{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T05:56:00Z","timestamp":1725688560900},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"\n Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the\n Elastic Compute Service<\/jats:italic>\n (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high\n uncertainty<\/jats:italic>\n and\n scale-sensitive<\/jats:italic>\n temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks.\n <\/jats:p>\n \n To address the above challenges, we propose a novel predictive autoscaling framework\n MagicScaler<\/jats:italic>\n , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of\n MagicScaler<\/jats:italic>\n , which outperforms other commonly adopted scalers, thus justifying our design choices.\n <\/jats:p>","DOI":"10.14778\/3611540.3611566","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T15:32:37Z","timestamp":1694791957000},"page":"3808-3821","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["MagicScaler: Uncertainty-Aware, Predictive Autoscaling"],"prefix":"10.14778","volume":"16","author":[{"given":"Zhicheng","family":"Pan","sequence":"first","affiliation":[{"name":"East China Normal University and Alibaba Group"}]},{"given":"Yihang","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University and Alibaba Group"}]},{"given":"Yingying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Sean Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"Aalborg University"}]},{"given":"Yunyao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Aalborg University"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Chenjuan","family":"Guo","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Qingsong","family":"Wen","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Xiduo","family":"Tian","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Yunliang","family":"Dou","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Zhiqiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Chengcheng","family":"Yang","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Aoying","family":"Zhou","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"East China Normal University"}]}],"member":"320","published-online":{"date-parts":[[2023,9,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2020.2995937"},{"key":"e_1_2_1_2_1","volume-title":"Learning Predictive Autoscaling Policies for Cloud-Hosted Microservices Using Trace-Driven Modeling. 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