The demand for Low-earth-orbit (LEO) satellite networking and routing is continuously growing. Container-based satellite network emulation becomes an important networking evaluation choice for LEO satellite network. While many container-based satellite network emulators optimize virtual network efficiency, they often overlook route installation latency—the duration of installing computed routes into kernel routing table. This latency represents a significant bottleneck for emulating large-scale LEO constellations, which are characterized by dynamic topologies and frequent, massive routing re-convergences. High routing installation latency not only reduces emulation efficiency but also compromises evaluation reliability and renders subsequent data transmission experiments infeasible. In this paper, we present a Lock-Free and Filtered Route Installation (LFRI) mechanism. LFRI filters out non-effective route installations, thereby shortening the installation procedure without changing the routing results. LFRI employs a lock-free installation mechanism that enables parallelized and efficient route installation. Experimental results demonstrate that, compared to the legacy Netlink installation, LFRI reduces the route installation latency by up to 99.9% in emulations of typical LEO satellite constellations, bringing it down to under ten milliseconds, within a performance level comparable to routing installation on physical machines. Meanwhile, in the case study evaluating path availability ratio and restoration time, LFRI yields more consistent and credible experimental data.
Wenhao Lu, Zhiyuan Wang, Shan Zhang et al.· Asia-Pacific Workshop on Net...· 0 citations
A heterogeneous resource allocation strategy for cross-high and low orbit mixed satellite networks is proposed, which takes dynamic communication service demands as input and collaboratively allocates beam bandwidth, frequency, time slot, power and inter-satellite links and other heterogeneous resources.
Zhi-Hao Wang, Hongbin Luo, Zhiyuan Wang et al.· Peer-to-Peer Networking and...· 0 citations
Deploying large language models (LLMs) on edge nodes enables low-latency and privacy-preserving inference, but faces severe resource constraints under high-concurrence workloads. While existing inference systems leverage intranode key–value (KV) caching to improve efficiency, they largely neglect the unique complexities of multinode edge environments. Specifically, reactive KV cache eviction policies suffer from temporal uncertainty, often discarding reusable KV caches prematurely, while the tight coupling between request scheduling and cache placement often leads to myopic decisions that exacerbate load imbalance and resource contention. To address these challenges, we propose a dynamic block-level paradigm that treats KV blocks as the fundamental units for caching and scheduling, enabling dynamic sharing, generation, and eviction of arbitrary-length prefixes. We present complete modeling of the spatiotemporal coupling between scheduling and caching under block-level granularity, capturing intricate interactions overlooked by prior work. Based on this model, we design an online joint optimization algorithm, which applies to general edge LLM serving scenarios. The algorithm decouples spatiotemporal dependencies via randomized rounding over per-slot subproblems, achieving a balance between real-time responsiveness and long-term system efficiency. Theoretical analysis establishes high-probability near-optimality guarantees, and extensive experiments show that our method reduces the average time to first token (TTFT) by up to 54.02% over existing baselines.
Xishuo Li, Wei Jiao, Jun He et al.· IEEE Internet of Things Jour...· 0 citations
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