Resource Demands and Nodes Availability in Telco Datacenter

In the contemporary digital landscape, telecommunication cloud services play a pivotal role in data management, connectivity, and service provision. Despite their significance, research on telco cloud environments often lacks comprehensive data on the characteristics of production workloads, essential for optimizing resource management systems. To address this gap, a comprehensive dataset is presented, capturing crucial information about application demands within telco datacenters. This dataset encompasses three distinct workload types: dynamic, static, and a hybrid mix, aiming to provide an understanding of application demands over time in telecommunication datacenters. Moreover, the dataset provides information about nodes availability (file nodes_allocatable). In the conducted experiments, the workloads trained machine learning algorithms to forecast resource demands. Subsequently, decision-making algorithms determined the optimal number of nodes required, aligning with anticipated pod demands.