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cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
1.5 Million Messages Per Second on 3 Machines: Benchmarki...
Muhamed Ramees Cheriya Mukkolakkal · 2026-03-31 · via cs.DC updates on arXiv.org

This paper presents two independent contributions for Apache Pulsar practitioners. First, we validate 1,499,947 msg/s at 3.88 ms median publish latency on just three bare-metal Kubernetes nodes running Pulsar 4.0.8 with Java 21 and ZGC Generational garbage collection, and project a hardware-driven path to 15 million msg/s on 15 machines using five independent clusters with key-based partition routing. Hardware selection -- specifically dedicated NVMe journals achieving 0.02 ms fdatasync and 25 Gbps network interfaces -- is the primary determinant of throughput ceiling, not compute or software tuning. Second, we trace the complete latency optimization journey from 213 ms GC spikes and 13-18 ms median publish latency in production to 3.88 ms through Java Flight Recorder guided root cause analysis. Three independent root causes are identified and resolved: G1GC pauses eliminated by switching to ZGC Generational; journal fdatasync latency reduced from 5.1 ms to 0.02 ms through NVMe journal dedication; and a previously undocumented Linux kernel page cache writeback interaction inside BookKeeper's ForceWriteThread that degrades fdatasync from under 1 ms to 15-22 ms even across physically separate NVMe drives sharing the kernel block layer. This finding is undocumented in official Apache Pulsar and BookKeeper documentation and is relevant to all Pulsar operators experiencing unexplained P99.9 latency spikes. The combined optimizations achieve a 4.7x latency improvement at 50x higher throughput.