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

量子位
I
InfoQ
人人都是产品经理
人人都是产品经理
博客园 - 三生石上(FineUI控件)
爱范儿
爱范儿
Hugging Face - Blog
Hugging Face - Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
S
SegmentFault 最新的问题
美团技术团队
小众软件
小众软件
Blog — PlanetScale
Blog — PlanetScale
Jina AI
Jina AI
aimingoo的专栏
aimingoo的专栏
H
Help Net Security
Last Week in AI
Last Week in AI
博客园_首页
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
L
LangChain Blog
云风的 BLOG
云风的 BLOG
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
G
Google Developers Blog
博客园 - 叶小钗
博客园 - Franky

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
Efficient Complex Event Processing in Information-centric...
Manisha Luthra, Johannes Pfannmüller, Boris Koldehofe, Jonas Höc · 2020-12-09 · via cs.DC updates on arXiv.org

Information-centric Networking (ICN) is an emerging Internet architecture that offers promising features, such as in-network caching and named data addressing, to support the edge computing paradigm, in particular Internet-of-Things (IoT) applications. ICN can benefit from Complex Event Processing (CEP), which is an in-network processing paradigm to specify and perform efficient query operations on data streams. However, integrating CEP into ICN is a challenging task due to the following reasons: (1) typical ICN architectures do not provide support for forwarding and processing continuous data streams; (2) IoT applications often need short response times and require robust event detection, which both are hard to accomplish using existing CEP systems. In this article, we present a novel network architecture, called INetCEP, for efficient CEP-based in-network processing as part of ICN. INetCEP enables efficient data processing in ICN by means of (1) a unified communication model that supports continuous data streams, (2) a meta query language for CEP to specify data processing operations in the data plane, and (3) query processing algorithms to resolve the specified operations. Our experimental results for two IoT use cases and datasets show that INetCEP offers very short response times of up to 73 μs under high workload and is more than 15X faster in terms of forwarding events than the state-of-the-art CEP system Flink. Furthermore, the delivery and processing of complex queries is around 32X faster than Flink and more than 100X faster than a naive pull-based reference approach, while maintaining 100% accuracy.