Abstract: A system for recommendation and optimization of information technology (IT) server resources is disclosed. The system includes a server comprising at least one processor configured to access input datasets associated with an IT workload, determine types and counts of server resources based on predefined criteria and a server consolidation configuration, and access a multi-server performance database. The processor utilizes a trained deep learning model to predict infrastructure requirements and generate recommendations for an optimal server configuration by balancing performance, power consumption, and resource utilization. The processor automatically allocates server resources from multiple manufacturers based on the recommendations and generates data for display on a user interface dashboard. The dashboard presents server utilization patterns, recommended configurations, and real-time performance metrics of allocated resources.

Abstract: A system for recommending and optimizing artificial intelligence (AI) workload placement in a multi-vendor cloud environment is disclosed. The system utilizes a server with at least one processor to access input datasets stored in a datacentre associated with an AI workload and determine suitable processing units from various manufacturers based on predefined criteria. It calculates the required number of processing units and accesses a multi-vendor performance database. A deep learning model predicts infrastructure requirements for the AI workload, enabling the generation of recommendations for optimal processing unit configuration. The system automatically allocates processing unit resources from multiple manufacturers based on these recommendations. Finally, it generates data for a user interface dashboard, presenting information about manufacturers, processing unit types, recommended configurations, and real-time performance metrics of allocated resources.