NTTコミュニケーションズでは、Azure Stack Hub with GPUを先行で導入し検証を行っています。本資料では、実際に利用している立場からデモを交えつつAzure Stack Hub with GPUのユースケースをお話すると共に、GPUのベンチマークを含む他社クラウドとの性能比較結果について情報共有をいたします。
NTTコミュニケーションズでは、Azure Stack Hub with GPUを先行で導入し検証を行っています。本資料では、実際に利用している立場からデモを交えつつAzure Stack Hub with GPUのユースケースをお話すると共に、GPUのベンチマークを含む他社クラウドとの性能比較結果について情報共有をいたします。
The document discusses new features of OpenStack Swift object storage and OpenStack Storlets. It summarizes global erasure coding in OpenStack Swift, which improves storage efficiency and reliability. It also discusses Storlets, which allow running compute logic directly on Swift storage nodes to process objects. The presentation provides an overview of these features and recommends related documentation for further reference.
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
This document discusses 5G and multi-access edge computing (MEC). The key points are: 1) 5G can achieve latency of 100ms while 4G is 300ms, and 5G bandwidth is 20Gbps compared to 4G's 1.29Gbps; 2) MEC deployed close to users on 5G can achieve even lower latency of under 10ms; 3) MEC integrated with 5G can enable new applications for IoT, VR/AR with high speed and low latency.
NTT Docomo's Challenge looking ahead the world pf 5G × OpenStack - OpenStack最...VirtualTech Japan Inc.
タイトル:NTT Docomo's Challenge looking ahead the world pf 5G × OpenStack
アジェンダ:
- Current Challenge
-- DOCOMO Cloud Platform
-- BizDevOps
- Challenge for the future
-- DOCOMO 5G Open Cloud
-- Next Challenge
Here are the key points from the AT&T presentation on their "Network AI" framework:
- AT&T is developing an open source framework called "Network AI" to drive their software-defined network transformation.
- The goal is to apply AI/machine learning techniques to continuously optimize their network performance. This will be done by collecting massive amounts of network data and using it to train ML models.
- As part of this effort, AT&T is contributing several open source projects to the Linux Foundation like Airship, Akraino, and Acumos. Airship provides tools for deploying OpenStack and Kubernetes on the edge, while Akraino is an edge computing framework. Acumos allows for developing and
Juju is a tool for deploying applications on public clouds, private clouds, and bare metal servers. It uses models to deploy applications across machines, with each model representing a separate environment. Juju charms define how to deploy and configure applications, and bundles define full multi-machine application topologies to deploy with Juju.
This document discusses using Juju and Kubernetes to deploy containerized applications on GPU-enabled infrastructure. It provides YAML examples for creating Kubernetes pods that utilize NVIDIA GPU resources and deploying Chainer and TensorFlow containers with GPU support. Commands are given for interacting with the Kubernetes cluster through kubectl to view nodes, create and delete pods, and execute commands on pods.
論文紹介:「Amodal Completion via Progressive Mixed Context Diffusion」「Amodal Insta...Toru Tamaki
Katherine Xu, Lingzhi Zhang, Jianbo Shi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, "Amodal Completion via Progressive Mixed Context Diffusion"CVPR2024
https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Amodal_Completion_via_Progressive_Mixed_Context_Diffusion_CVPR_2024_paper.html
Minh Tran, Khoa Vo, Tri Nguyen, and Ngan Le,"Amodal Instance Segmentation with Diffusion Shape Prior Estimation"ACCV 2024
https://uark-aicv.github.io/AISDiff/
This study aims to develop an interactive idea-generation support system that enables users to consider the potential side effects of realizing new ideas.
In idea generation, confirmation bias often leads to an excessive focus on ``convenience,'' which can result in the oversight of unintended consequences, referred to as the ``side effects of convenience.''
To address this, we explored methods to alleviate user biases and expand perspectives through system-supported dialogue, facilitating a broader consideration of potential side effects.
The proposed system employs a stepwise idea-generation process supported by large language models (LLMs), enabling users to refine their ideas interactively.
By dividing the ideation process into distinct stages, the system mitigates biases at each stage while promoting ideas' concretization and identifying side effects through visually supported dialogues.
Preliminary evaluation suggests that engaging with the proposed system fosters awareness of diverse perspectives on potential side effects and facilitates the generation of ideas that proactively address these issues.
3. たまおきが視聴したセッション①
1. An overview of container related projects in OpenStack as a
telco guy sees them
• OpenStack の Docker / k8s 関連プロジェクトをうまく整理
• OpenStack Fuxi プロジェクトを知る
• Cinder storage and Manila shares for Docker containers and containers in k8s
2. Implications of 5G RAN and IoT on OpenStack based edge
computing.
• AT&T の Edge Computing の取り組みを紹介
• Edge Computing とその Architecture の理解には役に立つ
3
4. Edge Computing(Summit 前の私の理解)
4
Edge Cloud
Centralized
Cloud
Connected Car
IoT
Connected Car
Services
Server-Side IoT
Client-Side Server-Side(Cloud-Side)
Connected Car
Services
Cloud
Application
Mobile, IoT, Anywhere Edge Cloud
5. MECを理解する①
MEC(Mobile Edge Computing)
5
MEC のユースケースによって
必要な要素(テクノロジー、品質、
ビジネス要件)が異なる
MBB: Mobile Broadband
mMTC: massive Machine
Type Communications
Dense Inf Society
Connected vehicles
VR office/factory/tactile
Throughput
Latency
Reliability
Availability
Energy
Efficiency
User/Device
density
Implications of 5G RAN and IoT on OpenStack based edge computing. より引用 [ OpenStack Summit にて AT&T, Ericsson 発表 ]
https://www.openstack.org/videos/sydney-2017/implications-of-5g-ran-and-iot-on-openstack-based-edge-computing
10 Mbps → 100 Mbps
10 ms → 1 ms
6. MECを理解する②
AT&T の MEC Architecture
6
Disaggregated CoreDisaggregated RAN
5G Application
Ecosystem
IoT
Connected
Car
MBB
RU DU UPF UPF
Macro Radio
& Small cell
Antennas
5G
Base
Stations
Edge
Cloud
Centralized
Cloud
CCF
Internet
CU-CP
CU-UP
NFV MANO (Management & Orchestration)
CU: Centralized Unit
CP: Control Plane
UP: User Plane
UPF: User Plane Function
CCF: Core Control Function
RU: Radio Unit
DU: Digital Unit
Implications of 5G RAN and IoT on OpenStack based edge computing. より引
用
8. たまおきが視聴したセッション②
3. Secrets for approaching bare-metal performance with Real-
Time Virtual Network Functions in OpenStack
• OVS+DPDK, SR-IOV, Smart NIC の特徴とボトルネックを説明
4. Self-healing and optimization SIG
• OpenStack のセルフヒーリングについて各プロジェクトがコミュニ
ケーションを開始した
8