Recent advances in fast optical switching show promise in meeting the high goodput and low latency requirements of datacenter networks. We present NegotiaToR, a simple network architecture for optical reconfigurable DCNs that utilizes on-demand scheduling to handle dynamic traffic. In NegotiaToR, racks exchange scheduling messages through an in-band control plane and distributedly calculate non-conflicting paths from binary traffic demand information. Optimized for incasts, it also provides opportunities to bypass scheduling delays. NegotiaToR is compatible with prevalent flat topologies, and is tailored towards a minimalist design for on-demand reconfigurable DCNs, enhancing practicality. Through large-scale simulations, we show that NegotiaToR achieves both small mice flow completion time and high goodput on two representative flat topologies, especially under heavy loads. Particularly, the flow completion time of mice flows is one to two orders of magnitude better than the state-of-the-art traffic-oblivious reconfigurable DCN design.

Tuning parameters in Data Center Networks (DCN) has long been a nuisance and one of the reasons service providers are reluctant to deploy new mechanisms in their production environments. Despite the excessive time and resources devoted to finding better configurations, a "one-size-fits-all" solution remains elusive. Neither manual configuration by experts nor black-box optimization can address the challenges of network heterogeneity and dynamics. One essential factor impeding efficient and stable parameter optimization is the need to explore in real environments, which has a long convergence time alongside the risk of performance degradation. To address this problem, we build a twin performance model of the physical DCN that approximates the mapping from parameters to Quality of Service (QoS) metrics for fast and safe performance inference and present a DCN configuration framework called Iphicles. Leveraging gradients provided by differentiable performance models built with Graph Neural Networks (GNN), Iphicles can automatically recommend better parameters efficiently and stably. Experimental results based on extensive simulation demonstrate that in complex scenarios with mixed and dynamic traffic, Iphicles can deliver parameters that lead to evident improvements in flow completion time (FCT) for both mice and elephant flows simultaneously, with minimum convergence time while maintaining performance stability during the optimization process.

Recent advances in fast optical switching technology show promise in meeting the high goodput and low latency requirements of datacenter networks (DCN). We present NegotiaToR, a simple network architecture for optical reconfigurable DCNs that utilizes on-demand scheduling to handle dynamic traffic. In NegotiaToR, racks exchange scheduling messages through an in-band control plane and distributedly calculate non-conflicting paths from binary traffic demand information. Optimized for incasts, it also provides opportunities to bypass scheduling delays. NegotiaToR is compatible with prevalent flat topologies, and is tailored towards a minimalist design for on-demand reconfigurable DCNs, enhancing practicality. Through large-scale simulations, we show that NegotiaToR achieves both small mice flow completion time (FCT) and high goodput on two representative flat topologies, especially under heavy loads. Particularly, the FCT of mice flows is one to two orders of magnitude better than the state-of-the-art traffic-oblivious reconfigurable DCN design.

Traffic modeling of Datacenter Network (DCN) today is over-simplified, deviating from the ground truth. Adopted by numerous researchers, the common practice relies on the assumptions of traffic homogeneity and independence for ease of use. Based on our investigation of a real-world traffic dataset, we disprove these assumptions and point out the severe fidelity issue of the common practice that could invalidate many motivations and conclusions from influential research works. In this paper, we present Encore, a DCN traic modeling framework for ine-grained traic modeling and high-fidelity synthetic traffic generation. Leveraging machine learning techniques, Encore effectively extracts and preserves essential distribution and sequential features from raw traic. Preliminary experiments demonstrate that the traic generated by Encore not only restores the key features of real traffic but also achieves high consistency when used to evaluate network performance. We envision further expanding Encore to full-process traffic modeling and generation, and expect these critical improvements in traffic models can facilitate the DCN performance evaluation and optimization.

Optical switching technology has developed fast in recent years, which has the potential to provide high good put as well as low latency in reconfigurable data center networks (RDCN). However, existing optical RDCN proposals fail to balance good put, latency, and design complexity well. In this paper, we introduce NegotiaToR, a simple optical RDCN architecture. NegotiaToR utilizes on-demand scheduling to ensure high performance and reduces possible deployment complexity with an in-band control protocol to do the schedule distributedly. Our preliminary evaluation shows that NegotiaToR outperforms the state-of-the-art optical RDCN proposal under similar complexity in both goodput and flow completion time (FCT).