EXASAGE: The first data center operational data analysis assistant
Junaid Ahmed Khan, Martin Molan, Andrea Giorgio Bartolini · Future Generation Computer Systems · 2025
• We propose EXASAGE, the first ODA operational data analysis assistant for data centers. To the best of our knowledge, this is the first prototype of a Large Language Model (LLM)-based tool that provides an AI-driven interoperable layer designed to interact with data collected at data center facilities, serving as an on-demand data access assistant that generates graph database query codes for timely, non-critical operational analysis. • The proposed framework leverages a Knowledge Graph (KG) approach instead of a standard NoSQL database at a data center. To achieve this, we provide a formal representation of the data collected at the data center using a Resource Description Framework (RDF) ontology. • We evaluated the framework in a real-world setting using 1,000 complex queries representative of the daily tasks performed by facility managers and engineers. The framework achieved a 93.6% accuracy for correctly generated and executed graph queries, compared to only 25% accuracy for standard NoSQL query generation, demonstrating the benefits of combining LLMs and KG. • We address the significant storage challenges caused by time-series data conversion into a KG, which results in a storage size increase of than 745x compared to NoSQL database storage, using virtualization of KGs. This results in a max storage overhead of just 52.62 MiB over all the 1000 user input queries. Data centers increasingly depend on Operational Data Analytics (ODA) for real-time insights from vast streams of telemetry data. They typically utilize NoSQL databases for scalability and data diversity, which leads to unstructured data representation and presents significant challenges for the data interoperability. Indeed, the lack of standardization, combined with schema flexibility and complex data structures, makes it difficult for system administrators to write and execute queries, ultimately complicating the automation of data retrieval tasks. Pre-trained Large Language Models (LLMs), with their latent knowledge, promise a ready-to-use AI-driven data interoperability layer, enabling data retrieval through natural language input. However, they often generate inaccurate or hallucinated query code when handling heterogeneous data sources and complex data structures. In this paper we present EXASAGE, the first ODA operational data analysis assistant that leverages a Knowledge Graph (KG)-based approach, addressing these LLM limitations and simplifying data retrieval tasks in data center facilities through a prototype implementation. EXASAGE employs an LLM based query generator as an interoperable layer to convert natural language into SPARQL queries (native to KGs), executed at a graph database endpoint, along with a virtual KG approach that retrieves only the data relevant to the user input query. In evaluations on 1,000 user input queries, EXASAGE achieved a 93.6% accuracy in generating correct SPARQL code and retrieving correct answers, significantly outperforming the 25% accuracy of NoSQL/SQLite queries, which frequently exhibited hallucinations. Furthermore, SPARQL queries are generally more concise and demonstrate shorter inference and execution times compared to compared to NoSQL/SQLite queries. For EXASAGE, the average end-to-end time for a single execution cycle is 12.77 seconds, which is suitable for interactive, non-critical operational data analysis tasks. The maximum observed storage overhead across all generated virtual KGs is just 52.62 MiB.