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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
  • 255 Downloads

Abstract

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.

Keywords

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

Abbreviations

DBMS

Database management system

ADBMS

Autonomic database management system

AWPT

Autonomic workload performance tuning

OLAP

Online analytical processing

OLTP

Online transaction processing

KCCA

Kernel canonical correlation analysis

TPC

Transaction Processing Council

DBA

Database administrator

SVM

Support vector machines

QEP

Query execution plan

AC

Autonomic computing

QoS

Quality of service

KNN

K-nearest neighbor

OSN

Online social network

CBMG

Customer behavior model graph

GC

Garbage collection

CRT

Classification and regression tree

BI

Business intelligence

PCA

Principal component analysis

CCA

Canonical correlation analysis

QP

Query patroller

PQR

Predictions of query runtime

SLA

Service level agreement

EQMS

External queue management system

WCF

Workload classification and forecasting

MAPEK

Monitor, Analyze, Plan, Execute, Knowledge

DML

Descartes Modeling Language

ANN

Artificial neural network

Notes

Acknowledgements

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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