Publication | Closed Access
Ganesha
80
Citations
15
References
2010
Year
Cluster ComputingEngineeringDistributed AlgorithmsGridmix Hadoop BenchmarkFault ToleranceMap-reduceDistributed Data AnalyticsHadoop ProblemsData ScienceData IntegrationParallel ComputingData ManagementComputer ScienceOs-level MetricsScalable ComputingCloud ComputingParallel ProgrammingMassive Data ProcessingBig Data
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.
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