Artificial Intelligence-Based Predictive Approximation of Belady\'s Optimal Page Replacement Algorithm Using Deep Learning and Reinforcement Learning

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• Semantic Scholar2026-07-16 23:31:51
  Artificial Intelligence-Based Predictive Approximation of Belady\'s Optimal Page Replacement Algorithm Using Deep Learning and Reinforcement Learning

  Efficient memory management is one of the fundamental responsibilities of modern operating systems, with page replacement algorithms playing a significant role in reducing page faults and improving overall system performance. Among the existing page replacement techniques, Belady's Optimal (OPT) algorithm provides the minimum achievable page fault rate by replacing the page whose next reference occurs farthest in the future. Although OPT represents the theoretical upper bound for page replacement performance, its practical implementation is impossible because future memory references are unknown during program execution [1]. Consequently, contemporary operating systems employ heuristic algorithms such as First-InFirst-Out (FIFO), Least Recently Used (LRU), Least Frequently Used (LFU), Clock, and Adaptive Replacement Cache (ARC), which utilize historical memory access information to approximate optimal replacement decisions [2]–[6]. Recent advances in Artificial Intelligence (AI) have enabled new approaches for predicting sequential data, making predictive memory management increasingly feasible. Deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, effectively learn temporal dependencies in sequential data, while Reinforcement Learning (RL) enables adaptive decision-making through continuous interaction with the execution environment [7]–[10]. These capabilities provide an opportunity to estimate future memory references rather than assuming perfect future knowledge. This paper proposes an Artificial Intelligence-Based Predictive Optimal Page Replacement (AI-POPR) framework that integrates an LSTM-based prediction engine with a reinforcement learning agent to approximate the behavior of Belady's Optimal algorithm. The framework analyzes historical memory reference sequences, predicts forthcoming page accesses, and dynamically selects victim pages using a hybrid decision strategy. Experimental evaluation using synthetic and benchmark workloads demonstrates that the proposed framework significantly reduces page faults and improves hit ratio compared with conventional algorithms while closely approximating the theoretical performance of OPT. The proposed AI-POPR framework provides an adaptive, intelligent, and practically deployable solution for next-generation operating systems, cloud computing platforms, virtualization environments, and edge computing infrastructures.