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Yong-Qiang Xiong

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Open access Aug 2026

Rethinking Cache Eviction: Low-Overhead and Precise Learning-based Design

Caches reduce latency and network traffic, and cache performance largely depends on the eviction policy. Eviction effectiveness is typically measured by byte and object miss ratios. Although learning-based eviction policies can reduce misses, their high computational overhead limits practical deployment. This work pres...

Qian Wang, Chang Xu, Wenbin Zhou et al. · 0 citations
Preprint Sep 2026

GeoMesh: Workload-Balanced and Sign-Compressed Geo-Distributed LLM Training

GeoMesh is presented, a synchronous geo-distributed training framework for heterogeneous GPUs that balances per-worker workloads by assigning each GPU a suitable batch size and number of inner steps, so faster GPUs do more useful work instead of waiting.

Changyong Shin, Jaerim Park, Mi-Gyung Kang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics

The latency with respect to the resource dynamics of processing multiple requests and tasks concurrently is characterized and new optimization opportunities that exploit the resource dynamics of tasks are demonstrated: CPU-aware tool admission and task-aware CPU allocation.

Wonmi Choi, Minuk Park, Zhixiong Niu et al. · 0 citations
Book Open access Aug 2026

PIN: Less Is More for RDMA Load Balancing

PIN is presented, a lightweight wrapper that layers on top of any existing fine-grained RDMA load balancer, showing analytically that practical thresholds preserve overall load-balance fairness while reducing short-flow exposure to temporarily slower paths.

Jichun Wu, Ran Shu, A. Moore et al. · 0 citations
Book Open access Aug 2026

Nüwa: A Generative Control Plane for AI Network Simulation

Nüwa is presented, which views routing as a compilation problem, it leverages the hierarchical and symmetric structure common in AI fabrics and compiles a declarative topology description together with routing policies into compact forwarding artifacts that are fast to generate and efficient to look up.

Wenkai Li, Ran Shu, Peng Zhang et al. · 0 citations
Book Open access Aug 2026

OptiFlow: Towards LLM-Driven Optimization of Collective Communication Algorithms

OptiFlow is presented, among the first LLM-driven frameworks for automated design of high-performance collective communication algorithms, with key insight is a two-layer decomposition: the LLM generates compact data-movement intent expressed in a domain-specific language, while deterministic scheduling algorithms comp...

Fei Long, Ziyue Yang, Kaihui Gao et al. · 1 citation
Book Open access Aug 2026

STORM: Enabling Traffic Scheduling for RDMA

STORM is presented, a NIC-level scheduler for all types of RDMA workloads using NIC-only information: the known RDMA request size, and per-queue-pair backlog, and converts these signals into a small number of extra priority levels on the wire and prioritizes requests that are either near completion or blocking queued d...

Jichun Wu, Ran Shu, Gianni Antichi et al. · 0 citations

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