Beichuan Zhang

Muaz Ali, Utkarsh Upadhyay, Sean McCormick et al.

Starlink has rapidly emerged as the world's largest satellite constellation and the de facto reference system for low Earth orbit (LEO) networking research. Existing literature predominantly models Starlink as a static, symmetric, and fully deployed structure with uniformly distributed satellites. However, we reveal that Starlink's actual deployment, orbital configurations, and operational dynamics fundamentally deviate from these idealized assumptions. Leveraging satellite observation data spanning 2019 to 2025, we demonstrate that the constellation is highly dynamic across multiple temporal and spatial scales. Macroscopically, Starlink comprises multiple orbital shells undergoing continuous active deployment and reconfiguration. Microscopically, individual satellites exhibit high mobility, frequently executing collision-avoidance maneuvers, altitude adjustments, and intra-orbital relocations. We discover that while the majority of satellites form a relatively stable structure with near-uniform spacing, other satellites tend to cluster as twins or triads as in-orbit backups. Furthermore, empirical survival analysis indicates an operational lifespan of 4-6 years and an average daily failure probability of 0.0128%. Ultimately, our data-driven characterization exposes Starlink as a highly heterogeneous and continuously evolving network. We provide critical empirical insights that challenge prevailing simulation models, offering a more accurate foundation for future LEO topology design, routing protocols, and performance evaluations.

Tianyuan Yu, Lan Wang, Beichuan Zhang et al.

The TCP/IP protocol stack uses IP addresses for two distinct roles: identifying hosts and locating their attachment points in the network topology. This dual purpose creates a fundamental tension that has led to routing and forwarding scalability challenges throughout the history of the Internet in unicast packet delivery and, more notably, in multicast delivery. This paper reviews the evolution of routing scalability solutions over the years and makes four observations. First, map-and-encap is a recurring architectural solution shared by all scalable unicast and multicast delivery methods, developed independently across different problem contexts. Second, a new solution tends to succeed when it can bring immediate local gains to early adopters without requiring coordination across administrative domains. Third, network routing and forwarding designs that depend on external factors, such as the number of distinct end sites or even application-specific deliveries, inherently preclude an upper bound on their scalability. Fourth, today's inter-domain routing protocol, BGP, lacks a topological abstraction equivalent to an egress router within a routing domain, thereby inherently preventing a map-and-encap solution for scalability. These observations offer insights into the design of future scalable routing system architectures.

Lin Guan, Jia-Qi Yang, Zhishan Zhao et al.

Short-video recommenders such as Douyin must exploit extremely long user histories without breaking latency or cost budgets. We present an end-to-end system that scales long-sequence modeling to 10k-length histories in production. First, we introduce Stacked Target-to-History Cross Attention (STCA), which replaces history self-attention with stacked cross-attention from the target to the history, reducing complexity from quadratic to linear in sequence length and enabling efficient end-to-end training. Second, we propose Request Level Batching (RLB), a user-centric batching scheme that aggregates multiple targets for the same user/request to share the user-side encoding, substantially lowering sequence-related storage, communication, and compute without changing the learning objective. Third, we design a length-extrapolative training strategy -- train on shorter windows, infer on much longer ones -- so the model generalizes to 10k histories without additional training cost. Across offline and online experiments, we observe predictable, monotonic gains as we scale history length and model capacity, mirroring the scaling law behavior observed in large language models. Deployed at full traffic on Douyin, our system delivers significant improvements on key engagement metrics while meeting production latency, demonstrating a practical path to scaling end-to-end long-sequence recommendation to the 10k regime.