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Then, the SVM and BP neural network was simulated and analyzed in MATLAB software and compared with SVM, BP and radial basis function (RBF) prediction models. The results showed that the average error of the SVM and BP based model was 0.670%, and the average error of SVM, BP and RBF was 0.781%, 0.759% and 0.708%, respectively; in the multi-step prediction, the prediction accuracy of SVM, BP, RBF and SVM\u200a+\u200aBP in the first step was 89.3%, 94.6%, 96.3% and 98.5%, respectively, the second step was 87.4%, 93.1%, 95.2% and 97.8%, respectively, the third step was 83.5%, 90.3%, 93.1% and 95.7%, the fourth step was 79.1%, 87.4%, 90.5% and 93.2%, respectively, the fifth step was 75.3%, 81.3%, 85.9% and 91.1% respectively, and the sixth step was 71.1%, 76.6%, 82.1% and 89.4%, respectively.<\/jats:p>","DOI":"10.3233\/jifs-191266","type":"journal-article","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T10:06:07Z","timestamp":1602237967000},"page":"2861-2867","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Workload prediction of cloud computing based on SVM and BP neural networks"],"prefix":"10.1177","volume":"39","author":[{"given":"Qiong","family":"Sun","sequence":"first","affiliation":[{"name":"Management College of Beijing Union University, Beijing, China"},{"name":"Beijing Technology And Business University, Beijing, China"}]},{"given":"Zhiyong","family":"Tan","sequence":"additional","affiliation":[{"name":"Beijing Open University, Beijing, China"}]},{"given":"Xiaolu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Management College of Beijing Union University, Beijing, China"}]}],"member":"179","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/dac.3308"},{"key":"e_1_3_1_3_2","first-page":"1","article-title":"Optimal Scheduling of VMs in Queueing Cloud Computing Systems with a Heterogeneous Workload","author":"Guo M.","year":"2018","unstructured":"GuoM., GuanQ. and KeW., Optimal Scheduling of VMs in Queueing Cloud Computing Systems with a Heterogeneous Workload, IEEE Access (2018), 1\u20131.","journal-title":"IEEE Access"},{"key":"e_1_3_1_4_2","first-page":"1","article-title":"An Effective Classification-based Framework for Predicting Cloud Capacity Demand in Cloud Services","author":"Xia B.","year":"2018","unstructured":"XiaB., TaoL., ZhouQ.F. and LiQ., An Effective Classification-based Framework for Predicting Cloud Capacity Demand in Cloud Services, IEEE Transactions on Services Computing (2018), 1\u20131.","journal-title":"IEEE Transactions on Services Computing"},{"key":"e_1_3_1_5_2","first-page":"2139","article-title":"GA-SVM modeling of multiclass seizure detector in epilepsy analysis system using cloud computing, Soft Computing-A Fusion of Foundations","volume":"21","author":"Shen C.P.","year":"2017","unstructured":"ShenC.P., LinJ.W., LinF.S. and LamA.Y-Y., GA-SVM modeling of multiclass seizure detector in epilepsy analysis system using cloud computing, Soft Computing-A Fusion of Foundations, Methodologies and Applications 21 (2017), 2139\u20132149.","journal-title":"Methodologies and Applications"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2015.09.011"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.12.087"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-015-1520-y"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2017.12.012"},{"key":"e_1_3_1_10_2","first-page":"1","article-title":"A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine","author":"Wei Z.","year":"2018","unstructured":"WeiZ., YiZ., JianS. and JingG., A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine, Applied Intelligence (2018), 1\u201312.","journal-title":"Applied Intelligence"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"HongC. 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