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. 2020 Aug 21;21(Suppl 10):353. doi: 10.1186/s12859-020-03562-x

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 4 — Unbalanced vs. balanced workload. Examples of the impact of different intelligent interval divisions on a system’s workload balance: when a naive interval division is employed, the system potentially suffers from unbalanced workload (a) which is instead overcome by our Dynamic Interval Analysis thanks to which the workload skyline becomes more homogeneous and all processes gain in fairness (b)