RL-Based Fragment Allocation and Replication for Distributed Heritage Multimedia Databases

Document Type : Original Article

Authors

1 faculty of computer and information science, information system, Ain shams university

2 Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt

3 Museums and Archaeology Sites Management Department Faculty of Archeology, Ain Shams University.

Abstract

Optimizing fragment allocation and replication in distributed heritage multimedia databases is crucial for minimizing query execution costs in dynamic, resource-constrained environments. Existing heuristics, such as VFAR (Vertical Fragment Allocation and Replication) often neglect inter-fragment dependencies, join relationships, and site capacity limitations, which can result in inefficient allocations. This paper introduces RL-FAWM (Reinforcement Learning based Fragment Allocation and Replication for Workload-aware Multimedia Systems), a Q-learning framework that models fragment placement as a Markov Decision Process. RL-FAWM incrementally learns effective allocation and replication strategies over multiple episodes using a Q-table, allowing the system to adapt to dynamic workload changes. The approach incorporates structured workload matrices, including read frequency (FRM), manipulation intensity (FMM), and co-access patterns (FCAM), along with inter-site communication costs and strict storage capacity constraints. A cost estimator provides real-time feedback to guide the learning process toward globally optimal and constraint-compliant configurations.Experimental results on a distributed multimedia case study demonstrate that RL-FAWM consistently reduces execution costs and avoids site overloading, outperforming VFAR while minimizing inter-site join costs. These findings underscore the potential of reinforcement learning for adaptive, scalable, and constraint-aware data management in digital preservation systems.

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