Enabling Operational Data Analytics for Datacenters through Ontologies, Monitoring, and Simulation-based Prediction

Abstract

Datacenters are key components in the ICT infrastructure supporting our digital society. Datacenter operations are hampered by operational complexity and dynamics, risking to reduce or even offset the performance, energy efficiency, and other datacenter benefits. A promising emerging technology, Operational Data Analytics∼(ODA), promises to collect and use monitoring data to improve datacenter operations. However, it is challenging to organize, share, and leverage the massive and heterogeneous data resulting from monitoring datacenters. Addressing this combined challenge, starting from the idea that graphs could provide a good abstraction, in this work we present our early work on designing and implementing a graph-based approach for datacenter ODA. We focus on two main components of datacenter ODA. First, we design, implement, and validate agraph-based ontology for datacenters that captures both high-level meta-data information and low-level metrics of operational data collected from real-world datacenters, and maps them to a graph structure for better organization and further use. Second, we design and implementODAbler, a software framework for datacenter ODA, which combines ODA data with an online simulator to make predictions about current operational decisions and other what-if scenarios. We take the first steps to illustrate the practical use of ODAbler, and explore its potential to support datacenter ODA through graph-based analysis. Our work helps construct the case that graph-based ontologies have great value for datacenter ODA and, further, to improving datacenter operations.