달력

92019  이전 다음

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Ganesha: Black-Box Diagnosis of MapReduce Systems
Link:http://portal.acm.org/citation.cfm?id=1710115.1710118
Xinghao Pan, Jiaqi Tan, Soila Kavulya, Rajeev Gandhi, Priya Narasimhan   Carnegie Mellon University, Pittsburgh, PA
SPECIAL ISSUE: Special issue on the workshop on hot topics in measurement & modeling of computer systems (HotMetrics 2009)

Abstract -
Ganesha aims to diagnose faults transparently (in a black-box manner) in MapReduce systems, by analyzing OS-level metrics. Ganesha's approach is based on peer-symmetry under fault-free conditions, and can diagnose faults that manifest asymmetrically at nodes within a MapReduce system. We evaluate Ganesha by diagnosing Hadoop problems for the Gridmix Hadoop benchmark on 10-node and 50-node MapReduce clusters on Amazon's EC2. We also candidly highlight faults that escape Ganesha's diagnosis.

[문제 설명]
There are manu challenges in problem localization and root-cause analysis.
[목표를 설명할 때]
Ganesha aims to diagnose faults transparently in MapReduce systems, by analyzing OS-level metrics.
[설명]
We describe Ganesha, our black-box diagnostic approach that we apply to diagnose such performance problems in Hadoop.
[메뉴 배열]
3.1 Hypotheses, 3.2 Goals, 3.3 Non-Goals, 3.4 Assumptions
[수식 설명]
Thus, the true-positive and false-positive ratios are computed as : [수식]

Posted by Teshi