{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T08:30:56Z","timestamp":1769329856976,"version":"3.49.0"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":197,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2015"],"award-info":[{"award-number":["U21B2015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972300"],"award-info":[{"award-number":["61972300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202357"],"award-info":[{"award-number":["62202357"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017550","name":"Shaanxi Science and Technology Association","doi-asserted-by":"publisher","award":["20220113"],"award-info":[{"award-number":["20220113"]}],"id":[{"id":"10.13039\/501100017550","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>The rise of containerization has led to the development of container cloud technology, which offers container deployment and management services. However, scheduling a large number of containers efficiently remains a significant challenge for container cloud service platforms. Traditional load prediction methods and scheduling algorithms do not fully consider interdependencies between containers or fine\u2010grained resource scheduling, leading to poor resource utilization and scheduling efficiency. To address these challenges, this paper proposes a new load prediction model CNN\u2010BiGRU\u2010Attention and a container scheduling strategy based on load prediction. The prediction model CNN and BiGRU focus on the local features of load data and long sequence dependencies, respectively, as well as introduce the attention mechanism to make the model more easily capture the features of long distance dependencies in the sequence. A container scheduling strategy based on load prediction is also designed, which first uses the load prediction model to predict the load state and then generates a scheduling strategy based on the load prediction value to determine the change of the number of container replicas in a fine\u2010grained manner based on the load prediction value in the next time window, while the established domain\u2010based container selection method is employed to facilitate the coarse\u2010grained online migration of containers. Experiments conducted using public datasets and open\u2010source simulation platforms demonstrate that the proposed approach achieves a 37.4% improvement in container load prediction accuracy and a 21.7% improvement in container scheduling efficiency compared to traditional methods. These results highlight the effectiveness of the proposed approach in addressing the challenges faced by container cloud service platforms.<\/jats:p>","DOI":"10.1155\/2023\/5959223","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T22:50:10Z","timestamp":1689634210000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Efficient Load Prediction\u2010Driven Scheduling Strategy Model in Container Cloud"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8414-4164","authenticated-orcid":false,"given":"Lu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1887-8788","authenticated-orcid":false,"given":"Shuaidong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2185-3399","authenticated-orcid":false,"given":"Pengli","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0559-6287","authenticated-orcid":false,"given":"Haodong","family":"Yue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5259-7260","authenticated-orcid":false,"given":"Yaxiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4990-0491","authenticated-orcid":false,"given":"Chenyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7040-9062","authenticated-orcid":false,"given":"Zhuang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2859-9003","authenticated-orcid":false,"given":"Di","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"e_1_2_9_1_2","unstructured":"Canonical Ltd Linux containers 2023 https:\/\/linuxcontainers.org\/."},{"key":"e_1_2_9_2_2","unstructured":"ChenH. Cloud computing ecosystem report 2022 1\u2013244 https:\/\/comptiacdn.azureedge.net\/webcontent\/docs\/default-source\/research-reports\/research-brief-comptia-cloud-ecosystem.pdf?sfvrsn=2495b5a7_2."},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/spe.995"},{"key":"e_1_2_9_4_2","volume-title":"Apache Mesos Essentials","author":"Kakadia D.","year":"2015"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-2598-1_3"},{"key":"e_1_2_9_6_2","doi-asserted-by":"crossref","unstructured":"YeK. KouY. LuC. WangY. andXuC.-Z. Modeling application performance in docker containers using machine learning techniques Proceedings of the 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) December 2018 Singapore IEEE 1\u20136.","DOI":"10.1109\/PADSW.2018.8644581"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"ZhangD. YanB.-H. FengZ. ZhangC. andWangY.-X. Container oriented job scheduling using linear programming model Proceedings of the 2017 3rd International Conference on Information Management (ICIM) April 2017 Chengdu China IEEE 174\u2013180.","DOI":"10.1109\/INFOMAN.2017.7950370"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"MaoY. OakJ. AnthonyP. DanielB. TaoH. andPeizhaoH. Draps: dynamic and resource-aware placement scheme for docker containers in a heterogeneous cluster Proceedings of the 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC) December 2017 San Diego CA USA IEEE 1\u20138.","DOI":"10.1109\/PCCC.2017.8280474"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"KaewkasiC.andChuenmuneewongK. Improvement of container scheduling for docker using ant colony optimization Proceedings of the 2017 9th international conference on knowledge and smart technology (KST) March 2017 Chonburi Thailand IEEE 254\u2013259.","DOI":"10.1109\/KST.2017.7886112"},{"key":"e_1_2_9_10_2","first-page":"228","article-title":"An improved kubernetes scheduling algorithm based on load balancing","volume":"34","author":"Tan L.","year":"2019","journal-title":"Journal of Chengdu University of Information Technology"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"NandaS.andHackerT. J. Racc: resource-aware container consolidation using a deep learning approach Proceedings of the First Workshop on Machine Learning for Computing Systems May 2018 West Lafayette IN USA 1\u20135.","DOI":"10.1145\/3217871.3217876"},{"key":"e_1_2_9_12_2","first-page":"133","article-title":"Fault prediction algorithm based on particle filter and linear autoregressive models","volume":"21","author":"Xue D.","year":"2011","journal-title":"Computer technology and development"},{"key":"e_1_2_9_13_2","first-page":"4711","article-title":"Load balancing algorithm based on load weights","volume":"29","author":"Wei Q.","year":"2012","journal-title":"Computer Application Research"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40092-014-0075-5"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcc.2014.2350475"},{"key":"e_1_2_9_16_2","first-page":"239","article-title":"A prediction method for mid-long term load forecasting using big data technology","volume":"50","author":"Chen Q.","year":"2017","journal-title":"Journal of Wuhan University (Natural Science Edition)"},{"key":"e_1_2_9_17_2","first-page":"230","article-title":"Dailyload curve forecastingbyusingk-modes clustering algorithm under the framework of mapreduce","volume":"44","author":"Wang Y.","year":"2016","journal-title":"Computer and Digital Engineering"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2011.05.027"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"QiuF. ZhangB. andGuoJ. A deep learning approach for vm workload prediction in the cloud Proceedings of the 2016 17th IEEE\/ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel\/Distributed Computing (SNPD) May 2016 Shanghai China IEEE 319\u2013324.","DOI":"10.1109\/SNPD.2016.7515919"},{"key":"e_1_2_9_20_2","unstructured":"AshrafA. S. Automatic cloud resource scaling algorithm based on long short-term memory recurrent neural network 2017 https:\/\/arxiv.org\/ftp\/arxiv\/papers\/1701\/1701.03295.pdf."},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"GuoJ. WuJ. NaJ. andZhangB. A type-aware workload prediction strategy for non-stationary cloud service Proceedings of the 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA) November 2017 Kanazawa Japan IEEE 98\u2013103.","DOI":"10.1109\/SOCA.2017.21"},{"key":"e_1_2_9_22_2","first-page":"319","volume-title":"Languages, Compilers, and Run-Time Systems for Scalable Computers","author":"Peter A.","year":"1998"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"InagakiT. UedaY. andOharaM. Container management as emerging workload for operating systems Proceedings of the 2016 IEEE International Symposium on Workload Characterization (IISWC) September 2016 Providence RI USA 1\u201310.","DOI":"10.1109\/IISWC.2016.7581267"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"ShangW. LiuD. ZhuL. andFengD. An improved dynamic load-balancing model Proceedings of the 2016 4th Intl Conf on Applied Computing and Information Technology\/3rd Intl Conf on Computational Science\/Intelligence and Applied Informatics\/1st Intl Conf on Big Data Cloud Computing Data Science & Engineering (ACIT-CSII-BCD) December 2016 Las Vegas NV USA 337\u2013341.","DOI":"10.1109\/ACIT-CSII-BCD.2016.071"},{"key":"e_1_2_9_25_2","unstructured":"HuZ.-J. A qos-oriented resource availability evaluation model in computational grids 2010 https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0164121215001715."},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.3724\/sp.j.1001.2013.04364"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-010-0145-4"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1186\/s41256-022-00274-y"},{"key":"e_1_2_9_29_2","unstructured":"LuoL. Research on container elastic scaling technology based on load prediction 2021 Wuhan Textile University Wuhan China M.Sc. thesis."},{"key":"e_1_2_9_30_2","unstructured":"GuoY. Research and implementation of docker container scheduling strategy in microservice environment 2018 Beijing University of Posts and Telecommunications Beijing China Ph.D. thesis."},{"key":"e_1_2_9_31_2","unstructured":"WuS. Research on docker container scheduling optimization method 2019 Zhengzhou University Zhengzhou China M.Sc. thesis."},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/2890784"},{"key":"e_1_2_9_33_2","doi-asserted-by":"crossref","unstructured":"SunY. Jacobus van WykB. andWangZ. A new multi-swarm multi-objective particle swarm optimization based on pareto front set 7 Proceedings of the Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: 7th International Conference ICIC 2011 August 2011 Zhengzhou China Springer 203\u2013210.","DOI":"10.1007\/978-3-642-25944-9_27"},{"key":"e_1_2_9_34_2","doi-asserted-by":"crossref","unstructured":"KimS.-H. LeeG. HongI. KimY.-J. andKimD. New potential functions for multi robot path planning: swarm or spread 2 Proceedings of the 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE) April 2010 Singapore IEEE 557\u2013561.","DOI":"10.1109\/ICCAE.2010.5451658"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/5959223.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/5959223.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/5959223","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:33:40Z","timestamp":1735623220000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/5959223"}},"subtitle":[],"editor":[{"given":"Mohammad R.","family":"Khosravi","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/5959223"],"URL":"https:\/\/doi.org\/10.1155\/2023\/5959223","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2023-05-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5959223"}}