AI-Driven Predictive Analytics for Optimizing Resource Utilization in Edge-Cloud Data Centers

Over the last several years edge-cloud data centers have emerged as one of the key infrastructures of contemporary computing enabling real-time computational intensive applications in IoT, Artificial Intelligence, and 5G networks. However, these hybrid environment markers are challenged by issues of resource allocation; they include variability of workloads, resource partitioning, high energy consumption and low operational efficiency. But static and passive methods of resource management are not efficient enough to address the requirements of such systems. These concerns are resolved with AI-driven predictive analytics, which presents the opportunity to predict a company’s resources necessity and further allocate those resources in real-time. In combination with machine learning and deep learning approaches, predictive analytics can calculate workload accurately, optimize energy consumption and identify potential failures at an early stage, which will guarantee efficiency and savings. In this article, the authors discuss the use of predictive analytics in edge-cloud environments, with an emphasis on how this technology facilitates a balance between PUE, throughput, and total power consumption, as well as overall edge-cloud system robustness. Using various AI Informed methods supported by the real-life examples and discussing the technical environments for intelligent technologies, the research also reveals the prospects and issues of the application of such AI systems, such as data heterogeneity, privacy, and scalability. Last but not the least, the discussion indicates that more promising paradigms like federated learning as well as Green-computing principles which suggest future resolution towards the green application of optimal resource utilization in edge cloud intricacies.