Storage resource scheduling optimization method for separated data center based on deep reinforcement learning - YUAN Zhengli; GUO Shaoyong; HU Xin; TONG Jie; HAO Jiakai (Tongxin xuebao)
To address the high overhead of scheduling, deployment, and algorithm execution in disaggregated data center storage systems, a deployment solution was proposed that embeded the storage scheduling mechanism into white-box switches. By enabling the interaction between the switches and data processing unit (DPU), the system achieved automated data migration, thereby freeing up I/O resources occupied by storage access. Through a Bayesian-based data temperature sensitivity optimization strategy integrating access volume and access interval, it fed the heat information to a deep reinforcement learning (DRL) scheduler that minimized access latency. Simulations show that, against rule-based LRU and FIFO, latency drops from 38.4% to 67.2%. Against DQN, PPO and DDPG, latency drops from 12.8% to 43.2 %.