A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network

[2308.01917] A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2308.01917 (cs) [Submitted on 11 Jul 2023 ( v1 ), last revised 11 Apr 2026 (this version, v3)] Title: A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network Authors: Feiyi Chen , Naijin Liu , Zhen Qin , Hailiang Zhao , Mengchu Zhou , Shuiguang Deng View a PDF of the paper titled A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network, by Feiyi Chen and 5 other authors View PDF HTML (experimental) Abstract: Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional workload bursts, which makes workload prediction challenging. The time series forecasting methods relying on periodicity information, often assume fixed and known periodicity length, which does not align with the periodicity-changeable nature of cloud service workloads. Although many state-of-the-art time-series forecasting methods do not rely on periodicity information and achieve high overall accuracy, they are vulnerable to data imbalance between heavy workloads and regular workloads. As a result, their prediction accuracy on rare heavy workloads is limited. Unfortunately, heavyload-prediction accuracy is more important than overall one, as errors in heavyload prediction are more likely to cause Service Level Agreement violations than errors in normal-load prediction. Thus, we propose a changeable-periodicity-perceived workload prediction network (PePNet) to fuse periodic information adaptively for periodicity-changeable time series and improve rare heavy workload prediction accuracy. It has two distinctive characteristics: (i) A Periodicity-Perceived Mechanism to detect the periodicity length automatically and fuses periodic information adaptively, which is suitable for periodicity-changeable time series, and (ii) An Achilles' Heel Loss Function that is used to iteratively optimize the most under-fitting part in predicting sequence for each step, thus evidently improving the prediction accuracy of heavy load. Extensive experiments conducted on real-world datasets demonstrate that PePNet improves accuracy for overall workload by 11.8% averagely, compared with state-of-the-art methods. Especially, PePNet improves accuracy for heavy workload by 21.0% averagely. Comments: Submitted to TII 2026 Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) ; Machine Learning (cs.LG) Cite as: arXiv:2308.01917 [cs.DC] (or arXiv:2308.01917v3 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2308.01917 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Feiyi Chen [ view email ] [v1] Tue, 11 Jul 2023 07:56:27 UTC (2,465 KB) [v2] Sun, 28 Apr 2024 12:23:38 UTC (1,640 KB) [v3] Sat, 11 Apr 2026 09:25:32 UTC (917 KB) Full-text links: Access Paper: View a PDF of the paper titled A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network, by Feiyi Chen and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2023-08 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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