Database Workload Capacity Planning using Time Series Analysis and Machine Learning

Antony Higginson, Mihaela Dediu, Octavian Arsene, Norman Paton, Suzanne Embury

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


When procuring or administering any I.T. system or a component of an I.T. system, it is crucial to understand the computational resources required to run the critical business functions that are governed by any Service Level Agreements. Predicting the resources needed for future consumption is like looking into the proverbial crystal ball. In this paper we look at the forecasting techniques in use today and evaluate if those techniques are applicable to the deeper layers of the technological stack such as clustered database instances, applications and groups of transactions that make up the database workload. The approach has been implemented to use supervised machine learning to identify traits such as reoccurring patterns, shocks and trends that the workloads exhibit and account for those traits in the forecast. An experimental evaluation shows that the approach we propose reduces the complexity of performing a forecast, and accurate predictions have been produced for complex workloads.
Original languageEnglish
Title of host publicationACM SIGMOD, 2020
Publication statusAccepted/In press - 31 Jan 2020


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