<![CDATA[Resource allocation inside the cloud infrastructure is an essential requirement for better performance and cost-effectiveness. With workloads growing so much more complex and dynamic, we now need automated solutions that allow us to move past tedious, manual resource management. AI provides a unique paradigm for optimizing resource allocation in such complex and dynamic environments. In this paper, we have compared various AI techniques such as machine learning, evolutionary algorithms and deep reinforcement learning for the problem of resource allocation in cloud infrastructure. We analyze their effectiveness for dynamic resource provisioning to satisfy performance objectives while reducing operational expenditure. We offer an in-depth performance comparison using real-world datasets and simulations to showcase the merits and drawbacks of each. Implications: We believe that our findings can offer guidelines to cloud providers, researchers, and practitioners who are interested in making proactive improvements on managing cloud infrastructures using intelligent resource allocation techniques.]]>
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