The document discusses the evolution from monolithic architectures to microservices and functions. It covers the journey to microservices, including breaking up monolithic systems, data storage considerations, and challenges. It then discusses how functions can be used to further decompose microservices for improved scalability, flexibility and cost optimization. The presentation argues that adopting these approaches can help organizations gradually transform their systems and keep up with changing demands.

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주경호
LDCC/HIT
MSA를 넘어 Function 의 로의 진화

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Agenda
What we do
MSA.?
Journey to Microservices
MSA to Function
Next Generation
Why

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회사 소개

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사업 소개
• AI ( 인공지능)
• IOT Platform ( 사물 인터넷)
• BigData ( 세상을 예측 하는 기술 )
• Cloud ( 토탈 클라우드 서비스 )
Digital
Transformation
• SI/SOC/Solution ( 기업 가치 혁신 )
• Data Center ( 안정적인 비즈니스 환경 )
• Security ( 토탈 보안 서비스 )
• Retail/E-Biz ( On/Off 유통 서비스 )
Business Value
Innovation
• Omni Channel ( 위치 기반 마케팅 채널 )
• Finance ( 금융 서비스 )
• U-Healthcare (의료 및 헬스케어 서비스)
• IBS ( 건설,환경 에너지 서비스)
Better Experience

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Why
‘96~
2015~
2016~
2017~ Total Cloud 서비스 제공
통합 설계 마이그레이션
디지털 혁신 플랫폼 매니지드 서비스
고객 비즈니스별 맞춤 컨설팅,
전략 제시로 최적의 아키텍처
통합 설계
디지털 혁신 플랫폼으로
비즈니스의 새로운 가치 창출
고객 맞춤형 하이브리드
클라우드 매니지드 서비스
진단/분석 PoC
To-Be
모델
 글로벌 퍼블릭 클라우드 연계
 플랫폼 중심 서비스 기반 마련
 그룹 클라우드 전환 확산
멀티 클라우드 서비스
 퍼블릭 클라우드 서비스
출시(L.Cloud)
퍼블릭 기반 SaaS, PaaS런칭
 OpenStack 기반
그룹 클라우드 서비스 개시
커뮤니티 클라우드
 IDC 센터 ISO27001(보안)
ISO20000 인증 (IT서비스, 아시아 최초)
글로벌 수준 IDC 센터 운영
IoT
빅데이타 AI
수년간 구축 운영
노하우의 사례 및 방법론
기반
클라우드 소프트랜딩
클라우드
역량

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ME
유통
금융
통신
게임

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What we do

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Application
Architecture
Managed
Service
Security
Architecture
DataStore
Architecture
Cloud
Architecture
What we do

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What we do
Recommend
점진적인 변화
•문화(DevOPS, Agile)
•기술(MSA)
•개발 언어
•저장소
•환경
Not Recommended
BigBang

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여기 들어서는 자는
모든 희망을 버려라
-단테 신곡 지옥 편 제 3곡 1-9행
지옥 문에 새겨진 구절

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MSA.?

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MSA.?

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MSA.?

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Journey to Microservices

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Journey to Microservices

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Journey to Microservices

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Journey to Microservices

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Journey to Microservices
H/W/C 기준표 HOT WARM COLD
Volume MB-GB GB-TB PB
Item Size B-KB KB-MB KB-TB
Latency ms ms,sec min, hrs
Durability Low-High High Very High
Request rate Very High High Low
Retention Period Sort(~ under ?month) Long Very Long
Sample Data Source Transactional data
Operational data
Service log file
Click stream data
Flat files
System log file
Historical data
Marketing research data

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Journey to Microservices
Hot Data Warm Data Cold Data
Amazon
ElasticCache
Amazon
DynamoDB
Amazon
Aurora
Amazon
Elasticsearch
Amazon
EMR / Athena
/ RedShift
Amazon S3 Amazon Glacier
Average
Latency
ms ms ms, sec ms, sec sec, min, hr ms, sec, min (~
size)
hrs
Data Volume GB GB-TBs (no
limit)
GB-TB (64 TB
Max) (3 TB
Refer)
GB-TB GB-PB MB-PB GB-PB
Item Size B-KB KB(400 KB Max) KB(64 KB) KB(1 MB Max) MB-GB KB-GB (5 TB
Max)
GB (40 TB Max)
Request rate High-Very High Very High (no
limit)
High High Low-Very High Low-Very High
(no limit)
Very Low
Durability Low-Moderate Very High Very High High High Very High Very High

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Journey to Microservices

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Journey to Microservices

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Journey to Microservices

23. Journey to Microservices

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Journey to Microservices
OLTP
HOT WARM
Redis
DynamoDB
RDB(Aurora)
ElasticSearch
Back Office
Analyze DSS Batch
비 정형
ElasticSearch RedShift Athena
EMR

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Journey to Microservices

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Journey to Microservices

27. Journey to Microservices

28. Journey to Microservices

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AS-IS
단일화된 Architecture
- 공통화
- 재사용
- Copy/Paste
넷플릭스 그림
Journey to Microservices

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TO-BE
서비스별 Architecture
- 개별
- 재사용 불가
- 개발 언어 통일 불가
Journey to Microservices

31. 기획
설계
개발배포
보완
Journey to Microservices

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Journey to Microservices

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1
2
Journey to Microservices

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MSA to Function

35. MSA to Function

36. MSA to Function

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MSA to Function

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MSA to Function

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MSA to Function

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MSA to Function

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MSA to Function

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MSA to Function

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Next Generation

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Next Generation

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Next Generation

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Next Generation

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WHY 롯데정보통신 & 현대정보기술

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Why

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Why

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Why
1. DynamoDB+ElasticSearch+Kibana 의 찰떡 궁합
2. Micro Batch 로의 변화

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