惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Martin Fowler
Martin Fowler
T
The Blog of Author Tim Ferriss
J
Java Code Geeks
M
MIT News - Artificial intelligence
F
Fortinet All Blogs
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
B
Blog
Microsoft Azure Blog
Microsoft Azure Blog
I
InfoQ
Microsoft Security Blog
Microsoft Security Blog
N
Netflix TechBlog - Medium
G
Google Developers Blog
L
LangChain Blog
腾讯CDC
大猫的无限游戏
大猫的无限游戏
U
Unit 42
Google DeepMind News
Google DeepMind News
人人都是产品经理
人人都是产品经理
罗磊的独立博客
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
小众软件
小众软件
The GitHub Blog
The GitHub Blog
博客园_首页
GbyAI
GbyAI

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
An Architectural Design for Brokered Collaborative Conten...
Mikael Fernandus Simalango, Sangyoon Oh · 2010-12-15 · via cs.DC updates on arXiv.org

Advances in web technologies have driven massive content uploads and requests that can be identified by the increased usage of multimedia web and social web services. This situation enforces the content providers to scale their infrastructure in order to cope with the extra provisioning of network traffic, storage and other resources. Since the complexity and cost factors in scaling the infrastructure exist, we propose a novel solution for providing and delivering contents to clients by introducing a brokered collaborative content delivery system. The architectural design of this system leverages content redundancy and content distribution mechanisms in other content providers to deliver contents to the clients. With the recent emergence of cloud computing, we show that this system can also be adopted to run on the cloud. In this paper, we focus on a brokering scheme to mediate user requests to the most appropriate content provider based on a ranking system. The architecture provides a novel Global Rank Value (GRV) concept in estimating content provider capability and transforming the QoS requirement of a content request. A fairness model that will bring this design to be attractive to the current content delivery regime is also introduced. Through simulation, we show that using fair provider selection, contents can be provisioned by a better pool of qualified providers thus leveraging the collaboration and preventing potential QoS violation that may occur when the size of pool is smaller.