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Knowledge and Information Systems
Published in: Knowledge and Information Systems 1/2019

04-09-2018 | Survey Paper

Autonomic workload performance tuning in large-scale data repositories

Authors: Basit Raza, Asma Sher, Sana Afzal, Ahmad Kamran Malik, Adeel Anjum, Yogan Jaya Kumar, Muhammad Faheem

Published in: Knowledge and Information Systems | Issue 1/2019

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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.
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Metadata
Title
Autonomic workload performance tuning in large-scale data repositories
Authors
Basit Raza
Asma Sher
Sana Afzal
Ahmad Kamran Malik
Adeel Anjum
Yogan Jaya Kumar
Muhammad Faheem
Publication date
04-09-2018
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 1/2019
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1272-0

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