Recently, transformer-based Unmanned Aerial Vehicle (UAV) trackers have achieved notable success. However, the computationally intensive transformer model limits these trackers to static templates and shallow backbone networks, hampering their discriminative power and localization precision. Here, we propose a novel discriminative Siamese deep-coupling framework. This framework constructs a lightweight fine-grid anchor-free Siamese tracker with high spatial resolution specifically tailored for UAV scenarios, and complements its discriminative power with a targeted online discriminator. To achieve this, an efficient distractor detector is developed via knowledge transfer, enabling targeted detection of distractors that disturb the Siamese tracker. These distractors are utilized as training samples to construct a targeted online discriminator, which is deeply coupled with the Siamese tracker to enhance its discriminative power and specifically suppress hard distractors that hinder tracking performance. Additionally, a leading principal submatrix cluster sample space model and a scene-aware dynamic update strategy are developed to purify online samples and dynamically schedule the online discriminator update, significantly reducing the computational cost of the online discriminator optimization and boosting the tracker’s real-time performance. Finally, extensive experiments on eight UAV tracking benchmarks demonstrate that our tracker surpasses state-of-the-art transformer-based UAV trackers while achieving 70 FPS on CPU.

ECN marking has been integrated into datacenter switches to enable high-throughput and low-latency transport. We observe that current marking schemes are coarse-grained: they blindly mark all flows when congestion occurs, causing large flows to occupy undeserved bandwidth and preventing newly arriving small flows from finishing quickly. In this paper, we propose DiffECN, a differential marking strategy that marks only the flows that are the culprits of congestion and protects the remaining flows from being limited. We have implemented it in the Barefoot Tofino switch and performed extensive evaluations via both physical testbed and large-scale simulations. The results show that DiffECN can restrain flows responsible for congestion successfully while providing desirable network performance. For instance, compared to the legacy way of ECN marking, DiffECN achieves up to 32.5% (40.1%) lower average (99th percentile) flow completion time (FCT) for small flows while delivering similar FCT for large flows under production workloads.

Random Early Detection (RED) has been integrated into datacenter switches as a fundamental Active Queue Management (AQM) for decades. The accurate configuration of RED parameters is crucial to achieving high throughput and low latency. However, due to the highly dynamic nature of workloads in datacenter networks, maintaining consistently high performance with statically configured RED thresholds poses a challenge. Prior work applies reinforcement learning to predict proper thresholds, but their real-world deployment has been hindered by poor tail performance caused by instability. In this paper, we propose$\textsf {PRED}$, a novel system that enables automatic and stable RED parameter adjustment in response to traffic dynamics. Specifically, the system employs a Multiplicative-Increase Multiplicative-Decrease (MIMD) strategy to dynamically adapt to flow concurrency while utilizing an Additive-Increase Additive-Decrease (AIAD) mechanism to adapt to flow distribution. We perform extensive evaluations on our physical testbed and large-scale simulations. The results demonstrate that$\textsf {PRED}$can keep up with the real-time network dynamics generated by realistic workloads. For instance, compared with the static-threshold-based methods,$\textsf {PRED}$keeps 66% shorter switch queue length and obtains up to 80% lower Flow Completion Time (FCT). Compared with the state-of-the-art learning-based method,$\textsf {PRED}$reduces the tail FCT by 34%.

Wi-Fi Direct has been serving a progressively wide range of applications such as device-to-device file sharing, face-to-face interactive gaming, and wireless projection. However, when TCP meets Wi-Fi Direct, we find that two independent control loops exist, i.e., the transport-layer control loop and the link-layer control loop. First, these functionally redundant loops result in spectrum inefficiency. Second, the lack of effective information interaction between layers results in local optimal. To tackle these issues, this paper proposes Wi-Fi Direct TCP (WDTCP), a performant TCP that provides a full protocol design of the acknowledgment de-redundancy and explicit-capacity-based congestion control. WDTCP tightly couples the two control loops by capturing the WiFi Direct’s key feature of one-hop communication. Evaluation results demonstrate that WDTCP can maximize bandwidth utilization while keeping low latency. For instance, compared to legacy TCP, WDTCP improves throughput by up to 49.2% and reduces average and 95th latency by up to 32.4% and 50.7%, respectively.

Converged Ethernet employs Priority-based Flow Control (PFC) to provide a lossless network. However, issues caused by PFC, including victim flow, congestion spreading, and deadlock, impede its large-scale deployment in production systems. The fine-grained experimental observations on switch buffer occupancy find that the root cause of these performance problems is a mismatch of sending rates between end-to-end congestion control and hop-by-hop flow control. Resolving this mismatch requires the switch to provide an additional buffer, which is not supported by the classic dynamic threshold (DT) policy in current shared-buffer commercial switches. In this paper, we propose Selective-PFC (SPFC), a practical buffer management scheme that handles such mismatch. Specifically, SPFC incrementally modifies DT by proactively detecting port traffic and adjusting buffer allocation accordingly to trigger PFC PAUSE frames selectively. Extensive case studies demonstrate that SPFC can reduce the number of PFC PAUSEs on non-bursty ports by up to 69.0%, and reduce the average flow completion time by up to 83.5% for large victim flows.

The emerging applications including AR/VR inter-active gaming, ultra-high-definition live streaming, 4K wireless projection, Metaverse, etc. imply the demand for ultra-low latency and ultra-high bandwidth wireless transmission. The legacy kernel TCP stack is not fully satisfactory because it induces the CPU bottleneck on hosts. In this paper, we propose Wireless-RDMA (W-RDMA) that enables RDMA in wireless networks to tackle the CPU bottleneck issue on wireless hosts. The feasibility of W-RDMA is demonstrated through testbed experiments. Technical challenges and future opportunities are further discussed. We believe it is a small but crucial step for enabling RDMA for wireless transmission.