Reinforcement Learning for Dynamic Memory Allocation

TL;DR

This paper demonstrates that reinforcement learning can be effectively applied to dynamic memory allocation, outperforming traditional algorithms in adaptive and complex environments by learning from system interactions.

Contribution

It introduces a novel RL framework for dynamic memory management and evaluates its effectiveness against traditional strategies in various scenarios.

Findings

RL agents can match and surpass traditional memory allocation algorithms.

RL-based memory allocators perform well in adversarial request environments.

History-aware policies improve handling of complex request patterns.

Abstract

In recent years, reinforcement learning (RL) has gained popularity and has been applied to a wide range of tasks. One such popular domain where RL has been effective is resource management problems in systems. We look to extend work on RL for resource management problems by considering the novel domain of dynamic memory allocation management. We consider dynamic memory allocation to be a suitable domain for RL since current algorithms like first-fit, best-fit, and worst-fit can fail to adapt to changing conditions and can lead to fragmentation and suboptimal efficiency. In this paper, we present a framework in which an RL agent continuously learns from interactions with the system to improve memory management tactics. We evaluate our approach through various experiments using high-level and low-level action spaces and examine different memory allocation patterns. Our results show that RL can successfully train agents that can match and surpass traditional allocation strategies, particularly in environments characterized by adversarial request patterns. We also explore the potential of history-aware policies that leverage previous allocation requests to enhance the allocator's ability to handle complex request patterns. Overall, we find that RL offers a promising avenue for developing more adaptive and efficient memory allocation strategies, potentially overcoming limitations of hardcoded allocation algorithms.