Sg: Automated tuning algorithm for storage systems based on simulated environments and group climbing.

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]