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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
IsoRAN: Isolation and Scaling for 5G RANvia User-Level Da...
Nishant Budhdev, Mun Choon Chan, Tulika Mitra · 2020-03-04 · via cs.DC updates on arXiv.org

5G presents a unique set of challenges for cellular network architecture. The architecture needs to be versatile in order to handle a variety of use cases. While network slicing has been proposed as a way to provide such versatility, it is also important to ensure that slices do not adversely interfere with each other. In other words, isolation among network slices is needed. Additionally, the large number of use cases also implies a large number of users, making it imperative that 5G architectures scale efficiently. In this paper we propose IsoRAN, which provides isolation and scaling along with the flexibility needed for 5G architecture. In IsoRAN, users are processed by daemon threads in the Cloud Radio Access Network (CRAN) architecture. Our design allows users from different use cases to be executed, in a distributed manner, on the most efficient hardware to ensure that the Service Level Agreements (SLAs) are met while minimising power consumption. Our experiments show that IsoRAN handles users with different SLA while providing isolation to reduce interference. This increased isolation reduces the drop rate for different users from 42% to nearly 0% in some cases. Finally, we run large scale simulations on real traces to show the benefits for power consumption and cost reduction scale while increasing the number of base stations.