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

A
About on SuperTechFans
GbyAI
GbyAI
I
InfoQ
C
Cisco Blogs
T
Tor Project blog
NISL@THU
NISL@THU
N
Netflix TechBlog - Medium
C
Cyber Attacks, Cyber Crime and Cyber Security
The Hacker News
The Hacker News
T
The Exploit Database - CXSecurity.com
H
Help Net Security
Cisco Talos Blog
Cisco Talos Blog
The Register - Security
The Register - Security
Microsoft Azure Blog
Microsoft Azure Blog
G
Google Developers Blog
A
Arctic Wolf
Stack Overflow Blog
Stack Overflow Blog
C
CXSECURITY Database RSS Feed - CXSecurity.com
P
Palo Alto Networks Blog
aimingoo的专栏
aimingoo的专栏
Microsoft Security Blog
Microsoft Security Blog
P
Privacy International News Feed
L
LINUX DO - 热门话题
Know Your Adversary
Know Your Adversary
L
LangChain Blog
AWS News Blog
AWS News Blog
Security Latest
Security Latest
T
Threat Research - Cisco Blogs
U
Unit 42
P
Privacy & Cybersecurity Law Blog
Spread Privacy
Spread Privacy
WordPress大学
WordPress大学
Latest news
Latest news
L
Lohrmann on Cybersecurity
小众软件
小众软件
阮一峰的网络日志
阮一峰的网络日志
Simon Willison's Weblog
Simon Willison's Weblog
雷峰网
雷峰网
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Y
Y Combinator Blog
S
SegmentFault 最新的问题
Recent Announcements
Recent Announcements
C
Cybersecurity and Infrastructure Security Agency CISA
Martin Fowler
Martin Fowler
S
Schneier on Security
C
CERT Recently Published Vulnerability Notes
B
Blog
Project Zero
Project Zero
Recorded Future
Recorded Future

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 Preserving Clusters in Error-Bounded Lossy Compression of Particle Data Unlocking the Edge deployment and ondevice acceleration of multi-LoRA enabled one-for-all foundational LLM UCCL-Zip: Lossless Compression Supercharged GPU Communication Training Time Prediction for Mixed Precision-based Distributed Training Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling Optimizing Stochastic Gradient Push under Broadcast Communications Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving AgileLog: A Forkable Shared Log for Agents on Data Streams Secure and Privacy-Preserving Vertical Federated Learning Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel CUTEv2: Unified and Configurable Matrix Extension for Diverse CPU Architectures with Minimal Design Overhead Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search NimbusGuard: A Novel Framework for Proactive Kubernetes Autoscaling Using Deep Q-Networks Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge Rebooting Microreboot: Architectural Support for Safe, Parallel Recovery in Microservice Systems A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs SMART: When is it Actually Worth Expanding a Speculative Tree? ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving OpenCLAW-P2P v7.0-P2PCLAW: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review v7.0 -- Mathematical Corrections & Ecosystem Developments Edition DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection HearthNet: Edge Multi-Agent Orchestration for Smart Homes Token-Budget-Aware Pool Routing for Cost-Efficient LLM Inference Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios Duration-Informed Workload Scheduler Domain-Adaptive Model Merging Across Disconnected Modes Why Smaller Is Slower? Dimensional Misalignment in Compressed LLMs veScale-FSDP: Flexible and High-Performance FSDP at Scale AEG: A Baremetal Framework for AI Acceleration via Direct Hardware Access in Heterogeneous Accelerators ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness StreamServe: Adaptive Speculative Flows for Low-Latency Disaggregated LLM Serving Emergent Social Structures in Autonomous AI Agent Networks: A Metadata Analysis of 626 Agents on the Pilot Protocol SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding Para-B&B: Load-Balanced Deterministic Parallelization of Solving MIP Rashomon Sets and Model Multiplicity in Federated Learning Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems NPU Design for Diffusion Language Model Inference PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving SHARe-KAN: Post-Training Vector Quantization for Cache-Resident KAN Inference Spira: Exploiting Voxel Data Structural Properties for Efficient Sparse Convolution in Point Cloud Networks Power to the Clients: Federated Learning in a Dictatorship Setting From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill Speculative Actions: A Lossless Framework for Faster Agentic Systems InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
Big Data Computing and Clouds: Trends and Future Directions
Marcos D. Assuncao, Rodrigo N. Calheiros, Silvia Bianchi, Marco · 2013-12-17 · via cs.DC updates on arXiv.org

This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。