Scalable Cloud Architectures for Real-Time AI: Dynamic Resource Allocation for Inference Optimization

Abstract

As the demand for Artificial Intelligence applications continues to grow across industries, the need for scalable and flexible cloud architectures has become more pronounced. AI workloads, characterized by diverse resource demands, unpredictable traffic patterns, and fluctuating computational requirements, require cloud architectures capable of dynamically adapting to changing conditions. Traditional static cloud resource allocation models often fail to meet the performance and cost-efficiency needs of AI-driven applications. This work explores the concept of dynamic scaling in cloud architectures and its potential to optimize AI workload performance through adaptive resource allocation. The importance of elastic scaling, auto-scaling mechanisms, and predictive analytics for anticipating workload demands is highlighted. Additionally, the use of containerization, serverless computing, and multi-cloud environments in enhancing the flexibility and efficiency of AI workloads is examined. Through an assessment of various techniques and models, a framework for adaptive cloud architectures is proposed that can optimize resource utilization, reduce operational costs, and improve the overall performance of AI applications.