SLAS: An efficient approach to scaling round-robin striped volumes
ACM Transactions on Storage (TOS)
A reinforcement learning framework for online data migration in hierarchical storage systems
The Journal of Supercomputing
Dynamic tuning of online data migration policies in hierarchical storage systems using reinforcement learning
Time-varying management of data storage
HotDep'05 Proceedings of the First conference on Hot topics in system dependability
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We present a policy-based architecture STEPS for lifecycle management (LCM) in a mass scale distributed file system. The STEPS architecture is designed in the context of IBM's SAN File System (SFS) and leverages the parallelism and scalability offered by SFS, while providing a centralized point of control for policy-based management. The architecture uses novel concepts like Policy Cache and Rate-Controlled Migration for efficient and non-intrusive execution of the LCM functions, while ensuring that the architecture scales with very large number of files. The architecture has been implemented and used for lifecycle management in a distributed deployment of SFS with heterogeneous data. We conduct experiments on the implementation to study the performance of the architecture. We observed that STEPS is highly scalable with increase in the number as well as the size of the file objects hosted by SFS. The performance study also demonstrated that most of the efficiency of policy execution is derived from Policy Cache. Further, a ratecontrol mechanism is necessary to ensure that users are isolated from LCM operations.