multi-Cloud Environments; Resource Scaling; Dynamic Workload Distribution (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i6:id:540

DOI: 10.32628/IJSRST241161186

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