Name
Title | ||
|---|---|---|
BDTune: Hierarchical Correlation-based Performance Analysis and Rule-based Diagnosis for Big Data Systems |
Abstract | ||
|---|---|---|
Although big data systems are in widespread use and there have much research efforts for improving big data systems performance, efficiently analysing and diagnosing performance bottlenecks over these massively distributed systems remain a major challenge. In this paper, we propose a hierarchical correlation-based analysis and rule-based diagnostic approach for big data systems. The key approaches lie in identifying performance bottlenecks, classifying root causes, analyzing performance according to multi-level performance metrics, and setting diagnostic rules for performance tuning. Based on this approach, we have implemented BDTune—a lightweight, extensible and transparent tool that can provide valuable insights into performance of big data applications with a very low overhead. We also report our experience on how to use BDTune to conduct performance analysis and performance bottlenecks diagnosis, and demonstrate BDTune can help users find the performance bottlenecks and provide optimization recommendations. |
Year | Venue | Keywords |
|---|---|---|
2016 | BigData | Big Data Systems,Correlation-based Analysis,Bottleneck Detection,Root Causes Diagnosis |
Field | DocType | Citations |
Data mining,Rule-based system,Computer science,Correlation,Artificial intelligence,Extensibility,Big data,Performance tuning,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |