Special Session- New research introduction

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  • Special Session- New research introduction
▶ 인공지능 기술 제품 적용 사례

▶ Problem Diagnosis in the Cloud Using Log-Based Reference Models (로그 기반의 레퍼런스 모델을 통한 클라우드에서의 문제 진단)


탁병철 교수(경북대)

2012 Pennsylvania State University - University Park 박사
2003 한국과학기술원(KAIST), 전산학 석사
2000 연세대학교, 컴퓨터과학 전공 학사

2017년 3월 - 현재: 경북대학교 IT대학 컴퓨터학부
2012년 3월 - 2017년 2월: IBM TJ Watson Research Center
2004년 3월 - 2006년 3월: 한국전자통신연구원
2002년12월 - 2004년 3월: ㈜디지탈아리아

분산시스템, 운영체제, 클라우드컴퓨팅, 서비스 컴퓨팅, 클라우드 보안

로그 분석을 통한 분산 시스템 고장 감내성 강화, 컨테이너 클라우드 보안성 강화, 저레벨 시스템 모니터링


Problem Diagnosis in the Cloud Using Log-Based Reference Models (로그 기반의 레퍼런스 모델을 통한 클라우드에서의 문제 진단)


Problem diagnosis is one crucial aspect in the cloud operation that is becoming increasingly challenging. On the one hand, the volume of logs generated in today's cloud is overwhelmingly large. On the other hand, cloud architecture becomes more distributed and complex, which makes it more difficult to troubleshoot failures. In order to address these challenges, we have developed a tool, called LOGAN, that enables operators to quickly identify the log entries that potentially lead to the root cause of a problem. It constructs behavioral reference models from logs that represent the normal patterns. When problem occurs, our tool enables operators to inspect the divergence of current logs from the reference model and highlight logs likely to contain the hints to the root cause. To support these capabilities we have designed and developed several mechanisms. First, we developed log correlation algorithms using various IDs embedded in logs to help identify and isolate log entries that belong to the failed request. Second, we provide efficient log comparison to help understand the differences between different executions. Finally we designed mechanisms to highlight critical log entries that are likely to contain information pertaining to the root cause of the problem. We have implemented the proposed approach in a popular cloud management system, OpenStack, and through case studies, we demonstrate this tool can help operators perform problem diagnosis quickly and effectively.

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