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

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
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园_首页
Engineering at Meta
Engineering at Meta
量子位
A
About on SuperTechFans
阮一峰的网络日志
阮一峰的网络日志
Recent Announcements
Recent Announcements
博客园 - 司徒正美
V
Visual Studio Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
The GitHub Blog
The GitHub Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
F
Fortinet All Blogs
Martin Fowler
Martin Fowler
腾讯CDC
Jina AI
Jina AI
C
Check Point Blog
H
Help Net Security
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
V2EX
爱范儿
爱范儿
I
InfoQ

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
Commercial Cloud Computing for Connected Vehicle Applicat...
Hsien-Wen Deng, Mizanur Rahman, Mashrur Chowdhury, M Sabbir Sale · 2020-08-17 · via cs.DC updates on arXiv.org

This study focuses on the feasibility of commercial cloud services for connected vehicle (CV) applications in a Transportation Cyber-Physical Systems (TCPS) environment. TCPS implies that CVs, in addition to being connected with each other, communicates with the transportation and computing infrastructure to fulfill application requirements. The motivation of this study is to accelerate commercial cloud-based CV application development by presenting the lessons learned by implementing a CV mobility application using Amazon Web Services (AWS). The feasibility of the cloud-based CV application is assessed at three levels: (i) the development of a cloud-based TCPS architecture, (ii) the deployment of a cloud-based CV application using AWS, and (iii) the evaluation of the cloud-based CV application. We implemented this CV mobility application using a serverless cloud architecture and found that such a cloud-based TCPS environment could meet the permissible delay limits of CV mobility applications. Commercial cloud services, as an integral part of TCPS, could reduce costs associated with establishing and maintaining vast computing infrastructure for supporting CV applications. As the CV penetration levels on the surface transportation systems increase significantly over the next several years, scaling the backend infrastructure to support such applications is a critical issue. This study shows how commercial cloud services could automatically scale the backend infrastructure to meet the rapidly changing demands of real-world CV applications. Through real-world experiments, we demonstrate how commercial cloud services along with serverless cloud architecture could advance the transportation digital infrastructure for supporting connected mobility applications in a TCPS environment.