Low Earth Orbit (LEO) satellite constellations are becoming a viable means for Internet access. However, their topology changes as satellites move towards or away from orbital intersection points, leading to constant link down or up. This may cause routing table entry updates and thus path changes between satellites. A path change during transmission may lead to out-of-order packet delivery and invalidate the current TCP congestion window. While some path changes are inevitable, some are avoidable. Dijkstra's algorithm is a popular choice among the routing protocols proposed for LEO satellite networks. We observe that many next-hop route updates by Dijkstra's algorithm are avoidable. Motivated by this, we propose StableR-oute, which stabilizes routing paths from different perspectives. StableRoute Local (SR_L) leverages equal-cost shortest paths and stays with the current one if it is still valid. StableRoute K-Short (SR_K) allows a path longer than the shortest path. StableRoute Global (SR_G) leverages the predictable satellite trajectories and topology variations, and thus works out a next-hop route selection sequence that minimizes the number of route updates over a time period. The evaluation shows that SR_L, SR_K and SR_G outperform Dijkstra's algorithm, substantially reducing the number of route updates in changing topologies.

Vision-language models (VLMs) have achieved remarkable success in various vision-language tasks, such as image captioning and visual question answering. However, these models often lack physical common sense, frequently failing to identify visually evident violations of common physical principles. Therefore, evaluating the VLMs' understanding of physical common sense is essential, which has not yet been systematically explored in existing research. To fill this gap, we introduce PhyVIB (Physical Common Sense Violation Image Benchmark). This novel benchmark consists of 16,000 images across eight categories, aiming to systematically assess the VLMs' capability to detect violations of physical common sense in images. Our evaluations show that even the state-of-the-art VLMs perform poorly on PhyVIB, highlighting a significant area for improvement. In response, we propose PhyDetector, a two-stage fine-tuning framework to enhance the VLMs' capability to detect violations of physical common sense. The first stage involves supervised fine-tuning, which equips the VLM with essential concepts related to visual physical anomalies. The second stage utilizes group relative policy optimization to enhance the VLM's multimodal reasoning capability on physical plausibility. Experimental results show that the model fine-tuned with PhyDetector can significantly outperform the state-of-the-art VLMs in physical common sense understanding. Our artifacts are available at https://github.com/ZitongWang018/PhyVIB.

With the low latency and high coverage, the low earth orbit (LEO) satellite systems are attracting more and more venture capitals as well as research attentions. Due to their highly dynamic constellation topologies, routing protocols on the ground have to be tailored to efficiently adapt to the regular topology changes. However, for irregular topology changes caused by exceptional link failure/recovery, network-wide route flooding is still necessary for route convergence. But, this will cause significant traffic flooding redundancy due to the high density of constellation topologies. For larger-scale constellations, the redundancy issue will be exacerbated. To lessen the redundancy, this work proposes a lightweight route flooding mechanism by generating a sparse flooding topology that prunes the original full-mesh topology, and only flooding the route information on the sparse topology. By considering the maximum flooding hop as well as the robustness of the flooding topology, we design an algorithm to calculate the optimal topology instead of just applying the minimum spanning tree. The evaluation shows that, for the LEO-96 constellation, the new flooding topology has a 28.1% reduction in the inter-satellite links (ISLs) compared with the original topology, and the new flooding mechanism has a 37.52% reduction in the traffic flooded and a 10.03% reduction in the route convergence time compared with OSPF. Such improvements will be amplified on larger-scale constellations.

In multi-tenant clouds, off-the-shelf x86 boxes are widely deployed as middleboxes. With the rapid growth of cloud traffic and the migration to NFV deployment in recent years, CPU overloading at middleboxes becomes more of an issue. From our data centers, we observed that the CPU overloading was caused by heavy hitters. To address this issue, we propose MIMIC, a cloud-scale flow backpressure system, implemented onto our existing SmartNIC with FPGA acceleration. MIMIC rate-limits the selected heavy hitters through a new per-flow backpressure protocol and a new heavy-hitter detection system, to protect the other tenants. The detection system is based on hierarchical memory design, leveraging on-chip SRAM and off-chip DRAM, which can handle highly concurrent cloud traffic without the losses of flow information. We extend the design by adding a pre-filtering procedure for rapid detection. To avoid CPU being flooded by FPGA through frequent heavy-hitter reporting, due to their performance disparity, the CPU queries the FPGA on demand. The backpressure protocol is non-invasive to protect tenant privacy and allows controllable rate-limiting through the novel use of ECN and meter tables. The SmartNIC acts as a man in the middle to facilitate heavy-hitter detection and per-flow backpressuring. In a production setting, we observe that MIMIC can react quickly and bring down CPU load to the normal level within 10ms without packet losses.

SpaceX plans ambitiously to launch approximately 12,000 satellites from 2019 to 2024, expected to be a complement or even competitor to ground networks. However, the mega-scale satellite network is topology-varying and the frequency of inter-satellite link (ISL) handovers increases rapidly as the topology expands, which will further arouse a massive number of route updates with considerable packet travel delay or even packet loss during the route convergence. The classic Dijkstra's algorithm is adopted for space route calculation, however, it always selects the default shortest path from multiple equal-cost shortest paths between two satellite nodes. To reduce the route change as much as possible during the periodical topology change, in this work, we refined the original Dijkstra and propose StableRoute to select the most appropriate route from the equal-cost candidates with the least route updates compared with the routing table last round. In this way, the end-to-end paths can be maintained as far as possible without time-to-time oscillation. Evaluation shows that it reduces 41% of the route updates in a 36 × 36 topology compared with Dijkstra, and the reduction rate will rise persistently with the growth of the satellite constellation.