Adaptive AI-Based Resource Management for Automated Enterprise Data Processing and Cloud Infrastructure

Abstract

With the rapid growth in size, speed and complexity of enterprise data; organizations have had difficulty managing their cloud infrastructure efficiently, scalable and cost effectively. Organizations commonly use static configuration settings, threshold limits and reactive scaling policies to manage resources. However, these methods are often inadequate when dealing with dynamic workloads, unpredictable resource requirements and demanding service level agreements. In this paper, we propose a conceptual model of adaptive artificial intelligence (AI) based resource management. This model incorporates workload prediction, intelligent resource assignment, automated data pipeline optimization, and ongoing performance monitoring throughout the entire cloud infrastructure. Our model incorporates machine learning based workload forecasting with reinforcement learning based scheduling and constraint aware optimization to enable dynamic decision making regarding compute capacity, memory allocation, storage usage, network bandwidth, and data processing priorities. Additionally, our model supports coordination among enterprise data ingestion, transformation, analytics, and storage processes in a multi-cloud environment. We utilize a design oriented research methodology to define our conceptual model, determine the primary components of our model, establish evaluation metrics, and conduct a comparative analysis of our model compared to traditional resource management techniques. Our analysis indicates that adaptive resource management provides a coherent methodology for optimizing operational costs, processing latencies, resource utilization, energy consumption and workload reliability. Nonetheless, the success of our model is dependent upon the accuracy of predictions, orchestration overhead, workload characteristics, security controls and the quality of operational data. Because our research is conceptual in nature and does not report on a deployed experimental system, we do not claim any quantitative performance improvement.