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

Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
小众软件
小众软件
爱范儿
爱范儿
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
量子位
博客园_首页
T
Tailwind CSS Blog
The Cloudflare Blog
J
Java Code Geeks
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
U
Unit 42
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
人人都是产品经理
人人都是产品经理
N
Netflix TechBlog - Medium
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
腾讯CDC
P
Proofpoint News Feed
aimingoo的专栏
aimingoo的专栏
Recent Announcements
Recent Announcements
T
The Blog of Author Tim Ferriss
D
Docker
Microsoft Azure Blog
Microsoft Azure Blog

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
Digital Twin of a Cloud Data Centre: OpenStack Cluster Vi...
Sheridan Gomes, Adel N. Toosi, Barrett Ens · 2021-08-13 · via cs.DC updates on arXiv.org

Data centres in contemporary times are essential as the supply of data increases. Data centres are areas where computing systems are concentrated for facilitating data processing, transfer and storage. At present traditional data centres have moved more towards the cloud model thereby making the processing, storage and harnessing of data more manageable and more accessible via the utility and subscription-based model of computing services. From the administrative point of view, cloud data centres are complex systems, hard to grasp and require large amounts of time to analyse different aspects of the cloud data centre such as maintenance and resource management. For a cloud data centre admin, this could be a challenging problem and a highly time-consuming task. Accordingly, there is a need to improve the useability of cloud data centre monitoring and management tools, and the digital twin could fulfil this need. This paper's primary objective is to construct a digital twin - a 3D visualisation and monitoring tool - of a cloud data centre managed by OpenStack, the well-known open-source cloud computing infrastructure software. To evaluate our proposed tool, we garner feedback on the digital twin's useability compared to the OpenStack dashboard. The input will be received from cloud data centres experts as they test the digital twin and answer various questions in an interview. The study results show that our proposed Digital Twin will help data centre admins better monitor and manage their data centres. It also will facilitate further research and implementation of the digital twin of data centres to improve usability.