FRLDM: A Self-Optimizing Algorithm for Data Migration in Distributed Storage Systems

DOI

10.2991/iwmecs-15.2015.114How to use a DOI?

Keywords

data migration, fuzzy approximation, reinforcement learning, self-optimize.

Abstract

In distributed storage systems, data migration is an efficient method for improving system resource utility and service capacity, and balancing the load. However, the user accessing is changing over time and the state of a distributed system is in an unpredictable stochastic fluctuation, hence traditional heuristic policy- based methods are hard to work in such environment. This paper proposes a fuzzy reinforcement learning method for online data migration named FRLDM which can enable the systems to self-optimize and dynamically choose the candidate data for migration based on their recent access pattern and the current state of the system, thus minimizing the average access response time. The experimental results prove that FRLDM can improve the accesses performance significantly compared with heuristic policy-based methods.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tao Wang
AU  - Shihong Yao
AU  - Zhengquan Xu
AU  - Shan Jia
AU  - Lizhi Xiong
PY  - 2015/10
DA  - 2015/10
TI  - FRLDM: A Self-Optimizing Algorithm for Data Migration in Distributed Storage Systems
BT  - Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences
PB  - Atlantis Press
SP  - 578
EP  - 584
SN  - 2352-538X
UR  - https://doi.org/10.2991/iwmecs-15.2015.114
DO  - 10.2991/iwmecs-15.2015.114
ID  - Wang2015/10
ER  -