A Framework for Optimizing Energy Allocation and Sustainability in Cloud-Edge AI Systems

Cloud-edge computing and artificial intelligence proliferation make it necessary to learn how to properly manage energy in the environment. This paper will aim to design and assess a power trading framework on the possibility of adequately optimizing power allocation for cloud-edge AI systems. The problem that the paper seeks to address is the critical problem of energy acumen in cloud-edge AI. With the growing number of edge devices and the unpredictability of AI workload, power allocation must be effectively done. Therefore, the paper’s scope will include designing a power trading system that will effectively and dynamically allocate power resources in the AI to optimize and secure efficiency, effectiveness, reduce costs, and completely eliminate or reduce latencies. The research approach is wide and thorough. I conducted a collected theoretical framework, developed algorithms, acquired actual and credible real-world data, and analyzed big data. During this process, I conducted real-world simulations and deployed the framework in different case studies involving various cloud-edge AI environments. The results showed that power trading framework sufficiently optimizes power allocation, which has increased pyre efficacy, relatively reduced costs, and low latency. Implementing the framework will also secure sustainability ambitions, especially in reducing carbon emissions and depending less on nonrenewable energy sources.