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As we approach the exascale era, the size and complexity of HPC systemscontinues to increase, raising concerns about their manageability andsustainability. For this reason, more and more HPC centers are experimentingwith fine-grained monitoring coupled with Operational Data Analytics (ODA) tooptimize efficiency and effectiveness of system operations. However, whilemonitoring is a common reality in HPC, there is no well-stated andcomprehensive list of requirements, nor matching frameworks, to supportholistic and online ODA. This leads to insular ad-hoc solutions, eachaddressing only specific aspects of the problem.  In this paper we propose Wintermute, a novel generic framework to enableonline ODA on large-scale HPC installations. Its design is based on the resultsof a literature survey of common operational requirements. We implementWintermute on top of the holistic DCDB monitoring system, offering a largevariety of configuration options to accommodate the varying requirements of ODAapplications. Moreover, Wintermute is based on a set of logical abstractions toease the configuration of models at a large scale and maximize code re-use. Wehighlight Wintermute's flexibility through a series of practical case studies,each targeting a different aspect of the management of HPC systems, and thendemonstrate the small resource footprint of our implementation.

DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems