Autonomic workload performance tuning in large-scale data repositories

  • Basit RazaEmail author
  • Asma Sher
  • Sana Afzal
  • Ahmad Kamran Malik
  • Adeel Anjum
  • Yogan Jaya Kumar
  • Muhammad Faheem
Survey Paper


The workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.


Autonomic computing Workload management Large-scale data repositories Classification Prediction Adaptation Online transaction processing (OLTP) Decision support system (DSS) 



Database management system


Autonomic database management system


Autonomic workload performance tuning


Online analytical processing


Online transaction processing


Kernel canonical correlation analysis


Transaction Processing Council


Database administrator


Support vector machines


Query execution plan


Autonomic computing


Quality of service


K-nearest neighbor


Online social network


Customer behavior model graph


Garbage collection


Classification and regression tree


Business intelligence


Principal component analysis


Canonical correlation analysis


Query patroller


Predictions of query runtime


Service level agreement


External queue management system


Workload classification and forecasting


Monitor, Analyze, Plan, Execute, Knowledge


Descartes Modeling Language


Artificial neural network



The study is funded by COMSATS University Islamabad (CUI), Islamabad, Pakistan, under CIIT/ORIC-PD/17. We appreciate the suggestions and comments of esteemed reviewers that helped in improving the quality of paper.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Basit Raza
    • 1
    Email author
  • Asma Sher
    • 1
  • Sana Afzal
    • 1
  • Ahmad Kamran Malik
    • 1
  • Adeel Anjum
    • 1
  • Yogan Jaya Kumar
    • 2
  • Muhammad Faheem
    • 3
  1. 1.Department of Computer ScienceCOMSATS University Islamabad (CUI)IslamabadPakistan
  2. 2.Faculty of Information and Communication TechnologyUniversiti Teknikal Malaysia MelakaMelakaMalaysia
  3. 3.Department of Computer EngineeringAbdullah Gul UniversityKayseriTurkey

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