• Li F (2019) Cloud-native database systems at Alibaba: opportunities and challenges. Proc VLDB Endowment 12(12):2263–2272

    Article 

    Google Scholar
     

  • Ton That DH, Wagner J, Rasin A, Malik T (2019) PLI+: efficient clustering of cloud databases. Distributed Parallel Databases 37(1):177–208

    Article 

    Google Scholar
     

  • GB/T 33136–2016 Information technology service—Service capability maturity model of data center. Available at: https://openstd.samr.gov.cn/bzgk/gb/newGbInfo?hcno=F7A2242CAA62FD4466E8BAB0F92661D8. Accessed 20 June 2022

  • Zhan C, Su M, Wei C et al (2019) AnalyticDB: real-time OLAP database system at alibaba cloud. Proc VLDB Endowment 12(12):2059–2070

    Article 

    Google Scholar
     

  • Antonopoulos P, Budovski A, Diaconu C et al (2019) Socrates: the new sql server in the cloud. In: Proceedings of the 2019 International Conference on Management of Data, pp 1743–1756

    Chapter 

    Google Scholar
     

  • Pang Z, Lu Q, Chen S et al (2021) ArkDB: a key-value engine for scalable cloud storage services. In: Proceedings of the 2021 International Conference on Management of Data, pp 2570–2583

    Chapter 

    Google Scholar
     

  • Chen Y, Zhao F, Lu Y, Chen X (2021) Dynamic task offloading for mobile edge computing with hybrid energy supply. Tsinghua Sci Technol. https://doi.org/10.26599/TST.2021.9010050

  • Xu J, Li D, Gu W, Chen Y (2022) UAV-assisted task offloading for IoT in smart buildings and environment via deep reinforcement learning. Build Environ 222:109218

  • Chen Y, Zhao F, Chen X, Wu Y (2021) Efficient multi-vehicle task offloading for mobile edge computing in 6G networks. IEEE Trans Veh Technol 71(5):4584–4596

    Article 

    Google Scholar
     

  • Xu X, Jiang Q, Zhang P, Cao X et al (2022) Game theory for distributed IoV task offloading with fuzzy neural network in edge computing. IEEE Trans Fuzzy Syst

  • Chen Y, Gu W, Li K (2022) Dynamic task offloading for internet of things in mobile edge computing via deep reinforcement learning. Int J Commun Syst 2022:e5154

  • Huang J, Tong Z, Feng Z (2022) Geographical POI recommendation for internet of things: a federated learning approach using matrix factorization. Int J Commun Syst 2022:e5161

  • Xu X, Tian H, Zhang X, Qi L, He Q, Dou W (2022) DisCOV: distributed COVID-19 detection on X-ray images with edge-cloud collaboration. IEEE Trans Serv Comput 15(3):1206–1219

  • Sandhu AK (2021) Big data with cloud computing: discussions and challenges. Big Data Mining Analytics 5(1):32–40

    Article 

    Google Scholar
     

  • Zhang Y, Zhang H, Cosmas J, Jawad N et al (2020) Internet of radio and light: 5G building network radio and edge architecture. Intell Converged Netw 1(1):37–57

    Article 

    Google Scholar
     

  • Comuzzi M, Patel A (2016) How organisations leverage big data: a maturity model. Ind Manag Data Syst 116(8):1468–1492

    Article 

    Google Scholar
     

  • Guoqiang GAI, Tingkun Y, Jun XIE, Chenning HUANG (2021) Database service ecology and system in China. Inform Commun Technol Policy 47(1):57–62


    Google Scholar
     

  • Spillner J, Bogado Y, Benítez W, López Pires F (2018) Co-transformation to cloud-native applications: development experiences and experimental evaluation. In: 8th International Conference on Cloud Computing and Services Science (CLOSER). SciTePress, Funchal, pp 19–21


    Google Scholar
     

  • Chen W, Liu C, Xing F, Peng G, Yang X (2021) Establishment of a maturity model to assess the development of industrial AI in smart manufacturing. J Enterp Inf Manag 35(3):701–728

    Article 

    Google Scholar
     

  • Tarhan A, Turetken O, Reijers HA (2016) Business process maturity models: a systematic literature review. Inf Softw Technol 75:122–134

    Article 

    Google Scholar
     

  • Sadiq RB, Safie N, Abd Rahman AH et al (2021) Artificial intelligence maturity model: a systematic literature review. PeerJ Comput Sci 7:e661

    Article 

    Google Scholar
     

  • Felch V, Asdecker B (2020) Quo Vadis, business process maturity model? Learning from the past to envision the future. In: International conference on business process management. Springer, Cham, pp 368–383

    Chapter 

    Google Scholar
     

  • Dutta A, Roy R, Seetharaman P (2022) An assimilation maturity model for IT governance and auditing. Inf Manag 59(1):103569

    Article 

    Google Scholar
     

  • Qi L, Hu C, Zhang X, Khosravi MR, Sharma S, Pang S, Wang T (2020) Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Transact Industr Inform 17(6):4159–4167

    Article 

    Google Scholar
     

  • Wolfswinkel JF, Furtmueller E, Wilderom CP (2013) Using grounded theory as a method for rigorously reviewing literature. Eur J Inf Syst 22(1):45–55

    Article 

    Google Scholar
     

  • Kitchenham BA, Budgen D, Brereton P (2015) Evidence-based software engineering and systematic reviews. CRC Press, Boca Raton

    Book 

    Google Scholar
     

  • Chen Y, Xing H, Ma Z, Chen X, Huang J (2022) Cost-efficient edge caching for NOMA-enabled IoT services. Chin Commun

  • Corbett JC, Dean J, Epstein M et al (2013) Spanner: Google’s globally distributed database. ACM Transact Comput Syst (TOCS) 31(3):1–22

    Article 

    Google Scholar
     

  • Huang D, Liu Q, Cui Q et al (2020) TiDB: a raft-based HTAP database. Proc VLDB Endowment 13(12):3072–3084

    Article 

    Google Scholar
     

  • Verbitski A, Gupta A, Saha D et al (2017) Amazon aurora: design considerations for high throughput cloud-native relational databases. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp 1041–1052

    Chapter 

    Google Scholar
     

  • Cao W, Liu Y, Cheng Z et al (2020) POLARDB meets computational storage: efficiently support analytical workloads in cloud-native relational database. In: 18th USENIX conference on file and storage technologies (FAST 20), pp 29–41


    Google Scholar
     

  • Cao W, Liu Z, Wang P et al (2018) PolarFS: an ultra-low latency and failure resilient distributed file system for shared storage cloud database. Proc VLDB Endowment 11(12):1849–1862

    Article 

    Google Scholar
     

  • Depoutovitch A, Chen C, Chen J et al (2020) Taurus database: how to be fast, available, and frugal in the cloud. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp 1463–1478

    Chapter 

    Google Scholar
     

  • Huang J, Lv B, Wu Y, Chen Y, Shen X (2021) Dynamic admission control and resource allocation for mobile edge computing enabled small cell network. IEEE Trans Veh Technol 71(2):1964–1973

    Article 

    Google Scholar
     

  • Chen Y, Liu Z, Zhang Y, Wu Y, Chen X, Zhao L (2020) Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things. IEEE Transact Industr Inform 17(7):4925–4934

    Article 

    Google Scholar
     

  • Nath S, Wu J (2020) Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems. Intell Converged Netw 1(2):181–198

    Article 

    Google Scholar
     

  • Herodotou H, Lim H, Luo G et al (2011) Starfish: a self-tuning system for big data analytics. 5th Biennial Conf Innovative Data Syst Res (CIDR’11) 11(2011):261–272. Asilomar

  • Li L, Gruenwald L (2016) An SLA and operation cost aware performance re-tuning algorithm for cloud databases. 2016 IEEE 9th Int Conf Cloud Comput (CLOUD) 2016:966–969

  • Wang X, Li N, Zhang L, Zhang X, Zhao Q (2021) Rapid trend prediction for large-scale cloud database KPIs by clustering. 2021 IEEE/ACM Int Workshop Cloud Intell (CloudIntelligence) 2021:1–6

  • Xiong P, Chi Y, Zhu S, Moon HJ, Pu C, Hacgümüş H (2014) SmartSLA: cost-sensitive management of virtualized resources for CPU-bound database services. IEEE Transact Parallel Distribut Syst 26(5):1441–1451

    Article 

    Google Scholar
     

  • Wang L, Xu J, Zhao M (2012) Application-aware cross-layer virtual machine resource management. In: Proceedings of the 9th international conference on Autonomic computing, pp 13–22

    Chapter 

    Google Scholar
     

  • Sotiriadis S, Bessis N, Buyya R (2018) Self managed virtual machine scheduling in cloud systems. Inf Sci 433:381–400

    Article 

    Google Scholar
     

  • Tan J, Zhang T, Li F et al (2019) iBTune: individualized buffer tuning for large-scale cloud databases. Proc VLDB Endowment 12(10):1221–1234

    Article 

    Google Scholar
     

  • Armağan Ö, Gören-Sümer L (2014) Feedback control for multi-resource usage of virtualised database server. Comput Electr Eng 40(5):1683–1702

    Article 

    Google Scholar
     

  • Omara FA, Khattab SM, Sahal R (2014) Optimum resource allocation of database in cloud computing. Egypt Inform J 15(1):1–12

    Article 

    Google Scholar
     

  • Zhang X, Wu H, Chang Z et al (2021) ResTune: resource oriented tuning boosted by meta-learning for cloud databases. In: Proceedings of the 2021 International Conference on Management of Data, pp 2102–2114

    Chapter 

    Google Scholar
     

  • Shen Z, Subbiah S, Gu X, Wilkes J (2011) CloudScale: elastic resource scaling for multi-tenant cloud systems. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, pp 1–14


    Google Scholar
     

  • Salmanian Z, Izadkhah H, Isazadeh A (2022) Auto-scale resource provisioning in IaaS clouds. Comput J 65(2):297–309

    MathSciNet 
    Article 

    Google Scholar
     

  • JV BB, Dharma D (2018) HAS: hybrid auto-scaler for resource scaling in cloud environment. J Parallel Distribut Comput 120:1–15

    Article 

    Google Scholar
     

  • Narasayya V, Menache I, Singh M et al (2015) Sharing buffer pool memory in multi-tenant relational database-as-a-service. Proc VLDB Endowment 8(7):726–737

    Article 

    Google Scholar
     

  • Cao W, Zhang Y, Yang X et al (2021) Polardb serverless: a cloud native database for disaggregated data centers. In: Proceedings of the 2021 International Conference on Management of Data, pp 2477–2489

    Chapter 

    Google Scholar
     

  • Das S, Li F, Narasayya VR, König AC (2016) Automated demand-driven resource scaling in relational database-as-a-service. In: Proceedings of the 2016 International Conference on Management of Data, pp 1923–1934

    Chapter 

    Google Scholar
     

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