Synchronization-induced congestion is a critical performance bottleneck in modern distributed machine learning (ML) training, where simultaneous gradient exchanges create bursty traffic patterns. Existing solutions, both reactive and proactive, struggle to balance throughput and latency in the presence of synchronized flows. We propose DeSync, a proactive traffic shaping scheme that introduces structured random delay to de-synchronize communication rounds. Evaluations with DCQCN, HPCC, DCTCP, and TIMELY demonstrate that DeSync significantly improves FCT, job completion times, and congestion metrics, enhancing existing CC mechanisms without specialized hardware.

Communication overhead is a major bottleneck in distributed AI training, particularly in multi-tenant environments, limiting GPU utilization. Existing job-level scheduling methods fail to address the varying urgency of individual communication operations. We propose Flux, a novel fine-grained scheduler that prioritizes communication operations based on their Urgency Score and job intensity. Our evaluation shows Flux improves GPU utilization by up to 10 % compared to state-of-the-art job-level algorithms. This demonstrates the significant advantage of fine-grained communication scheduling in multitenant AI clusters.

While Multi-CQF enables deterministic networking over wide-area networks(WANs) by decoupling transmission and reception, it introduces significant queuing delays. We propose Effective Phase Alignment (EPA), which mitigates queuing delay by adjusting transmission offsets to align the effective phase, defined as the phase difference between the sending and receiving windows. EPA lowers the upper bound of average queuing delay from 2T to 1.5T, and achieves T under perfect alignment.

Integrating Frame Replication and Elimination for Reliability (FRER) with Cyclic Queuing and Forwarding (CQF) in Time-Sensitive Networks (TSN) encounters redundancy failures and resource reservation inefficiencies due to length disparities across redundant paths. To address these challenges, we propose ReCQF, a Reliability-Enhanced CQF scheduling framework built on Multi-Instance CQF. ReCQF adaptively assigns redundant flows to multiple CQF queue pairs with specific cycles, effectively aligning transmission delays across redundant paths to ensure low delay and inter-path delay differences while significantly reducing resource reservations.

Benefiting from high flexibility and probability of line-of-sight, deploying unmanned aerial vehicles (UAV s) as aerial access points has emerged as a promising solution for ensuring reliable wireless connectivity in crowded events. This paper introduces a UAV scheduling process adaptive to dynamic mobile network demand, including three phases. In the sensing phase, the user distribution is sensed, and user number thresholds are set to determine whether UAV assistance is needed. The planning phase presents an enhanced mean shift algorithm to find suitable locations to deploy UAVs with a dynamic bandwidth derived from the user distribution, the UAV's maximum capacity, and the UAV's maximum throughput. The deploying phase dispatches and recalls UAV s based on planning results. Comprehensive simulation experiments are conducted on OMNeT ++ using real-world data. Results show that the proposed process shows great adaptivity, with an efficiency increase of 18.7% and a fairness increase of 28.9 % compared to the existing related works on average.

Industrial networks require deterministic and reliable communication, which can be achieved by Time-Sensitive Networking (TSN), a set of standards that enable precise timing and synchronization of data transmission. However, TSN is susceptible to unexpected delays caused by device malfunction, interference or cyber attacks, which can have a domino effect and disrupt multiple data flows. To address this challenge, we propose RobustTSN, a framework that protects TSN against the domino effect of delayed frames and tolerates harmless accident frames using Per-Stream Filtering and Policing (PSFP) mechanism. We develop algorithms to calculate ingress filtering schedules based on local-safe delay and global-safe interval concepts, which decide whether to accept or discard out-of-schedule frames. We use a finite state machine to model the interaction between frames and evaluate frame safety. We build a software-defined networking based system to dynamically monitor network states and reconfigure device filtering after out-of-schedule transmission occurs. We conduct experiments on practical scenario topologies and large groups of random flows to demonstrate the effectiveness and efficiency of our framework.

In the domain of Time-Sensitive Networking (TSN), the quest for ultra-reliable low-latency communication is paramount. Current scheduling strategies, which hinge on strict isolation to ensure low latency and jitter, confront the challenges of high overhead in worst-case latency evaluation and consequent limitations in network flow capacity. This paper introduces an innovative framework that transcends traditional isolation constraints, thereby expanding the solution space and augmenting network schedulability. At the heart of this framework lies a novel latency jitter analysis method that assesses the viability of non-isolation scenarios with constant time complexity. This method underpins a heuristic scheduling algorithm that not only boasts the smallest time complexity among existing heuristics but also significantly increases the number of scheduled flows. Complementing this, we integrate a discrete time reference approach to hasten time-intensive scheduling operations, achieving an optimal balance between schedulability and runtime efficiency. The framework further incorporates a workload-shifting technique to enhance online scheduling responsiveness. It adeptly manages the variability in scheduling times caused by disharmonious flow periods, further bolstering the framework’s robustness. Experimental validations demonstrate that our framework can increase the scheduled flows up to 269%. It reduces scheduling runtime by up to 98.44% for medium-scale networks while maintaining a flat runtime growth curve, ensuring predictable performance in online scheduling scenarios.

Time Sensitive Networks (TSN), as an important representative of deterministic networks, provide low-latency and highly reliable communication services for the growing network applications that have strict requirements. Cyclic Queuing and Forwarding (CQF) is a well-known mechanism proposed by IEEE 802.1Qch for low-latency flow control of time-sensitive networks. It achieves bounded end-to-end delay and jitter transmission through a set of queues without complicated queue gating. However, most of the current work overlooks the widespread existence of multi-link rate networks in LANs and WANs, and the single-cycle CQF is unable to adjust different link rates, resulting in low bandwidth utilization and high latency. In this paper, we propose a novel scheduling approach named Multi-Cycle CQF (MCCQF) to solve the transmission problem in multi-link rate networks, aiming to reduce deterministic end-to-end delay and improve link bandwidth utilization. In addition, we formulate the scheduling constraints, being of guiding significance for designing the transmission of multi-link-rate networks, and we design an online scheduling algorithm based on it. We compare the proposed scheme with the single-cycle CQF online scheduling algorithm in hierarchical multi-link-rate networking scenarios, and the evaluation shows that our algorithm achieves better end-to-end ultra-low latency (38.9% reduction) with a smaller schedulability gap compared with single-cycle CQF. And we also improved the scheduleability based on MCCQF by utilizing internal offset.

5G and Industrial Internet are bringing a variety of applications with on-time and reliable demands. Cyclic queuing and forwarding (CQF), a well-known mechanism defined by IEEE 802.1 Qch in Time Sensitive Network (TSN), achieves deterministic end-to-end latency and jitter without complex gating calculations. However, most of the current work ignores the prevalence of hybrid networks with different link rates, resulting in low bandwidth utilization and high latency for single-cycle CQF. In this paper, we propose a multi-cycle CQF to address the transmission in multi-link-rate networking, reducing deterministic end-to-end latency and improving link bandwidth utilization. In addition, we formulate the scheduling constraints, being of guiding significance for designing the transmission of multi-link-rate networks, and we design an online scheduling algorithm based on it. We compare the proposed scheme with the single-cycle CQF online scheduling algorithm in hierarchical multi-link-rate networking scenarios, and the evaluation shows that our algorithm achieves better end-to-end ultra-low latency (38.9% reduction) with a smaller schedulability gap compared with single-cycle CQF.