1. The document introduces Cisco's Software Defined Data Center (SDDC) technology strategy. It discusses the trends of BiModal IT and the emergence of Mode 1 and Mode 2 IT.
2. It describes the SDDC architecture including Software Defined Computing (SDC) using Cisco UCS, Software Defined Storage (SDS) using Cisco HyperFlex, and Software Defined Networking (SDN) using Cisco ACI.
3. Case studies show that implementing SDDC with SDx technologies from Cisco can improve agility, reduce costs, and help organizations deploy both Mode 1 and Mode 2 applications.
[Container 기반의 DevOps] Cloud Native
열린기술공방에서 처음으로 런칭한 교육 프로그램의 트렌드 세션 자료입니다. 급변하는 환경에 맞춘 SW를 개발하고 배포하기 위해, 빠른 의사결정을 할 수 있는 환경과 프로세스가 더욱 중요해지고 있는데요. 기업들에게 왜 클라우드 네이티브 전략이 필수적인지에 대해 소개한 자료입니다.
열린기술공방의 교육 과정을 통해 Kubernetes위에서 동작하는 Application의 빌드부터 배포까지의 과정을 한 눈에 확인하실 수 있습니다.
While many organizations have started to automate their software development processes, many still engineer their infrastructure largely by hand. Treating your infrastructure just like any other piece of code creates a “programmable infrastructure” that allows you to take full advantage of the scalability and reliability of the AWS cloud. This session will walk through practical examples of how AWS customers have merged infrastructure configuration with application code to create application-specific infrastructure and a truly unified development lifecycle. You will learn how AWS customers have leveraged tools like CloudFormation, orchestration engines, and source control systems to enable their applications to take full advantage of the scalability and reliability of the AWS cloud, create self-reliant applications, and easily recover when things go seriously wrong with their infrastructure.
어떻게 하면 배포 프로세스를 빠르게 개선할 수 있을까요?
git branch를 푸시하고 개별 테스트 서버를 만드려면 어떻게 해야 할까요?
쿠버네티스와 GitOps, Argo CD를 이용한 배포 방법을 소개 합니다.
Open Infrastructure & Cloud Native Days Korea 2019 발표자료
원본 슬라이드 다운로드 - http://bit.ly/subicura-gitops
[OpenStack Day in Korea 2015] Keynote 2 - Leveraging OpenStack to Realize the...OpenStack Korea Community
OpenStack Day in Korea 2015 - Keynote 2
Leveraging OpenStack to Realize the SKT Software-Defined Data Center
Jinsung Choi, Ph.D - CTO, Corporate R&D Center, SK Telecom
[Container 기반의 DevOps] Cloud Native
열린기술공방에서 처음으로 런칭한 교육 프로그램의 트렌드 세션 자료입니다. 급변하는 환경에 맞춘 SW를 개발하고 배포하기 위해, 빠른 의사결정을 할 수 있는 환경과 프로세스가 더욱 중요해지고 있는데요. 기업들에게 왜 클라우드 네이티브 전략이 필수적인지에 대해 소개한 자료입니다.
열린기술공방의 교육 과정을 통해 Kubernetes위에서 동작하는 Application의 빌드부터 배포까지의 과정을 한 눈에 확인하실 수 있습니다.
While many organizations have started to automate their software development processes, many still engineer their infrastructure largely by hand. Treating your infrastructure just like any other piece of code creates a “programmable infrastructure” that allows you to take full advantage of the scalability and reliability of the AWS cloud. This session will walk through practical examples of how AWS customers have merged infrastructure configuration with application code to create application-specific infrastructure and a truly unified development lifecycle. You will learn how AWS customers have leveraged tools like CloudFormation, orchestration engines, and source control systems to enable their applications to take full advantage of the scalability and reliability of the AWS cloud, create self-reliant applications, and easily recover when things go seriously wrong with their infrastructure.
어떻게 하면 배포 프로세스를 빠르게 개선할 수 있을까요?
git branch를 푸시하고 개별 테스트 서버를 만드려면 어떻게 해야 할까요?
쿠버네티스와 GitOps, Argo CD를 이용한 배포 방법을 소개 합니다.
Open Infrastructure & Cloud Native Days Korea 2019 발표자료
원본 슬라이드 다운로드 - http://bit.ly/subicura-gitops
[OpenStack Day in Korea 2015] Keynote 2 - Leveraging OpenStack to Realize the...OpenStack Korea Community
OpenStack Day in Korea 2015 - Keynote 2
Leveraging OpenStack to Realize the SKT Software-Defined Data Center
Jinsung Choi, Ph.D - CTO, Corporate R&D Center, SK Telecom
The starting point for this project was a MapReduce application that processed log files produced by the support portal. This application was running on Hadoop with Ruby Wukong. At the time of the project start it was underperforming and did not show good scalability. This made the case for redesigning it using Spark with Scala and Java.
Initial review of the Ruby code revealed that it was using disk IO excessively, in order to communicate between MapReduce jobs. Each job was implemented as a separate script passing large data volumes through. Spark is more efficient in managing intermediate data passed between MapReduce jobs – not only it keeps it in memory whenever possible, it often eliminates the need for intermediate data at all. However, that alone not brought us much improvement since there were additional bottlenecks at data aggregation stages.
The application involved a global data ordering step, followed by several localized aggregation steps. This first global sort required significant data shuffle that was inefficient. Spark allowed us to partition the data and convert a single global sort into many local sorts, each running on a single node and not exchanging any data with other nodes. As a result, several data processing steps started to fit into node memory, which brought about a tenfold performance improvement.
How Cisco Migrated from MapReduce Jobs to Spark Jobs - StampedeCon 2015StampedeCon
At the StampedeCon 2015 Big Data Conference: The starting point for this project was a MapReduce application that processed log files produced by the support portal. This application was running on Hadoop with Ruby Wukong. At the time of the project start it was underperforming and did not show good scalability. This made the case for redesigning it using Spark with Scala and Java.
Initial review of the Ruby code revealed that it was using disk IO excessively, in order to communicate between MapReduce jobs. Each job was implemented as a separate script passing large data volumes through. Spark is more efficient in managing intermediate data passed between MapReduce jobs – not only it keeps it in memory whenever possible, it often eliminates the need for intermediate data at all. However, that alone not brought us much improvement since there were additional bottlenecks at data aggregation stages.
The application involved a global data ordering step, followed by several localized aggregation steps. This first global sort required significant data shuffle that was inefficient. Spark allowed us to partition the data and convert a single global sort into many local sorts, each running on a single node and not exchanging any data with other nodes. As a result, several data processing steps started to fit into node memory, which brought about a tenfold performance improvement.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/introducing-the-kria-robotics-starter-kit-robotics-and-machine-vision-for-smart-factories-a-presentation-from-amd/
Chetan Khona, Director of Industrial, Vision, Healthcare and Sciences Markets at AMD, presents the “Introducing the Kria Robotics Starter Kit: Robotics and Machine Vision for Smart Factories” tutorial at the May 2022 Embedded Vision Summit.
A robot is a system of systems with diverse sensors and embedded processing nodes focused on core capabilities such as motion, navigation, perception, machine vision, communication and control — alongside more unique and application-specific requirements. With the new Kria KR260 Robotics Starter Kit and the Kria Robotics Stack (KRS), users can easily build a complete robotics system using a ROS 2-based environment with low-latency, deterministic communications connecting production-ready Kria SOMs.
The resultant adaptive system can readily implement evolving and diverse algorithms as well as scale across multiple projects. This presentation highlights the capabilities and solutions possible with the Kria KR260 Robotics Starter Kit for roboticists, machine vision developers and industrial solution architects.
It’s surprisingly straightforward to migrate feature code from the CPU to the DSP – and determine the resulting benefits to the end application. In this session we’ll demonstrate Qualcomm® Hexagon™ SDK installation, code generation, profiling and execution of dynamic code modules on a Qualcomm® Snapdragon™ hardware target, and you’ll learn how to analyze the resulting performance benefits. Qualcomm Snapdragon and Qualcomm Hexagon are products of Qualcomm Technologies, Inc.
Learn more about Hexagon SDK: https://developer.qualcomm.com/hexagon
Watch this presentation on YouTube:
https://www.youtube.com/watch?v=x6mKEWLzJM0
Watch the replay: http://cs.co/9000DCie4
In today’s digital economy, getting ahead means crunching a lot of data. That’s why businesses of all sizes and industries are investing in high-performance computing. However, the last thing IT needs is another tech silo to manage.
Fortunately, the new Cisco UCS C4200 Series chassis and C125 M5 server node help you scale out compute-intensive workloads with ease—with the network fabric you already have. This TechWiseTV Workshop will get you up to speed fast.
Resources:
Watch the related TechWiseTV episode: http://cs.co/9006DAVPC
TechWiseTV: http://cs.co/9009DzrjN
Model Driven, Component Based Development for CBDDS and IDL to C++11Remedy IT
This presentation will show the advantages of a CBDDS solution compared to a plain DDS based architecture. It also highlights some of the concepts of the new IDL to C++11 Language Mappping
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalR - Slides Onl...Peter Gallagher
In this session delivered at Leeds IoT, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
Google Calendar is a versatile tool that allows users to manage their schedules and events effectively. With Google Calendar, you can create and organize calendars, set reminders for important events, and share your calendars with others. It also provides features like creating events, inviting attendees, and accessing your calendar from mobile devices. Additionally, Google Calendar allows you to embed calendars in websites or platforms like SlideShare, making it easier for others to view and interact with your schedules.
3. BiModal IT 등장과 시대변화
3
Mode 1 Mode 2
(SDLC, ITIL) Agile
, , ,
/ ( , IT)
IT 2/3 IT 1/3
( ) (Mobile,App)
IT ,
CIO CIO, CDO, , CMO, CEO
[Source : Gartner 2014 – BiModal IT]
1. Mode 1 Digital Transformation
/
Mode 1 Mode 2
ERP / MES / SCM
SDLC , ITIL, CMMi
2. Mode 2 Digital Transformation
•
• Mode 2 Mode 1
• SDDC , AutoScaling , Multi-Cloud
• Agile
3. Mode1+Mode2 Digital Transformation
• Mode1/Mode2
• /
•
U2L (Unix to Linux), Oracle DBMS PAS DBMS
2014 IT “BiModal IT” IT 2
. Mode 1,2 App , .
4. 비즈니스 민첩성 기반의 IT 적용 사례 – 산업별 범례 : 금융산업군의 변화들.
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6
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17
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.
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SDN
API
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[ SDDC IaaS (Infra as a Service) ]
NFV SDN
SDDC
PaaS
API API
API API API
CI/CD
Analytics
Security
18. 시스코 SDDC 아키텍쳐 솔루션 요약
18
SDDC SDx , HCI (SDS/SDC), SDN .
, .
SDDC
1. SDDC
SDS/ SDC
– x86 UCS
- HCI HyperFlex
- SDS/SDC : UCSM, HX Connect
SDN
- Nexus 9000
- SDN / : Cisco ACI
SDDC
– UCSD
SDDC
– SDDC/MultiCloud Cloud Center
SDDC
- Cisco Tetration
End to End , Cisco SDDC
19. SDDC
시스코 SDDC 아키텍쳐 기반 서비스 배치
19
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IT / , .
BiModal IT IT .
1. SDDC
– /
- CI/CD
- PaaS API/PlugIn Connector
-
- /
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– , Mode 1 Slow IT , Mode 2
,
-
“ SDDC ”
21. SDx 아키텍쳐 구축 사례 및 효과 – SDN 적용 사례 : 클라우드 서비스
21
A , SDN ACI
.
[ As-Is : ] [ To-Be : SDN ]
AS-IS
–
Mode 1( ) Mode 2(Cloud )
-
.
To-Be
– Cisco ACI
–
SDN
- TCO
CapEx ( 60% )
IT ( 97% )
22. SDx 아키텍쳐 구축 사례 및 효과 – SDDC 적용 사례 : 클라우드 서비스
22
B , HCI
. SDN .
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–
IT IT
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, IT
.
To-Be
– Cisco HyperFlex/UCS/ACI
–
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(2~3 )
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[ As-Is : ] [ To-Be : SDDC ]
23. SDx 아키텍쳐 구축 사례 및 효과 – SDDC 적용 사례 : R&D SDx 서비스
23
C . IT
, UCS SDN ACI .
[ As-Is : ] [ To-Be : SDC/SDN ]
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–
, IT
-
x86
To-Be
– Cisco UCS Blade ACI
–
SDN , x86
x86
-
UCS .
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24. SDx 아키텍쳐 구축 사례 및 효과 – SDDC 적용 사례 : 멀티 데이터센터 SDN
24
D , 2 , Cisco ACI
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AS-IS
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- :
.
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– :
,
- :
,
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25. SDx 아키텍쳐 구축 사례 및 효과 – SDDC 적용 사례 : SDDC 인프라 구축
25
E , Cisco ACI/ HCI Software Defined DataCenter .
TCO .
SDDC (Fast IT + Slow IT )
–
SDN
SDN ,
Fast IT , HCI
- TCO .
, SDN
SDDC CapEx OpEx .
-
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(vCenter & Cisco ACI )
26. SDx 아키텍쳐 구축 사례 및 효과 – SDDC 적용 사례 : SDN 인프라 구축
26
F SDDC , Cisco ACI Software Defined DataCenter .
, .
SDDC
– STP
STP, ARP Flooding
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-
IT
-
27. SDx 아키텍쳐 구축 사례 및 효과 – SDDC 적용 사례 : 멀티/하이브리드 클라우드
27
G Shadow IT (R&D) Hybrid & MultiCloud . HCI HX
AWS/Azure , Shadow IT .
Hybrid/MultiCloud
– Shadow IT
Shadow IT AWS
Cloud Center
-
Cloud AWS, Azure, Private Cloud
Cloud Management Platform
-
Private Cloud :
( )
Public Cloud : GPU, CPU intensive
- VPC Private Cloud VPN