DAR: Deadline-Aware Rerouting for Mix-flows in Datacenter Networks
Jinbin Hu, Rui Zhi, Shuying Rao, Ying Liu, Jin Wang · 2024
In modern datacenter networks (DCNs), the booming online data-intensive applications generate mix-flows with or without deadlines. Balancing these heterogenous flows among parallel equal-cost paths to meet the tight deadlines is crucial. However, due to the unaware of deadlines, the existing load balancing mechanisms cannot choose suitable (re)routing path for mix-flows to meet their respective stringent requirements. In this paper, we propose a deadline-aware rerouting scheme called DAR, which applies different routing strategies for mix-flows. Specifically, DAR first perceives the deadline flows and then categorizes them based on the urgency of the deadline, and employs different (re)routing strategies to ensure that flows with more urgent deadlines are completed earlier. The NS-3 simulation results show that DAR effectively balances mix-flows. For example, compared to the state-of-the-art load balancing schemes, DAR reduces the deadline miss rate and the average flow completion time (AFCT) by up to 38% and 35.5%, respectively.