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The cloud platform is the most popular and powerful scale-out infrastructure to perform big data analytics and eliminate the need to maintain expensive and high-end computing resources at the user side. The performance and the cost of such infrastructure depend on the overall server configuration, such as processor, memory, network, and storage configurations. In addition to the cost of owning or maintaining the hardware, the heterogeneity in the server configuration further expands the selection space, leading to non-convergence. The challenge is further exacerbated by the dependency of the application\u2019s performance on the underlying hardware. Despite an increasing interest in resource provisioning, few works have been done to develop accurate and practical models to proactively predict the performance of data-intensive applications corresponding to the server configuration and provision a cost-optimal configuration online.<\/jats:p>\n          <jats:p>In this work, through a comprehensive real-system empirical analysis of performance, we address these challenges by introducing ProMLB: a proactive machine-learning-based methodology for resource provisioning. We first characterize diverse types of data-intensive workloads across different types of server architectures. The characterization aids in accurately capture applications\u2019 behavior and train a model for prediction of their performance.<\/jats:p>\n          <jats:p>Then, ProMLB builds a set of cross-platform performance models for each application. Based on the developed predictive model, ProMLB uses an optimization technique to distinguish close-to-optimal configuration to minimize the product of execution time and cost. Compared to the oracle scheduler, ProMLB achieves 91% accuracy in terms of application-resource matching. On average, ProMLB improves the performance and resource utilization by 42.6% and 41.1%, respectively, compared to baseline scheduler. Moreover, ProMLB improves the performance per cost by 2.5\u00d7 on average.<\/jats:p>","DOI":"10.1145\/3442696","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T17:05:07Z","timestamp":1615309507000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Adaptive Performance Modeling of Data-intensive Workloads for Resource Provisioning in Virtualized Environment"],"prefix":"10.1145","volume":"5","author":[{"given":"Hosein Mohamamdi","family":"Makrani","sequence":"first","affiliation":[{"name":"University of California, Davis, USA"}]},{"given":"Hossein","family":"Sayadi","sequence":"additional","affiliation":[{"name":"California State University, Long Beach, USA"}]},{"given":"Najmeh","family":"Nazari","sequence":"additional","affiliation":[{"name":"University of Tehran, Iran"}]},{"given":"Sai Mnoj Pudukotai","family":"Dinakarrao","sequence":"additional","affiliation":[{"name":"George Mason University, USA"}]},{"given":"Avesta","family":"Sasan","sequence":"additional","affiliation":[{"name":"George Mason University, USA"}]},{"given":"Tinoosh","family":"Mohsenin","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County, USA"}]},{"given":"Setareh","family":"Rafatirad","sequence":"additional","affiliation":[{"name":"George Mason University, USA"}]},{"given":"Houman","family":"Homayoun","sequence":"additional","affiliation":[{"name":"University of California, Davis, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,3,9]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. The Apache Software Foundation. Retrieved from https:\/\/cloudstack.apache.org\/.  [n.d.]. The Apache Software Foundation. Retrieved from https:\/\/cloudstack.apache.org\/."},{"volume-title":"2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA'14)","author":"Wang L.","key":"e_1_2_1_2_1","unstructured":"L. Wang , J. Zhan , C. Luo , Y. Zhu , Q. Yang , Y. He , W. Gao , Z. Jia , Y. Shi , S. Zhang , and C. Zheng . 2014. Bigdatabench: A big data benchmark suite from internet services . In 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA'14) . IEEE, 488--499. L. Wang, J. Zhan, C. Luo, Y. Zhu, Q. Yang, Y. He, W. Gao, Z. Jia, Y. Shi, S. Zhang, and C. Zheng. 2014. Bigdatabench: A big data benchmark suite from internet services. In 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA'14). IEEE, 488--499."},{"key":"e_1_2_1_3_1","unstructured":"Thomas Willhalm and Roman Dementiev. [n.d.]. Retrieved from https:\/\/software.intel.com\/en-us\/articles\/intel-performance-counter-monitor.  Thomas Willhalm and Roman Dementiev. [n.d.]. Retrieved from https:\/\/software.intel.com\/en-us\/articles\/intel-performance-counter-monitor."},{"key":"e_1_2_1_4_1","unstructured":"[n.d.]. Martin A. Brown. Traffic Control Howto. Retrieved from http:\/\/linux-ip.net\/articles\/Traffic-Control-HOWTO\/.  [n.d.]. Martin A. Brown. Traffic Control Howto. Retrieved from http:\/\/linux-ip.net\/articles\/Traffic-Control-HOWTO\/."},{"volume-title":"Intel R 64 and IA-32 Architecture Software Developer\u2019s Manual","key":"e_1_2_1_5_1","unstructured":"2014. Intel R 64 and IA-32 Architecture Software Developer\u2019s Manual , Vol. 3B: System Programming Guide, Part 2 . 2014. Intel R 64 and IA-32 Architecture Software Developer\u2019s Manual, Vol. 3B: System Programming Guide, Part 2."},{"key":"e_1_2_1_6_1","unstructured":"2017. Rightscale Inc. 2017. Amazon EC2: Rightscale. Retrieved from http:\/\/www.rightscale.com\/.  2017. Rightscale Inc. 2017. Amazon EC2: Rightscale. Retrieved from http:\/\/www.rightscale.com\/."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI\u201917)","volume":"2","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 Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI\u201917) , Vol. 2 . 4--2. 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. 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In Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds. ACM, 1--6. Peter Bodik, Rean Griffith, Charles Sutton, Armando Fox, Michael I. Jordan, and David A. Patterson. 2009. Automatic exploration of datacenter performance regimes. In Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds. ACM, 1--6."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.01.015"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-013-3903-7"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499368.2451125"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2644865.2541941"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2954680.2872365"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3093337.3037703"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2248487.2150982"},{"volume-title":"Proceedings of the 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA\u201913)","author":"Guevara Marisabel","key":"e_1_2_1_18_1","unstructured":"Marisabel Guevara , Benjamin Lubin , and Benjamin C. Lee . 2013. Navigating heterogeneous processors with market mechanisms . 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In Proceedings of the IEEE 2020 11th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON\u201920) . Maryam Heidari and James H. Jr Jones. 2020. Using BERT to extract topic-independent sentiment features for social media bot detection. In Proceedings of the IEEE 2020 11th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON\u201920)."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW51313.2020.00071"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMAP49528.2020.9248443"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW.2010.5452747"},{"volume-title":"Proceedings of the 2010 IEEE 2nd International Conference on Cloud Computing Technology and Science (CloudCom\u201910)","author":"Jackson Keith R.","key":"e_1_2_1_24_1","unstructured":"Keith R. Jackson , Lavanya Ramakrishnan , Krishna Muriki , Shane Canon , Shreyas Cholia , John Shalf , Harvey J. 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In Hardware Architectures for Deep Learning . 95--115. Nameh Nazari and Mostafa E. Salehi. 2020. Binary neural networks. In Hardware Architectures for Deep Learning. 95--115."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSD.2019.00052"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/PDP50117.2020.00033"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229631.3229639"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCC.2014.22"},{"key":"e_1_2_1_44_1","volume-title":"Mage: Online interference-aware scheduling in multi-scale heterogeneous systems. arXiv:1804.06462.","author":"Romero Francisco","year":"2018","unstructured":"Francisco Romero and Christina Delimitrou . 2018 . Mage: Online interference-aware scheduling in multi-scale heterogeneous systems. arXiv:1804.06462. Retrieved from https:\/\/arxiv.org\/abs\/1804.06462. Francisco Romero and Christina Delimitrou. 2018. Mage: Online interference-aware scheduling in multi-scale heterogeneous systems. arXiv:1804.06462. Retrieved from https:\/\/arxiv.org\/abs\/1804.06462."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2019.8715080"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465828"},{"volume-title":"Congress on Evolutionary Computation.","author":"Selmar","key":"e_1_2_1_47_1","unstructured":"Selmar K. Smit et\u00a0al. 2009. Comparing parameter tuning methods for evolutionary algorithms . In Congress on Evolutionary Computation. Selmar K. Smit et\u00a0al. 2009. Comparing parameter tuning methods for evolutionary algorithms. In Congress on Evolutionary Computation."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.04.018"},{"key":"e_1_2_1_49_1","volume-title":"Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN\u201900)","volume":"6","author":"Theodore","unstructured":"Theodore B. Trafalis and Huseyin Ince. 2000. Support vector machine for regression and applications to financial forecasting . In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN\u201900) , Vol. 6 . IEEE, 348--353. Theodore B. Trafalis and Huseyin Ince. 2000. Support vector machine for regression and applications to financial forecasting. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN\u201900), Vol. 6. IEEE, 348--353."},{"key":"e_1_2_1_50_1","volume-title":"Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI\u201916)","author":"Venkataraman Shivaram","year":"2016","unstructured":"Shivaram Venkataraman , Zongheng Yang , Michael J. Franklin , Benjamin Recht , and Ion Stoica . 2016 . Ernest: Efficient performance prediction for large-scale advanced analytics . In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI\u201916) . 363--378. Shivaram Venkataraman, Zongheng Yang, Michael J. Franklin, Benjamin Recht, and Ion Stoica. 2016. Ernest: Efficient performance prediction for large-scale advanced analytics. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI\u201916). 363--378."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2670979.2671005"},{"volume-title":"Proceedings of the 2017 Symposium on Cloud Computing. 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