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With tremendous growing interests in Big Data systems, analyzing andfacilitating their performance improvement become increasingly important.Although there have much research efforts for improving Big Data systemsperformance, efficiently analysing and diagnosing performance bottlenecks overthese massively distributed systems remain a major challenge. In this paper, wepropose a spatio-temporal correlation analysis approach based on stagecharacteristic and distribution characteristic of Big Data applications, whichcan associate the multi-level performance data fine-grained. On the basis ofcorrelation data, we define some priori rules, select features and vectorizethe corresponding datasets for different performance bottlenecks, such as,workload imbalance, data skew, abnormal node and outlier metrics. And then, weutilize the data and model driven algorithms for bottlenecks detection anddiagnosis. In addition, we design and develop a lightweight, extensible toolHybridTune, and validate the diagnosis effectiveness of our tool withBigDataBench on several benchmark experiments in which the outperformstate-of-the-art methods. Our experiments show that the accuracy ofabnormal/outlier detection we obtained reaches about 80%. At last, we reportseveral Spark and Hadoop use cases, which are demonstrated how HybridTunesupports users to carry out the performance analysis and diagnosis efficientlyon the Spark and Hadoop applications, and our experiences demonstrateHybridTune can help users find the performance bottlenecks and provideoptimization recommendations.

HybridTune: Spatio-temporal Data and Model Driven Performance Diagnosis for Big Data Systems