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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
Active Admission Control in a P2P Distributed Environment...
Andrei Negulescu, Weijia Shang · 2023-11-10 · via cs.DC updates on arXiv.org

In this study, the Active Control in an Intelligent and Distributed Environment (ACIDE) media distribution model solution and algorithms are proposed for livestreaming in capacity efficient mobile wireless networks. The elements of the ACIDE model are a base station and a cluster formed by a number of peers able to establish peer to peer communications. The cluster peers are selected from a group of users interested in livestreaming the same media. The ACIDE model solution minimizes the bandwidth allocated to a cluster of n peers such that an uninterrupted media play for all peers is guaranteed. The livestream media is sent to the peers in packages and every media package is divided into n blocks. The blocks are distributed to the n peers of a cluster in two phases, such that the base station bandwidth is utilized during first phase only. The allocated bandwidth, the amount of bandwidth the base station has to allocate to a cluster, is minimized and its lower bound is equal to the bandwidth required for multicasting. In this study, the ACIDE model is used to address the problem of how to find the maximum number of peers n, chosen from a group of N users, that can be admitted to a cluster knowing the given allocated bandwidth, the amount of bandwidth that a base station allocates to a cluster in advance, prior to admitting users. When users become peers of an ACIDE cluster, the network capacity, the total number of users who are able to access live media, increases meaning that network resources are used more efficiently. The problem of finding the maximum number of peers n is addressed as an optimization problem, with the objective of having the entire given allocated bandwidth used by the peers admitted to the cluster. This problem is NP-complete and a non-optimal solution is proposed for peers selection such that all admitted peers play media continuously.