Deep Reinforcement Learning-Based Flow Scheduling for Power-Efficient Data Center Networks

In this chapter, we introduce SmartFCT, which employs DRL coupled with 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 and adaptively generate an 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 to save power, and fine-grained margin configuration for active links avoids FCT violation of unexpected flow bursts.