Efficient resource allocation for generative AI workloads in cloud-native infrastructures: A multi-tiered approach

Abstract

Resource management becomes essential in ensuring that generative AI workloads in cloud-native infrastructures deliver the best results. The architecture described in this article targets such workloads due to their inherent fluctuations in resource usage and the difficulties in scaling them. The proposed framework divides resources into groups to guarantee that applications are given support based on difficulty level. The features of the proposed methodology are the performance assessment of resource distribution effectiveness, taking into account metrics, including latency, throughput, and utilization rates. Furthermore, examples have been provided to support the use of this approach and its efficiency in real-life situations. Based on these, applying the multi-tiered approach to resource management improves the organization's operations performance and minimizes expenses connected with resource provisioning. Such a study also emphasizes the importance of developing flexible and effective resource management tools that can be especially useful in modern generative AI development environments.