In data center networks (DCNs), flows with different objectives coexist and compete for limited network resources (such as bandwidth and buffer space). Without harmonious resource planning, chaotic competition among these flows would lead to severe performance degradation. Furthermore, low latency is critical for many emerging applications such as augmented reality(AR), virtual reality(VR) and telepresence, making the network control problem even more challenging. To address the above issues, this paper novelly proposes a receiver-driven two-layer control framework called HOPASS, which incorporates a slow control layer and a fast control layer to strike a balance among multiple network sharing objectives and achieve low latency. The slow control layer ensures bandwidth guarantee in an aggregated flow level by solving a multi-objective network utility maximization (NUM) problem using an online learning approach. Then the results will be dispatched to the data plane by configuring weights in the switches with weighted fair queue functionality. Under the configuration dictated by the slow control layer, the fast control layer leverages the token packets sent by receivers to dynamically probe and reserve network capacity, so that it can proactively prevent network congestion and guarantee low latency data delivery. To evaluate the proposed framework, we have implemented HOPASS in ns-3 and conduct extensive experiments under various network scenarios. The simulation results show that HOPASS achieves near-optimal performance in terms of bandwidth allocation in multi-objective scenarios and also guarantees low end-to-end delay. Moreover, it outperforms DCTCP and NewReno in terms of average bandwidth utilization and global total network utility at the aggregated flow level. Therefore, we conclude that HOPASS provides an effective framework for DCNs when considering both multi-objective optimization and a low latency network.

在数据中心网络 (DCN) 中，具有不同目标的流共存并争夺有限的网络资源（例如带宽和缓冲区空间）。如果没有和谐的资源规划，这些流之间的混乱竞争将导致严重的性能下降。此外，低延迟对于增强现实 (AR)、虚拟现实 (VR) 和远程呈现等许多新兴应用至关重要，这使得网络控制问题更具挑战性。针对上述问题，本文新颖地提出了一种接收器驱动的两层控制框架，称为HOPASS，它包含一个慢控制层和一个快速控制层，以在多个网络共享目标之间取得平衡并实现低延迟。慢速控制层通过使用在线学习方法解决多目标网络效用最大化 (NUM) 问题，确保聚合流级别的带宽保证。然后，通过在具有加权公平队列功能的交换机中配置权重，将结果分派到数据平面。在慢控制层指定的配置下，快速控制层利用接收方发送的令牌包动态探测和预留网络容量，从而主动防止网络拥塞并保证低延迟的数据传输。为了评估提议的框架，我们实施了HOPASS在 ns-3 中并在各种网络场景下进行广泛的实验。仿真结果表明，HOPASS在多目标场景下的带宽分配方面实现了接近最优的性能，并且保证了低端到端延迟。此外，它在聚合流级别的平均带宽利用率和全球总网络效用方面优于 DCTCP 和 NewReno。因此，我们得出结论，在考虑多目标优化和低延迟网络时，HOPASS为DCN提供了有效的框架。