SmartFCT: Improving power-efficiency for data center networks with deep reinforcement learning

Reducing the power consumption of Data Center Networks (DCNs) and guaranteeing the Flow Completion Time (FCT) of applications in DCNs are two major concerns for data center operators. However, existing works cannot realize the two goals together because of two issues: (1) dynamic traffic pattern in DCNs is hard to accurately model; (2) an optimal flow scheduling scheme is computationally expensive. In this paper, we propose SmartFCT, which employs the Deep Reinforcement Learning (DRL) coupled with Software-Defined Networking (SDN) to improve the power efficiency of DCNs and guarantee FCT. SmartFCT dynamically collects traffic distribution from switches to train its DRL model. The well-trained DRL agent of SmartFCT can quickly analyze the complicated traffic characteristics using neural networks and adaptively generate a action for scheduling flows and deliberately configuring margins for different links. Following the generated action, flows are consolidated into a few of active links and switches for saving power, and fine-grained margin configuration for active links avoids FCT violation of unexpected flow bursts. Simulation results show that SmartFCT can guarantee FCT and save up to 12.2% power consumption, compared with the state-of-the-art solutions.