Deep Reinforcement Learning based Data Placement optimization in Data Center Networks

Intelligent data placement in data center networks for big data applications is a constructive approach to reduce the data movement delay, which can further improve the user service latency. Traditionally, data placement methods have relied on the study of the prior distribution of data requests discovered via network trace data analysis. Data placement in the network is an online decision-making problem. Despite the available machine learning architectures, how to deal with dynamic network conditions and user requests that vary over time is still an unanswered question. Toward this goal, this research formulates a Markov Decision Process (MDP) model based on deep reinforcement learning (RL) that takes into account the delay associated with data movement and service latency. The proposed method presents a novel intelligent data placement approach that leverages two state-of-the-art RL techniques; Deep Q-Network (DQN) and Soft Actor-Critic (SAC). Additionally, a prioritized butter is incorporated to replay important transitions more frequently and therefore, learn more efficiently. Extensive experiments and analyses conducted on Microsoft Research Cambridge (MSR) traces show the effectiveness and superior performance of the proposed approach for minimizing overall user service latency. The results highlight that our model outperforms the existing techniques in terms of the overall reward, achieving optimal data placement in real-world scenarios.