{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:51:30Z","timestamp":1773481890083,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,7]]},"DOI":"10.1145\/3540250.3558962","type":"proceedings-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T20:46:22Z","timestamp":1668026782000},"page":"1555-1565","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Industry practice of configuration auto-tuning for cloud applications and services"],"prefix":"10.1145","author":[{"given":"Runzhe","family":"Wang","sequence":"first","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinglong","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxi","family":"Hu","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heyuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Central South University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuheng","family":"Shen","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhan","sequence":"additional","affiliation":[{"name":"Central South University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Fu","sequence":"additional","affiliation":[{"name":"Ant Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Alibaba Group, China \/ Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohai","family":"Shi","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064029"},{"key":"e_1_3_2_1_2_1","unstructured":"Alexey Kopytov. 2017. sysbench. https:\/\/github.com\/akopytov\/sysbench \t\t\t\t  Alexey Kopytov. 2017. sysbench. https:\/\/github.com\/akopytov\/sysbench"},{"key":"e_1_3_2_1_3_1","unstructured":"Alibaba. 2019. Alibaba Cloud Linux. https:\/\/github.com\/alibaba\/cloud-kernel \t\t\t\t  Alibaba. 2019. Alibaba Cloud Linux. https:\/\/github.com\/alibaba\/cloud-kernel"},{"key":"e_1_3_2_1_4_1","unstructured":"Alibaba Group. 2015. ECS. https:\/\/www.alibabacloud.com\/product\/ecs \t\t\t\t  Alibaba Group. 2015. ECS. https:\/\/www.alibabacloud.com\/product\/ecs"},{"key":"e_1_3_2_1_5_1","unstructured":"Alibaba Group. 2018. ACK. https:\/\/www.alibabacloud.com\/product\/kubernetes \t\t\t\t  Alibaba Group. 2018. ACK. https:\/\/www.alibabacloud.com\/product\/kubernetes"},{"key":"e_1_3_2_1_6_1","volume-title":"Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang.","author":"Alipourfard Omid","year":"2017","unstructured":"Omid Alipourfard , Hongqiang Harry Liu , Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang. 2017 . CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. In NSDI. USENIX Association , 469\u2013482. Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang. 2017. CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. In NSDI. USENIX Association, 469\u2013482."},{"key":"e_1_3_2_1_7_1","unstructured":"James Bergstra R\u00e9mi Bardenet Yoshua Bengio and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for Hyper-Parameter Optimization. In NIPS. 2546\u20132554. \t\t\t\t  James Bergstra R\u00e9mi Bardenet Yoshua Bengio and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for Hyper-Parameter Optimization. In NIPS. 2546\u20132554."},{"key":"e_1_3_2_1_8_1","volume-title":"Cox","author":"Bergstra James","year":"2013","unstructured":"James Bergstra , Daniel Yamins , and David D . Cox . 2013 . Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In ICML (1) (JMLR Workshop and Conference Proceedings , Vol. 28). JMLR.org, 115\u2013 123 . James Bergstra, Daniel Yamins, and David D. Cox. 2013. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In ICML (1) (JMLR Workshop and Conference Proceedings, Vol. 28). JMLR.org, 115\u2013123."},{"key":"e_1_3_2_1_9_1","unstructured":"Canonical. 2004. The ubuntu Project. https:\/\/ubuntu.com \t\t\t\t  Canonical. 2004. The ubuntu Project. https:\/\/ubuntu.com"},{"key":"e_1_3_2_1_10_1","unstructured":"Centos. 2019. The CentOS Project. https:\/\/www.centos.org \t\t\t\t  Centos. 2019. The CentOS Project. https:\/\/www.centos.org"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In KDD. ACM 785\u2013794. \t\t\t\t  Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In KDD. ACM 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_12_1","unstructured":"Debian. 1997. The Debian Project. https:\/\/www.debian.org \t\t\t\t  Debian. 1997. The Debian Project. https:\/\/www.debian.org"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687767"},{"key":"e_1_3_2_1_14_1","unstructured":"Hewlett Packard. 2005. netperf. https:\/\/github.com\/HewlettPackard\/netperf \t\t\t\t  Hewlett Packard. 2005. netperf. https:\/\/github.com\/HewlettPackard\/netperf"},{"key":"e_1_3_2_1_15_1","unstructured":"Huawei. 2021. A-Tune. https:\/\/gitee.com\/openeuler\/A-Tune \t\t\t\t  Huawei. 2021. A-Tune. https:\/\/gitee.com\/openeuler\/A-Tune"},{"key":"e_1_3_2_1_16_1","volume-title":"Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates","author":"Ilievski Ilija","unstructured":"Ilija Ilievski , Taimoor Akhtar , Jiashi Feng , and Christine Annette Shoemaker . 2017. Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates . In AAAI. AAAI Press , 822\u2013829. Ilija Ilievski, Taimoor Akhtar, Jiashi Feng, and Christine Annette Shoemaker. 2017. Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates. In AAAI. AAAI Press, 822\u2013829."},{"key":"e_1_3_2_1_17_1","unstructured":"KeenTune. 2021. KeenTune. https:\/\/openanolis.cn\/sig\/keentune \t\t\t\t  KeenTune. 2021. KeenTune. https:\/\/openanolis.cn\/sig\/keentune"},{"key":"e_1_3_2_1_18_1","unstructured":"Kubernetes. 2016. ingress-nginx. https:\/\/github.com\/kubernetes\/ingress-nginx \t\t\t\t  Kubernetes. 2016. ingress-nginx. https:\/\/github.com\/kubernetes\/ingress-nginx"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352129"},{"key":"e_1_3_2_1_20_1","volume-title":"Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. In ICLR (Poster). OpenReview.net.","author":"Li Lisha","year":"2017","unstructured":"Lisha Li , Kevin G. Jamieson , Giulia DeSalvo , Afshin Rostamizadeh , and Ameet Talwalkar . 2017 . Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. In ICLR (Poster). OpenReview.net. Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2017. Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_21_1","unstructured":"Linux. 2020. sysctl. https:\/\/man7.org\/linux\/man-pages\/man8\/sysctl.8.html \t\t\t\t  Linux. 2020. sysctl. https:\/\/man7.org\/linux\/man-pages\/man8\/sysctl.8.html"},{"key":"e_1_3_2_1_22_1","volume-title":"Lundberg and Su-In Lee","author":"Scott","year":"2017","unstructured":"Scott M. Lundberg and Su-In Lee . 2017 . A Unified Approach to Interpreting Model Predictions. In NIPS. 4765\u20134774. Scott M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In NIPS. 4765\u20134774."},{"key":"e_1_3_2_1_23_1","unstructured":"Microsoft. 2021. Neural Network Intelligence. https:\/\/github.com\/microsoft\/nni \t\t\t\t  Microsoft. 2021. Neural Network Intelligence. https:\/\/github.com\/microsoft\/nni"},{"key":"e_1_3_2_1_24_1","unstructured":"NGINX Inc. 2020. nginx. https:\/\/nginx.org\/en\/ \t\t\t\t  NGINX Inc. 2020. nginx. https:\/\/nginx.org\/en\/"},{"key":"e_1_3_2_1_25_1","unstructured":"OceanBase. 2010. oceanbase. https:\/\/github.com\/oceanbase\/oceanbase \t\t\t\t  OceanBase. 2010. oceanbase. https:\/\/github.com\/oceanbase\/oceanbase"},{"key":"e_1_3_2_1_26_1","unstructured":"OpenAnolis. 2020. Anolis OS. https:\/\/openanolis.cn\/anolisos \t\t\t\t  OpenAnolis. 2020. Anolis OS. https:\/\/openanolis.cn\/anolisos"},{"key":"e_1_3_2_1_27_1","unstructured":"OpenAnolis. 2020. OpenAnolis community. https:\/\/openanolis.cn \t\t\t\t  OpenAnolis. 2020. OpenAnolis community. https:\/\/openanolis.cn"},{"key":"e_1_3_2_1_28_1","unstructured":"Oracle Corporation. 2010. MySQL 8.0 Reference Manual. https:\/\/www.mysql.com \t\t\t\t  Oracle Corporation. 2010. MySQL 8.0 Reference Manual. https:\/\/www.mysql.com"},{"key":"e_1_3_2_1_29_1","volume-title":"CLITE: Efficient and QoS-Aware Co-Location of Multiple Latency-Critical Jobs for Warehouse Scale Computers","author":"Patel Tirthak","year":"2020","unstructured":"Tirthak Patel and Devesh Tiwari . 2020 . CLITE: Efficient and QoS-Aware Co-Location of Multiple Latency-Critical Jobs for Warehouse Scale Computers . In HPCA. IEEE , 193\u2013206. Tirthak Patel and Devesh Tiwari. 2020. CLITE: Efficient and QoS-Aware Co-Location of Multiple Latency-Critical Jobs for Warehouse Scale Computers. In HPCA. IEEE, 193\u2013206."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_31_1","volume-title":"DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates","author":"Renda Alex","unstructured":"Alex Renda , Yishen Chen , Charith Mendis , and Michael Carbin . 2020. DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates . In MICRO. IEEE , 442\u2013455. Alex Renda, Yishen Chen, Charith Mendis, and Michael Carbin. 2020. DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. In MICRO. IEEE, 442\u2013455."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Marco T\u00falio Ribeiro Sameer Singh and Carlos Guestrin. 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. 1135\u20131144. \t\t\t\t  Marco T\u00falio Ribeiro Sameer Singh and Carlos Guestrin. 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. 1135\u20131144.","DOI":"10.18653\/v1\/N16-3020"},{"key":"e_1_3_2_1_33_1","volume-title":"Adams","author":"Snoek Jasper","year":"2012","unstructured":"Jasper Snoek , Hugo Larochelle , and Ryan P . Adams . 2012 . Practical Bayesian Optimization of Machine Learning Algorithms. In NIPS. 2960\u20132968. Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. In NIPS. 2960\u20132968."},{"key":"e_1_3_2_1_34_1","unstructured":"Standard Performance Evaluation Corporation. 2015. SPECjbb2015. https:\/\/www.spec.org\/jbb2015 \t\t\t\t  Standard Performance Evaluation Corporation. 2015. SPECjbb2015. https:\/\/www.spec.org\/jbb2015"},{"key":"e_1_3_2_1_35_1","unstructured":"Stressapptest. 2017. stressapp. https:\/\/github.com\/stressapptest\/stressapptest \t\t\t\t  Stressapptest. 2017. stressapp. https:\/\/github.com\/stressapptest\/stressapptest"},{"key":"e_1_3_2_1_36_1","unstructured":"Kevin Swersky Jasper Snoek and Ryan Prescott Adams. 2013. Multi-Task Bayesian Optimization. In NIPS. 2004\u20132012. \t\t\t\t  Kevin Swersky Jasper Snoek and Ryan Prescott Adams. 2013. Multi-Task Bayesian Optimization. In NIPS. 2004\u20132012."},{"key":"e_1_3_2_1_37_1","unstructured":"TPC. 1992. TPC-C. https:\/\/www.tpc.org\/tpcc \t\t\t\t  TPC. 1992. TPC-C. https:\/\/www.tpc.org\/tpcc"},{"key":"e_1_3_2_1_38_1","unstructured":"TPC. 2007. TPC-H. https:\/\/www.tpc.org\/tpch \t\t\t\t  TPC. 2007. TPC-H. https:\/\/www.tpc.org\/tpch"},{"key":"e_1_3_2_1_39_1","unstructured":"Will Glozer. 2013. wrk. https:\/\/github.com\/wg\/wrk \t\t\t\t  Will Glozer. 2013. wrk. https:\/\/github.com\/wg\/wrk"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300085"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457291"}],"event":{"name":"ESEC\/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","location":"Singapore Singapore","acronym":"ESEC\/FSE '22","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","NUS NUS"]},"container-title":["Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3540250.3558962","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3540250.3558962","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:04Z","timestamp":1750182544000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3540250.3558962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":41,"alternative-id":["10.1145\/3540250.3558962","10.1145\/3540250"],"URL":"https:\/\/doi.org\/10.1145\/3540250.3558962","relation":{},"subject":[],"published":{"date-parts":[[2022,11,7]]},"assertion":[{"value":"2022-11-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}