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

Y
Y Combinator Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园_首页
量子位
V
Visual Studio Blog
博客园 - Franky
宝玉的分享
宝玉的分享
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 【当耐特】
罗磊的独立博客
小众软件
小众软件
V
V2EX
GbyAI
GbyAI
B
Blog RSS Feed
博客园 - 三生石上(FineUI控件)
大猫的无限游戏
大猫的无限游戏
有赞技术团队
有赞技术团队
月光博客
月光博客
Recent Announcements
Recent Announcements
雷峰网
雷峰网
F
Fortinet All Blogs
M
MIT News - Artificial intelligence

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
Design and Implementation of an IoT Cluster with Raspberr...
Noel Portillo · 2025-03-05 · via cs.DC updates on arXiv.org

This document presents the design and implementation of a low-power IoT server cluster, based on Raspberry Pi 3 Model B and powered by solar energy. The proposed architecture integrates Kubernetes (K3s) and Docker, providing an efficient, scalable, and high-performance computing environment. The cluster is designed to optimize energy consumption, leveraging a 200W solar panel system and a 100Ah lithium-ion battery to support continuous operation under favorable environmental conditions. Performance analysis was conducted based on theoretical inferences and data obtained from external sources, evaluating resource allocation, power consumption, and service availability. These analyses provide theoretical estimates of the system's operational feasibility under different scenarios. The results suggest that this system can serve as a viable and sustainable alternative for edge computing applications and cloud services, reducing dependence on traditional data centers. In addition to its positive impact on environmental sustainability by significantly reducing the carbon footprint, this solution also addresses economic concerns, as conventional data centers consume enormous amounts of energy, leading to increased demand on the power grid and higher operational costs.