The emergence of new applications brings multi-class traffic with diverse quality of service (QoS) demands in wide area networks (WANs), which motivates the research in traffic engineering (TE). In recent years, novel centralized TE schemes have employed heuristic or machine-learning techniques to orchestrate resources in closed systems, such as datacenter networks. However, these schemes suffer long delivery delay and high control overhead when applied to general WANs. Semi-centralized TE schemes have been proposed to address these drawbacks, providing lower delay and control overhead. Despite this, they suffer performance degradation dealing with volatile traffic. To provide low-delay service and keep high network utility, we propose an asynchronous multi-class traffic management scheme, AMTM. We first establish an asynchronous TE paradigm, in which distributed nodes instantly make traffic control decisions based on link prices. To manage varying traffic and control delivery time, we propose state-based iteration strategies of link pricing under different scenarios and investigate their convergence. Furthermore, we present a system design and corresponding algorithms. Simulation results indicate that AMTM outperforms existing schemes in terms of both delay reduction and scalability improvement. In addition, AMTM outperforms the semi-centralized scheme with 12-20$\%$ more network utility and achieves 2-7$\%$ less network utility compared to the theoretical optimum.

翻译：新型应用的出现带来了广域网中具有多样化服务质量（QoS）需求的多类流量，这推动了流量工程（TE）的研究。近年来，新颖的集中式TE方案采用启发式或机器学习技术来协调封闭系统（如数据中心网络）中的资源。然而，这些方案在应用于通用广域网时存在长交付延迟和高控制开销的问题。为克服这些缺陷，半集中式TE方案被提出，可提供更低的延迟和控制开销。尽管如此，它们在应对波动性流量时仍会出现性能下降。为提供低延迟服务并保持高网络效用，我们提出了一种异步多类流量管理方案AMTM。首先建立了异步TE范式，其中分布式节点基于链路价格即时做出流量控制决策。为管理变化的流量和控制交付时间，我们提出了不同场景下链路定价的基于状态的迭代策略，并研究了其收敛性。此外，还给出了系统设计与相应算法。仿真结果表明，AMTM在延迟降低和可扩展性提升方面均优于现有方案。此外，与半集中式方案相比，AMTM的网络效用提高了12-20%，相较于理论最优值仅降低2-7%。