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Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    Cloud Workload Prediction (CWP) is a critical task in cloud computing, essential for resource scheduling, performance optimization, and cost management. However, existing time series prediction methods struggle with instability and inefficiency when applied directly to cloud workloads due to their high variability and frequent fluctuations. To address these challenges, we propose DEL4CW, a novel\n                    <jats:italic toggle=\"yes\">D<\/jats:italic>\n                    eep\n                    <jats:italic toggle=\"yes\">E<\/jats:italic>\n                    xpansion\n                    <jats:italic toggle=\"yes\">L<\/jats:italic>\n                    earning framework specifically designed for\n                    <jats:italic toggle=\"yes\">CWP<\/jats:italic>\n                    . DEL4CW introduces a unique self-decoupling mechanism to disentangle the complex dependencies present in highly variable cloud workloads, leading to more accurate predictions of job arrival rates. The core contribution of DEL4CW lies in its ability to decouple cloud workload signals into three key components\u2014trend, periodicity, and residuals\u2014by treating these as hidden variables. This enables the model to better manage both short-term fluctuations and long-term workload trends. DEL4CW employs a deep expansion learning framework structured as stacked blocks, where each block includes dedicated modules for trend, periodicity, and compensation. Specifically, the trend module utilizes multi-layer fully connected networks to capture evolving trends at multiple granularities, while the periodicity module leverages multi-head attention to identify diverse periodic patterns. The compensation module addresses unpredictable, localized fluctuations, improving the model\u2019s robustness to noise. In addition to its predictive accuracy, DEL4CW provides interpretable insights through its hierarchical design, allowing for layer-by-layer aggregation of meaningful partial predictions. This interpretability stems from the doubly residual learning pipeline, which ensures that each prediction block contributes progressively refined predictions. Extensive experiments on real-world cloud workload traces demonstrate that DEL4CW significantly outperforms existing baselines, with error reductions reaching up to 27.74% in certain scenarios.\n                  <\/jats:p>","DOI":"10.1145\/3797952","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:08:29Z","timestamp":1771513709000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DEL4CW: Deep Expansion Learning for Cloud Workloads Prediction"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4267-7795","authenticated-orcid":false,"given":"Xiaoyu","family":"Shi","sequence":"first","affiliation":[{"name":"Chongqing Key Laboratory of Edge Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7675-7025","authenticated-orcid":false,"given":"Qiuyue","family":"Lv","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1846-6655","authenticated-orcid":false,"given":"Bingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-7210","authenticated-orcid":false,"given":"Hong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7024-2270","authenticated-orcid":false,"given":"Mingsheng","family":"Shang","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"12433","DOI":"10.1609\/aaai.v36i11.21509","article-title":"Wasserstein adversarial transformer for cloud workload prediction","author":"Arbat Shivani","year":"2022","unstructured":"Shivani Arbat, Vinodh Kumaran Jayakumar, Jaewoo Lee, Wei Wang, and In Kee Kim. 2022. 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