Strategies and Performance Analysis of Queries Associated with Cloud Database

  • Mishra Jyoti PrakashEmail author
  • Prasad Suman Sourav
  • Mishra Sambit Kumar
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)


In the present day, cloud computing plays a vital role towards technologies associated with service. The primary objective of cloud computing is to make people compute and store resources properly and effectively. Therefore to improve the performance in the cloud, it may require optimization towards processing data. It is obvious that cloud computing enhances sharing computing power as well as storage for a number of applications towards the database with heterogeneity. But it has been observed that the way a number of applications is influenced by various cloud platforms, the high scale generated data the data generated may be increased as well as consumed during the applications. Accordingly with the availability of virtual machines, cloud computing may enable users for the usage of resources to execute complex queries efficiently on large-scale data. The complete autonomy towards each node in the large database environments may be expected towards the services through external communication along with the experimentation towards optimizing query terms. Accordingly, unifying and authorization linked with the desired problem may be partially linked with specific points towards information retrieval along with its characteristics. In that scenario, the large database may be linked with the virtual server towards providing services to the relevant data. Also the database associated with the cloud may be associated with the various instances linked with different heterogeneous databases. Many techniques have already been presented linked to processing queries in cloud databases. In this paper, it has been proposed to optimize query processing linked with virtual data associated with virtual servers. Accordingly, the generation of queries along with the execution query plans may also attempt to optimize the performance of virtual databases.


Cloud database Virtualization Query plans Schema Virtual machine 


  1. 1.
    C.M. Costa, A.L. Sousa, Adaptive query processing in cloud database systems, in 2013 Third International Conference on Cloud and Green Computing (CGC) (IEEE, 2013), pp. 201–202Google Scholar
  2. 2.
    M. Stonebraker, D. Abadi, D.J. DeWitt, S. Madden, E. Paulson, A. Pavlo, A. Rasin, MapReduce and parallel DBMSs: friends or foes? Commun. ACM 53(1), 64–71 (2010)CrossRefGoogle Scholar
  3. 3.
    K. Anyanwu, H. Kim, P. Ravindra, Algebraic optimization for processing graph pattern queries in the cloud. Internet Comput. 17(2), 52–61. IEEE (2013)Google Scholar
  4. 4.
    B. Theeten, N. Janssens, CHive: bandwidth optimized continuous querying in distributed clouds”, Cloud [1]. D.J. Abadi, Data management in the cloud: limitations and opportunities. IEEE Data Eng. Bull. 33(2), 3–12 (2009)CrossRefGoogle Scholar
  5. 5.
    M.N. Garofalakis, Y.E. Ioannidis, Multi-dimensional resource scheduling for parallel queries, in ACM SIGMOD Record, ACM (vol. 25, no. 2) (1996), pp. 365–376CrossRefGoogle Scholar
  6. 6.
    H. Andrade, T. Kurc, A. Sussman, J. Saltz, Multiple query optimization for data analysis applications on clusters of SMPs, in 2002 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, May 2002 (IEEE, 2002), pp. 154–154Google Scholar
  7. 7.
    T. Dokeroglu, M.A. Bayir, A. Cosar, Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries. Appl. Soft Comput. 30, 72–82 (2015)CrossRefGoogle Scholar
  8. 8.
    T. Dokeroglu, S.A. Sert, M.S. Cinar, Evolutionary multi-objective query workload optimization of Cloud data warehouses. Sci. World J. (2014)Google Scholar
  9. 9.
    N. Bruno, S. Jain, J. Zhou, Continuous cloud-scale query optimization and processing. Proc. VLDB Endow. 6(11), 961–972 (2013)CrossRefGoogle Scholar
  10. 10.
    A. Mesmoudi, M.S. Hacid, F. Toumani, Benchmarking SQL on MapReduce systems using large astronomy databases. Distrib. Parallel Databases 34(3) (2016)CrossRefGoogle Scholar
  11. 11.
    M.D. de Assuncao, A. da. Silva Veith, R. Buyya, Distributed data stream processing and edge computing: a survey on resource elasticity and future directions. J. Netw. Comput. Appl. 103 (2018)Google Scholar
  12. 12.
    S. Gopalani, R. Arora, Comparing Apache Spark and Map reduce with performance analysis using K-means. Int. J. Comput. Appl. 113(1), 8–11 (2015)Google Scholar
  13. 13.
    M. Bertoni, S. Ceri, A. Kaitoua, P. Pinoli, Evaluating cloud frameworks on genomic applications, in Proceedings of the 3rd IEEE International Conference on Big Data, IEEE Big Data 2015, pp. 193–202, November 2015Google Scholar
  14. 14.
    L. Gu, H. Li, Memory or time: performance evaluation for iterative operation on Hadoop and Spark, in Proceedings of the 15th IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2013, pp. 721–727, November 2013Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mishra Jyoti Prakash
    • 1
    Email author
  • Prasad Suman Sourav
    • 2
  • Mishra Sambit Kumar
    • 3
  1. 1.Gandhi Institute for Education and TechnologyBhubaneswarIndia
  2. 2.Ajay Binay Institute of TechnologyCuttackIndia
  3. 3.Gandhi Institute for Education and TechnologyBaniatangiIndia

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