With the rapid growth of large language model training and generative artificial intelligence services, data center networks face severe micro-burst traffic and high concurrency. Traditional hash-based flow-level load balancing cannot sense link states, leading to hash collisions, hotspot congestion, and tail latency in multipath Clos networks. Existing packet-level schemes are constrained by stale state information, high hardware complexity, and poor adaptation to heterogeneous links. To address these issues, this paper proposes probabilistic state-proportional (PSP) dispatching, a packet-level load balancing algorithm. Using a Band-based discrete state representation, PSP replaces global sorting with local probability mapping, reducing hardware complexity while suppressing herding and oscillations caused by stale states. Experiments on a cycle-accurate simulator show that PSP is robust across port scales, bandwidth-limited paths, and fixed-flow interference. It outperforms join-the-shortest-queue (JSQ) scheduling and Random in loss rate, 99th-percentile buffer occupancy, and scalability, while remaining competitive with Top-k at lower hardware cost. PSP provides an effective balance among performance, stability, and overhead for artificial intelligence data centers.
Jiaqi Liu, Chunyang Zhang, Heng Pan et al.· 0 citations
Scaling Large Language Models (LLMs) increasingly mandates the aggregation of geographically dispersed resources. However, cross-datacenter (Cross-DC) training faces significant hurdles due to extreme hardware heterogeneity and limited Wide-Area Network (WAN) bandwidth. Empirical measurements on a 2,000 km Cross-DC testbed reveal that the interplay of hardware mismatches, combinatorial search complexity, and shared network contention creates severe performance bottlenecks for heterogeneous training. To address these complexities, we propose GeoOrchestra, a system that decouples resource filtering from fine-grained strategy search. By abstracting compute nodes via computation and memory profiles while modeling WAN links as a virtual hard pipe, GeoOrchestra deconstructs the parallel strategy search problem into three cascading stages. Specifically, it employs hetero-aware pruning to filter invalid resource sets, followed by a resource-driven search that exploits resource disparities to maximize efficiency. Finally, the system orchestrates training traffic via dynamic time-slot slicing to enforce lower-bound bandwidth guarantees for individual jobs. Evaluations on our testbed demonstrate that GeoOrchestra achieves up to 1.6× to 1.8× throughput improvement over state-of-the-art baselines, while maintaining stable execution under real-world shared WAN contention.
Ting Liu, Qinghua Wu, Jun Zhou et al.· Proceedings of the ACM SIGCO...· 0 citations
An architecture that decouples complex policy enforcement from high-speed packet forwarding to support VPC semantics on back-end NICs and enable front-end/back-end integration is proposed, suggesting that commodity hardware can support both high-throughput AI training and flexible VPC features.
Yinhe Wang, Xing Li, Enge Song et al.· Asia-Pacific Workshop on Net...· 0 citations
