Reinforcement Learning Approach for Data Migration in Hierarchical Storage Systems
نویسندگان
چکیده
There are several shortcomings in the existing data migration techniques in Hierarchical Storage Systems (HSS). The first and the most important among them is that data migration policies are user defined hence static and reactive. Secondly, data migration at the host side is not yet completely explored. The other major drawbacks are that each storage tier is modelled as an agent; the data migration methodology is I/O triggered and the tier cost represented as a complex fuzzy rule base (FRB). This paper proposes a simple and single data migration agent in the HSS. The data migration agent will be a standalone daemon which implements the Reinforcement Learning (RL) algorithm. The agent will formulate and tune policies based on which the data migration will take place. The proposed model in this paper aims to achieve comparable results with existing systems in data migration, input/output queue length and response time of storage tiers.
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