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Home > Archive>Volume 36, Issue 1, 2025 >446-468. DOI:10.13328/j.cnki.jos.007240
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Survey on Resource Planning and Scheduling Technologies for Multi-tenant Cloud Databases
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    Abstract:

    Multi-tenant cloud databases offer services more cheaply and conveniently, with advantages like paying on demand, scaling on demand, automatic deployment, high availability, self-maintenance, and shared resources. Now more and more enterprises and individuals begin to host their database services on database as a service (DaaS) platforms. These DaaS platforms provide services to multiple tenants in accordance with their service-level agreements (SLAs), while improving revenue for themselves. However, due to the dynamic, heterogeneous, and competitive characteristics of multiple tenants and their loads, it is a very challenging task for DaaS platform providers to adaptively plan and schedule resources according to dynamic loads while complying with multi-tenants’ SLAs. For common types of multi-tenant cloud databases, such as relational databases, this survey firstly analyzes the challenges faced by resource planning and scheduling of multi- tenant cloud databases in detail and then outlines related key scientific issues. Then, it provides a framework of related techniques and a summary of existing research in four areas: resource planning and scheduling technologies, resource forecasting technologies, resource elastic scaling technologies, and resource planning and scheduling tools for existing databases. Lastly, this survey provides suggestions for future research directions on resource planning and scheduling technologies for multi-tenant cloud databases.

    Reference
    [1] Zhang X, Tune E, Hagmann R, Jnagal R, Gokhale V, Wilkes J. CPI2: CPU performance isolation for shared compute clusters. In: Proc. of the 8th ACM European Conf. on Computer Systems. Prague: ACM, 2013. 379–391. [doi: 10.1145/2465351.2465388]
    [2] Lu JH, Holubová I. Multi-model databases: A new journey to handle the variety of data. ACM Computing Surveys, 2020, 52(3): 55.
    [3] Wang YZ, Yu JQ, Yu ZB. Resource scheduling techniques in cloud from a view of coordination: A holistic survey. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 1–40.
    [4] 李永峰, 周敏奇, 胡华梁. 集群资源统一管理和调度技术综述. 华东师范大学学报(自然科学版), 2014, (5): 17–30.
    Li YF, Zhou MQ, Hu HL. Survey of resource uniform management and scheduling in cluster. Journal of East China Normal University (Natural Science), 2014, (5): 17–30 (in Chinese with English abstract).
    [5] 董昊文, 张超, 李国良, 冯建华. 云原生数据库综述. 软件学报, 2024, 35(2): 899–926. http://www.jos.org.cn/1000-9825/6952.htm
    Dong HW, Zhang C, Li GL, Feng JH. Survey on cloud-native databases. Ruan Jian Xue Bao/Journal of Software, 2024, 35(2): 899–926 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6952.htm
    [6] 李国良, 周煊赫, 孙佶, 余翔, 袁海涛, 刘佳斌, 韩越. 基于机器学习的数据库技术综述. 计算机学报, 2020, 43(11): 2019–2049.
    Li GL, Zhou XH, Sun J, Yu X, Yuan HT, Liu JB, Han Y. A survey of machine learning based database techniques. Chinese Journal of Computers, 2020, 43(11): 2019–2049 (in Chinese with English abstract).
    [7] Pavlo A, Butrovich M, Ma L, Menon P, Lim WS, Van Aken D, Zhang W. Make your database system dream of electric sheep: Towards self-driving operation. Proc. of the VLDB Endowment, 2021, 14(12): 3211–3221.
    [8] Narasayya V, Menache I, Singh M, Li F, Syamala M, Chaudhuri S. Sharing buffer pool memory in multi-tenant relational database-as-a-service. Proc. of the VLDB Endowment, 2015, 8(7): 726–737.
    [9] CapacityScheduler Guide. 2024. http://hadoop.apache.org/docs/r1.2.1/capacity_scheduler.html
    [10] Fair Scheduler. 2024. http://hadoop.apache.org/docs/r1.2.1/fair_scheduler.html
    [11] 2024. https://en.wikipedia.org/wiki/Max-min_fairness
    [12] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008, 51(1): 107–113.
    [13] Zaharia M, Borthakur D, Sen Sarma J, Elmeleegy K, Shenker S, Stoica I. Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In: Proc. of the 5th European Conf. on Computer Systems. Paris: ACM, 2010. 265–278.
    [14] Isard M, Prabhakaran V, Currey J, Wieder U, Talwar K, Goldberg A. Quincy: Fair scheduling for distributed computing clusters. In: Proc. of the 22nd ACM SIGOPS Symp. on Operating Systems Principles. Big Sky: ACM, 2009. 261–276.
    [15] Isard M, Budiu M, Yu Y, Birrell A, Fetterly D. Dryad: Distributed data-parallel programs from sequential building blocks. ACM SIGOPS Operating Systems Review, 2007, 41(3): 59–72.
    [16] Ghodsi A, Zaharia M, Shenker S, Stoica I. Choosy: Max-min fair sharing for datacenter jobs with constraints. In: Proc. of the 8th ACM European Conf. on Computer Systems. Prague: ACM, 2013. 365–378. [doi: 10.1145/2465351.2465387]
    [17] Ghodsi A, Zaharia M, Hindman B, Konwinski A, Shenker S, Stoica I. Dominant resource fairness: Fair allocation of multiple resource types. In: Proc. of the 8th USENIX Conf. on Networked Systems Design and Implementation. Boston: USENIX Association, 2011. 323–336.
    [18] Li JX, König AC, Narasayya V, Chaudhuri S. Robust estimation of resource consumption for SQL queries using statistical techniques. Proc. of the VLDB Endowment, 2012, 5(11): 1555–1566.
    [19] Peng YH, Bao YX, Chen YR, Wu C, Guo CX. Optimus: An efficient dynamic resource scheduler for deep learning clusters. In: Proc. of the 13th EuroSys Conf. Porto: ACM, 2018. 3. [doi: 10.1145/3190508.3190517]
    [20] Mondal SS, Sheoran N, Mitra S. Scheduling of time-varying workloads using reinforcement learning. In: Proc. of the 35th AAAI Conf. on Artificial Intelligence. Virtually: AAAI, 2021. 9000–9008. [doi: 10.1609/aaai.v35i10.17088]
    [21] Sun J, Li GL. An end-to-end learning-based cost estimator. Proc. of the VLDB Endowment, 2019, 13(3): 307–319.
    [22] Das S, Narasayya VR, Li F, Syamala M. CPU sharing techniques for performance isolation in multi-tenant relational database-as-a-service. Proc. of the VLDB Endowment, 2013, 7(1): 37–48.
    [23] Tan J, Zhang TY, Li FF, Chen J, Zheng QX, Zhang P, Qiao HL, Shi Y, Cao W, Zhang R. iBTune: Individualized buffer tuning for large-scale cloud databases. Proc. of the VLDB Endowment, 2019, 12(10): 1221–1234.
    [24] Basiuk T, Gruenwald L, D’Orazio L, Leal E. A persistent memory-aware buffer pool manager simulator for multi-tenant cloud databases. In: Proc. of the 36th Int’l Conf. on Data Engineering Workshops (ICDEW). Dallas: IEEE, 2020. 121–126.
    [25] Zhao YL, Calheiros RN, Gange G, Bailey J, Sinnott RO. SLA-based profit optimization resource scheduling for big data analytics-as-a-service platforms in cloud computing environments. IEEE Trans. on Cloud Computing, 2021, 9(3): 1236–1253.
    [26] Wang YC, He Q, Zhang XY, Ye DY, Yang Y. Efficient QoS-aware service recommendation for multi-tenant service-based systems in cloud. IEEE Trans. on Services Computing, 2020, 13(6): 1045–1058.
    [27] 张超, 李国良, 冯建华, 张金涛. HTAP数据库关键技术综述. 软件学报, 2023, 34(2): 761–785. http://www.jos.org.cn/1000-9825/6713.htm
    Zhang C, Li GL, Feng JH, Zhang JT. Survey of key techniques of HTAP databases. Ruan Jian Xue Bao/Journal of Software, 2023, 34(2): 761–785 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6713.htm
    [28] Raza A, Chrysogelos P, Anadiotis AC, Ailamaki A. Adaptive HTAP through elastic resource scheduling. In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020. 2043–2054. [doi: 10.1145/3318464.3389783]
    [29] Korkmaz M, Karsten M, Salem K, Salihoglu S. Workload-aware CPU performance scaling for transactional database systems. In: Proc. of the 2018 Int’l Conf. on Management of Data. Houston: ACM, 2018. 291–306. [doi: 10.1145/3183713.3196901]
    [30] Zhang XY, Wu H, Chang Z, Jin SW, Tan J, Li FF, Zhang TY, Cui B. ResTune: Resource oriented tuning boosted by meta-learning for cloud databases. In: Proc. of the 2021 Int’l Conf. on Management of Data. Virtual Event: ACM, 2021. 2102–2114.
    [31] 徐海洋, 刘海龙, 杨超云, 王硕, 李战怀. MMOS: 支持超卖的多租户数据库内存资源共享方法. 计算机科学, 2024, 51(2): 27–35.
    Xu HY, Liu HL, Yang CY, Wang S, Li ZH. MMOS: Memory resource sharing methods to support overselling in multi-tenant databases. Computer Science, 2024, 51(2): 27–35 (in Chinese with English abstract).
    [32] 徐海洋. 多租户数据库资源管理关键技术研究与实现 [硕士学位论文]. 西安: 西北工业大学, 2024.
    Xu HY. Research and implementation of key technologies in multi-tenant database resource management [MS. Thesis]. Xi’an: Northwestern Polytechnical University, 2024 (in Chinese with English abstract).
    [33] 王硕. 基于AI的多租户资源分配与调度机制研究 [硕士学位论文]. 西安: 西北工业大学, 2024.
    Wang S. Research on AI based multi tenant resource allocation and scheduling mechanism [MS. Thesis]. Xi’an: Northwestern Polytechnical University, 2024 (in Chinese with English abstract).
    [34] Ahmad M, Duan SY, Aboulnaga A, Babu S. Predicting completion times of batch query workloads using interaction-aware models and simulation. In: Proc. of the 14th Int’l Conf. on Extending Database Technology. Uppsala: ACM, 2011. 449–460.
    [35] Akdere M, Çetintemel U, Riondato M, Upfal E, Zdonik SB. Learning-based query performance modeling and prediction. In: Proc. of the 28th Int’l Conf. on Data Engineering. Arlington: IEEE, 2012. 390–401. [doi: 10.1109/ICDE.2012.64]
    [36] Duggan J, Cetintemel U, Papaemmanouil O, Upfal E. Performance prediction for concurrent database workloads. In: Proc. of the 2011 ACM SIGMOD Int’l Conf. on Management of Data. Athens: ACM, 2011. 337–348. [doi: 10.1145/1989323.1989359]
    [37] Ganapathi A, Kuno H, Dayal U, Wiener JL, Fox A, Jordan M, Patterson D. Predicting multiple metrics for queries: Better decisions enabled by machine learning. In: Proc. of the 25th Int’l Conf. on Data Engineering. Shanghai: IEEE, 2009. 592–603.
    [38] Wu WT, Chi Y, Zhu SH, Tatemura J, Hacigümüs H, Naughton JF. Predicting query execution time: Are optimizer cost models really unusable? In: Proc. of the 29th Int’l Conf. on Data Engineering (ICDE). Brisbane: IEEE, 2013. 1081–1092.
    [39] Wu WT, Chi Y, Hacigümüs H, Naughton JF. Towards predicting query execution time for concurrent and dynamic database workloads. Proc. of the VLDB Endowment, 2013, 6(10): 925–936.
    [40] Singhal R, Nambiar M. Predicting SQL query execution time for large data volume. In: Proc. of the 20th Int’l Database Engineering & Applications Symp. Montreal: ACM, 2016. 378–385. [doi: 10.1145/2938503.2938552]
    [41] Popescu AD, Balmin A, Ercegovac V, Ailamaki A. PREDIcT: Towards predicting the runtime of large scale iterative analytics. Proc. of the VLDB Endowment, 2013, 6(14): 1678–1689.
    [42] Duggan J, Papaemmanouil O, Cetintemel U, Upfal E. Contender: A resource modeling approach for concurrent query performance prediction. In: Proc. of the 17th Int’l Conf. on Extending Database Technology. Athens: OpenProceedings.org, 2014. 109–120. [doi: 10.5441/002/edbt.2014.11]
    [43] Ahmad M, Duan S, Aboulnaga A, Babu S. Interaction-aware prediction of business intelligence workload completion times. In: Proc. of the 26th Int’l Conf. on Data Engineering (ICDE 2010). Long Beach: IEEE, 2010. 413–416. [doi: 10.1109/ICDE.2010.5447834]
    [44] Popescu AD, Ercegovac V, Balmin A, Branco M, Ailamaki A. Same queries, different data: Can we predict runtime performance? In: Proc. of the 28th Int’l Conf. on Data Engineering Workshops. Arlington: IEEE, 2012. 275–280. [doi: 10.1109/ICDEW.2012.66]
    [45] König AC, Ding BL, Chaudhuri S, Narasayya V. A statistical approach towards robust progress estimation. Proc. of the VLDB Endowment, 2011, 5(4): 382–393.
    [46] Kipf A, Vorona D, Müller J, Kipf T, Radke B, Leis V, Boncz P, Neumann T, Kemper A. Estimating cardinalities with deep sketches. In: Proc. of the 2019 Int’l Conf. on Management of Data. Amsterdam: ACM, 2019. 1937–1940. [doi: 10.1145/3299869.3320218]
    [47] Chen L, Huang HQ, Chen DH. Join cardinality estimation by combining operator-level deep neural networks. Information Sciences, 2021, 546: 1047–1062.
    [48] Park Y, Zhong SC, Mozafari B. QuickSel: Quick selectivity learning with mixture models. In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020. 1017–1033. [doi: 10.1145/3318464.3389727]
    [49] Ma L, Van Aken D, Hefny A, Mezerhane G, Pavlo A, Gordon GJ. Query-based workload forecasting for self-driving database management systems. In: Proc. of the 2018 Int’l Conf. on Management of Data. Houston: ACM, 2018. 631–645.
    [50] Zhang QC, Yang LT, Yan Z, Chen ZK, Li P. An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans. on Industrial Informatics, 2018, 14(7): 3170–3178.
    [51] Fang W, Lu ZH, Wu J, Cao ZY. RPPS: A novel resource prediction and provisioning scheme in cloud data center. In: Proc. of the 9th Int’l Conf. on Services Computing. Honolulu: IEEE, 2012. 609–616. [doi: 10.1109/SCC.2012.47]
    [52] Huang JH, Li CL, Yu J. Resource prediction based on double exponential smoothing in cloud computing. In: Proc. of the 2nd Int’l Conf. on Consumer Electronics, Communications and Networks (CECNet). Yichang: IEEE, 2012. 2056–2060.
    [53] Mozafari B, Curino C, Jindal A, Madden S. Performance and resource modeling in highly-concurrent OLTP workloads. In: Proc. of the 2013 ACM SIGMOD Int’l Conf. on Management of Data. New York: ACM, 2013. 301–312. [doi: 10.1145/2463676.2467800]
    [54] Yoon DY, Mozafari B, Brown DP. DBSeer: Pain-free database administration through workload intelligence. Proc. of the VLDB Endowment, 2015, 8(12): 2036–2039.
    [55] Gu R, Chen YQ, Liu S, Dai HP, Chen GH, Zhang K, Che Y, Huang YH. Liquid: Intelligent resource estimation and network-efficient scheduling for deep learning jobs on distributed GPU clusters. IEEE Trans. on Parallel and Distributed Systems, 2022, 33(11): 2808–2820.
    [56] Sen R, Roy A, Jindal A. Predictive price-performance optimization for serverless query processing. In: Proc. of the 26th Int’l Conf. on Extending Database Technology. Ioannina: OpenProceedings.org, 2023. 118–130. [doi: 10.48786/EDBT.2023.10]
    [57] Lorido-Botran T, Miguel-Alonso J, Lozano JA. A review of auto-scaling techniques for elastic applications in cloud environments. Journal of Grid Computing, 2014, 12(4): 559–592.
    [58] Alhamazani K, Ranjan R, Mitra K, Rabhi F, Jayaraman PP, Khan SU, Guabtni A, Bhatnagar V. An overview of the commercial cloud monitoring tools: Research dimensions, design issues, and state-of-the-art. Computing, 2015, 97(4): 357–377.
    [59] Chen T, Bahsoon R, Yao X. A survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems. ACM Computing Surveys, 2019, 51(3): 61.
    [60] Han R, Guo L, Ghanem MM, Guo YK. Lightweight resource scaling for cloud applications. In: Proc. of the 12th IEEE/ACM Int’l Symp. on Cluster, Cloud and Grid Computing (CCGRID 2012). Ottawa: IEEE, 2012. 644–651. [doi: 10.1109/CCGrid.2012.52]
    [61] Koperek P, Funika W. Dynamic business metrics-driven resource provisioning in cloud environments. In: Proc. of the 9th Int’l Conf. on Parallel Processing and Applied Mathematics. Torun: Springer, 2012. 171–180. [doi: 10.1007/978-3-642-31500-8_18]
    [62] Hasan MZ, Magana E, Clemm A, Tucker L, Gudreddi SLD. Integrated and autonomic cloud resource scaling. In: Proc. of the 2012 IEEE Network Operations and Management Symp. Maui: IEEE, 2012. 1327–1334. [doi: 10.1109/NOMS.2012.6212070]
    [63] Casalicchio E, Silvestri L. Autonomic management of cloud-based systems: The service provider perspective. In: Proc. of the 27th Int’l Symp. on Computer and Information Sciences III. London: Springer, 2013. 39–47. [doi: 10.1007/978-1-4471-4594-3_5]
    [64] Chieu TC, Mohindra A, Karve AA. Scalability and performance of web applications in a compute cloud. In: Proc. of the 8th Int’l Conf. on e-Business Engineering. Beijing: IEEE, 2011. 317–323. [doi: 10.1109/ICEBE.2011.63]
    [65] Ghanbari H, Simmons B, Litoiu M, Iszlai G. Exploring alternative approaches to implement an elasticity policy. In: Proc. of the 4th Int’l Conf. on Cloud Computing. Washington: IEEE, 2011. 716–723. [doi: 10.1109/CLOUD.2011.101]
    [66] Simmons B, Ghanbari H, Litoiu M, Iszlai G. Managing a SaaS application in the cloud using PaaS policy sets and a strategy-tree. In: Proc. of the 7th Int’l Conf. on Network and Services Management. Paris: International Federation for Information Processing, 2011. 343–347.
    [67] Maurer M, Brandic I, Sakellariou R. Enacting SLAs in clouds using rules. In: Proc. of the 17th Int’l Euro-Par Conf. on Euro-Par 2011 Parallel Processing. Bordeaux: Springer, 2011. 455–466. [doi: 10.1007/978-3-642-23400-2_42]
    [68] Nguyen TT, Yeom YJ, Kim T, Park DH, Kim S. Horizontal pod autoscaling in kubernetes for elastic container orchestration. Sensors, 2020, 20(16): 4621.
    [69] Das S, Li F, Narasayya VR, König AC. Automated demand-driven resource scaling in relational database-as-a-service. In: Proc. of the 2016 Int’l Conf. on Management of Data. San Francisco: ACM, 2016. 1923–1934. [doi: 10.1145/2882903.2903733]
    [70] Nouri SMR, Li H, Venugopal S, Guo WX, He MY, Tian WH. Autonomic decentralized elasticity based on a reinforcement learning controller for cloud applications. Future Generation Computer Systems, 2019, 94: 765–780.
    [71] Kardani-Moghaddam S, Buyya R, Ramamohanarao K. ADRL: A hybrid anomaly-aware deep reinforcement learning-based resource scaling in clouds. IEEE Trans. on Parallel and Distributed Systems, 2021, 32(3): 514–526.
    [72] Zafeiropoulos A, Fotopoulou E, Filinis N, Papavassiliou S. Reinforcement learning-assisted autoscaling mechanisms for serverless computing platforms. Simulation Modelling Practice and Theory, 2022, 116: 102461.
    [73] Garí Y, Monge DA, Pacini E, Mateos C, Garino CG. Reinforcement learning-based application Autoscaling in the Cloud: A survey. Engineering Applications of Artificial Intelligence, 2021, 102: 104288.
    [74] Dutreilh X, Moreau A, Malenfant J, Rivierre N, Truck I. From data center resource allocation to control theory and back. In: Proc. of the 3rd Int’l Conf. on Cloud Computing. Miami: IEEE, 2010. 410–417. [doi: 10.1109/CLOUD.2010.55]
    [75] Dutreilh X, Kirgizov S, Melekhova O, Malenfant J, Rivierre N, Truck I. Using reinforcement learning for autonomic resource allocation in clouds: Towards a fully automated workflow. In: Proc. of the 7th Int’l Conf. on Autonomic and Autonomous Systems. Venice: IARIA, 2011. 67–74.
    [76] Barrett E, Howley E, Duggan J. Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurrency and Computation: Practice and Experience, 2013, 25(12): 1656–1674.
    [77] Bu XP, Rao JH, Xu CZ. Coordinated self-configuration of virtual machines and appliances using a model-free learning approach. IEEE Trans. on Parallel and Distributed Systems, 2013, 24(4): 681–690.
    [78] Rao J, Bu XP, Xu CZ, Wang LY, Yin G. VCONF: A reinforcement learning approach to virtual machines auto-configuration. In: Proc. of the 6th Int’l Conf. on Autonomic Computing. Barcelona: ACM, 2009. 137–146. [doi: 10.1145/1555228.1555263]
    [79] Rao J, Bu X, Xu CZ, Wang K. A distributed self-learning approach for elastic provisioning of virtualized cloud resources. In: Proc. of the 19th Annual Int’l Symp. on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems. Singapore: IEEE, 2011. 45–54. [doi: 10.1109/MASCOTS.2011.47]
    [80] Zhu Q, Agrawal G. Resource provisioning with budget constraints for adaptive applications in cloud environments. IEEE Trans. on Services Computing, 2012, 5(4): 497–511.
    [81] Taft R, El-Sayed N, Serafini M, Lu Y, Aboulnaga A, Stonebraker M, Mayerhofer R, Andrade F. P-store: An elastic database system with predictive provisioning. In: Proc. of the 2018 Int’l Conf. on Management of Data. Houston: ACM, 2018. 205–219.
    [82] Ali-Eldin A, Kihl M, Tordsson J, Elmroth E. Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control. In: Proc. of the 3rd Workshop on Scientific Cloud Computing. Delft: ACM, 2012. 31–40. [doi: 10.1145/2287036.2287044]
    [83] Ali-Eldin A, Tordsson J, Elmroth E. An adaptive hybrid elasticity controller for cloud infrastructures. In: Proc. of the 2012 IEEE Network Operations and Management Symp. Maui: IEEE, 2012. 204–212. [doi: 10.1109/NOMS.2012.6211900]
    [84] Bacigalupo DA, Van Hemert J, Usmani A, Dillenberger DN, Wills GB, Jarvis SA. Resource management of enterprise cloud systems using layered queuing and historical performance models. In: Proc. of the 2010 IEEE Int’l Symp. on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW). Atlanta: IEEE, 2010. 1–8. [doi: 10.1109/IPDPSW.2010.5470782]
    [85] Han R, Ghanem MM, Guo L, Guo YK, Osmond M. Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. Future Generation Computer Systems, 2014, 32: 82–98.
    [86] Urgaonkar B, Shenoy P, Chandra A, Goyal P, Wood T. Agile dynamic provisioning of multi-tier internet applications. ACM Trans. on Autonomous and Adaptive Systems, 2008, 3(1): 1.
    [87] Villela D, Pradhan P, Rubenstein D. Provisioning servers in the application tier for e-commerce systems. ACM Trans. on Internet Technology, 2007, 7(1): 57–66.
    [88] Zhang Q, Cherkasova L, Smirni E. A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: Proc. of the 4th Int’l Conf. on Autonomic Computing (ICAC’07). Jacksonville: IEEE, 2007. 27. [doi: 10.1109/ICAC.2007.1]
    [89] Bodík P, Griffith R, Sutton C, Fox A, Jordan M, Patterson D. Statistical machine learning makes automatic control practical for internet datacenters. In: Proc. of the 2009 Conf. on Hot Topics in Cloud Computing. San Diego: USENIX Association, 2009. 12.
    [90] Lama P, Zhou X. Autonomic provisioning with self-adaptive neural fuzzy control for percentile-based delay guarantee. ACM Trans. on Autonomous and Adaptive Systems, 2013, 8(2): 9.
    [91] Lim HC, Babu S, Chase JS. Automated control for elastic storage. In: Proc. of the 7th Int’l Conf. on Autonomic Computing. Washington: ACM, 2010. 1–10. [doi: 10.1145/1809049.1809051]
    [92] Sellami W, Hadj Kacem H, Hadj Kacem A. Dynamic provisioning of service composition in a multi-tenant SaaS environment. Journal of Network and Systems Management, 2020, 28(2): 367–397.
    [93] Caron E, Desprez F, Muresan A. Pattern matching based forecast of non-periodic repetitive behavior for cloud clients. Journal of Grid Computing, 2011, 9(1): 49–64.
    [94] Chandra A, Gong W, Shenoy P. Dynamic resource allocation for shared data centers using online measurements. In: Proc. of the 11th Int’l Workshop on Quality of Service. Berkeley: Springer, 2003. 381–398. [doi: 10.1007/3-540-44884-5_21]
    [95] Dutta S, Gera S, Verma A, Viswanathan B. SmartScale: Automatic application scaling in enterprise clouds. In: Proc. of the 5th Int’l Conf. on Cloud Computing. Honolulu: IEEE, 2012. 221–228. [doi: 10.1109/CLOUD.2012.12]
    [96] Gong ZH, Gu XH, Wilkes J. PRESS: Predictive elastic resource scaling for cloud systems. In: Proc. of the 2010 Int’l Conf. on Network and Service Management. Niagara Falls: IEEE, 2010. 9–16. [doi: 10.1109/CNSM.2010.5691343]
    [97] Islam S, Keung J, Lee K, Liu AN. Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems, 2012, 28(1): 155–162.
    [98] Mi HB, Wang HM, Yin G, Zhou YF, Shi DX, Yuan L. Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: Proc. of the 2010 IEEE Int’l Conf. on Services Computing. Miami: IEEE, 2010. 514–521.
    [99] Roy N, Dubey A, Gokhale A. Efficient autoscaling in the cloud using predictive models for workload forecasting. In: Proc. of the 4th Int’l Conf. on Cloud Computing. Washington: IEEE, 2011. 500–507. [doi: 10.1109/CLOUD.2011.42]
    [100] Shen ZM, Subbiah S, Gu XH, Wilkes J. CloudScale: Elastic resource scaling for multi-tenant cloud systems. In: Proc. of the 2nd ACM Symp. on Cloud Computing. Cascais: ACM, 2011. 5. [doi: 10.1145/2038916.2038921]
    [101] Verma A, Pedrosa L, Korupolu M, Oppenheimer D, Tune E, Wilkes J. Large-scale cluster management at Google with Borg. In: Proc. of the 10th European Conf. on Computer Systems. Bordeaux: ACM, 2015. 18. [doi: 10.1145/2741948.2741964]
    [102] Ousterhout K, Wendell P, Zaharia M, Stoica I. Sparrow: Distributed, low latency scheduling. In: Proc. of the 24th ACM Symp. on Operating Systems Principles. Farminton: ACM, 2013. 69–84. [doi: 10.1145/2517349.2522716]
    [103] Vavilapalli VK, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R, Graves T, Lowe J, Shah H, Seth S, Saha B, Curino C, O’Malley O, Radia S, Reed B, Baldeschwieler E. Apache hadoop YARN: Yet another resource negotiator. In: Proc. of the 4th Annual Symp. on Cloud Computing. Santa Clara: ACM, 2013. 5. [doi: 10.1145/2523616.2523633]
    [104] Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph AD, Katz R, Shenker S, Stoica I. Mesos: A platform for fine-grained resource sharing in the data center. In: Proc. of the 8th USENIX Conf. on Networked Systems Design and Implementation. Boston: USENIX Association, 2011. 295–308.
    [105] Schwarzkopf M, Konwinski A, Abd-El-Malek M, Wilkes J. Omega: Flexible, scalable schedulers for large compute clusters. In: Proc. of the 8th ACM European Conf. on Computer Systems. Prague: ACM, 2013. 351–364. [doi: 10.1145/2465351.2465386]
    [106] Delgado P, Dinu F, Kermarrec AM, Zwaenepoel W. Hawk: Hybrid datacenter scheduling. In: Proc. of the 2015 USENIX Conf. on Usenix Annual Technical Conf. Santa Clara: USENIX Association, 2015. 499–510.
    [107] Karanasos K, Rao S, Curino C, Douglas C, Chaliparambil K, Fumarola GM, Heddaya S, Ramakrishnan R, Sakalanaga S. Mercury: Hybrid centralized and distributed scheduling in large shared clusters. In: Proc. of the 2015 USENIX Annual Technical Conf. Santa Clara: USENIX Association, 2015. 485–497.
    [108] Greenplum Database Resource Groups. 2022. https://greenplum.org/greenplum-database-resource-groups/
    [109] https://www.oceanbase.com
    [110] https://opengauss.org/zh/
    [111] https://docs.oracle.com/en/database/oracle/oracle-database/19/admin/managing-resources-with-oracle-database-resource-manager.html
    [112] https://learn.microsoft.com/zh-cn/sql/relational-databases/resource-governor/resource-governor-resource-pool?source=recommendations&view=sql-server-ver16
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刘海龙,王硕,侯舒峰,徐海洋,李战怀.云多租数据库资源规划调度技术综述.软件学报,2025,36(1):446-468

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  • Received:October 27,2023
  • Revised:May 06,2024
  • Online: November 06,2024
  • Published: January 06,2025
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