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Sg: Automated tuning algorithm for storage systems based on simulated environments and group climbing.
- Source :
- Cluster Computing; Jul2024, Vol. 27 Issue 4, p4841-4853, 13p
- Publication Year :
- 2024
-
Abstract
- With the increasing volume of data on the web, distributed file systems such as HDFS are widely used to provide efficient, fast and reliable access to the ever-growing volumes of data. Parameter tuning, a core performance management task, directly affects the performance of storage systems. However, large, non-linear, discrete and non-digital storage system configuration parameters significantly increase the storage system parameter tuning burden. Traditional manual tuning methods rely on a lot of a priori knowledge and have high tuning costs. In this paper, we propose a Smart gambler (SG) algorithm based on a simulated environment and Hill Climbing to reduce the probability of falling into a local optimum. In comparative experiments on publicly available datasets, SG achieves an average improvement of 3.14 % and 5.02 % in the highest tuning value over search algorithms such as Genetic Algorithm and an average time saving of 32.1 % and 50.6 % in tuning time for both workloads, respectively. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13867857
- Volume :
- 27
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Cluster Computing
- Publication Type :
- Academic Journal
- Accession number :
- 178805402
- Full Text :
- https://doi.org/10.1007/s10586-023-04206-4