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Semelhante a Theius: A Streaming Visualization Suite for Hadoop Clusters
Semelhante a Theius: A Streaming Visualization Suite for Hadoop Clusters (20)
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Theius: A Streaming Visualization Suite for Hadoop Clusters
- 1. Jon Tedesco IC2E 2013, San Francisco, CA, USA Jon Tedesco, Roman Dudko, Abhishek Sharma, Reza Farivar, Roy Campbell
- 2. Problem ◦ System administrators Bottleneck for detecting & responding to failures Communicate state of system quickly Monitoring ◦ Streaming, real-time data ◦ Ganglia Widely used, scalable, and flexible Visualization Prediction ◦ Online prediction algorithms (real-time) Visualization Problem ◦ Ganglia Static, time-based graphs
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- 5. Interactive ◦ Responsive and controllable Real-time ◦ Streaming, real-time, automatic Informative ◦ Direct attention to potential problems and artifacts Intuitive ◦ Demand skill, not experience Scalable ◦ Visualize large clusters without sacrificing usability 5
- 6. Objectives ◦ Streaming data ◦ Configurable and interactive ◦ Informative Use cases ◦ Heterogeneous cluster ◦ Rack failure ◦ Node failure ◦ Uneven load distribution 6
- 7. Architecture ◦ Simulator Generates simulated cluster data Streams data to clients ◦ Webpage Asynchronous & interactive Implementation ◦ JavaScript d3.js jQuery ◦ Python ◦ AJAX 7
- 8. Data ◦ Methodology Data types from previous work Heuristic values ◦ Examples CPU, memory, context switch rate Log events MapReduce tasks and jobs Failure or event prediction 8
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- 10. Main Visualization Customizable using control panel Aggregate view ◦ Summarize and drill down Draws attention to anomalies 10
- 11. Switch between main visualizations Seamless transitions ◦ Uninterrupted data stream 11
- 12. Hierarchy of nodes, organized by rack Color and size configurable Scalable using summarization and drill- down Identify abnormal rack or nodes 12
- 13. Hierarchy of nodes, organized by rack Color and size configurable Scalable using summarization and drill-down Identify abnormal rack or nodes 13
- 14. Grouped by job Color and size configurable ◦ Example uses role for color, time remaining for size Identify abnormal jobs or tasks 14
- 15. Grouped by rack Color and size configurable ◦ Example uses CPU usage and rack color coding Identify abnormal nodes or racks 15
- 16. Identify trends with nodes and racks Color, size, and plots configurable Identify correlations between metrics 16
- 17. Detailed data for individual node Traditional visualizations for single node 17
- 18. Controls Configure metrics for visualizations Pause and resume data stream Legend for main visualization 18
- 19. Aggregate Data Aggregate data for the cluster ◦ Log events stream ◦ Global node data ◦ Summarization data 19
- 20. History Controls Snapshots of historical data ◦ See main visualization and sidebar data at certain time Visualize metric across time 20
- 21. Scalable ◦ Drill-down and summarization ◦ Efficient web-based framework Intuitive, informative ◦ Topological visualization ◦ Draw attention to abnormalities Interactive, real-time ◦ Designed for streaming data ◦ Configurable visualization ◦ Pause, rewind, resume 21
- 22. Experimental Setup ◦ Compare Theius to Ganglia ◦ 5 graduate students at UIUC No prior experience with Ganglia or Theius ◦ 4 comparative tasks Both Ganglia & Theius ◦ 6 scenarios for trends and correlations Theius only ◦ Timings & subjective feedback 22
- 23. 60 Tasks 50 ◦ Scenario 1 CPU usage in single node 40 ◦ Scenario 2 Seconds 30 Node with highest CPU ◦ Scenario 3 20 High memory usage 10 nodes ◦ Scenario 4 0 Aggregate cluster use Theius Ganglia 23
- 24. Task 1 ◦ Identify abnormal rack in heterogeneous cluster 2.2 s Task 2 ◦ Identify rack with abnormal CPU usage 6.2 s Task 3 10.0 s ◦ Identify machine that logged the last fatal error Task 4 67.4 s ◦ Identify machine with high CPU, memory usage, or context switch rate Task 5 ◦ Identify rack with high CPU, memory usage, or context switch rate 1.2 s Task 6 7.8 s ◦ Identify correlation between context switch rate and CPU usage 24
- 25. Source Code ◦ https://github.com/jtedesco/Theius Future Work ◦ User study System administrators Larger group Timing as appropriate metric ◦ MapReduce-specific visualizations ◦ Scalability experiments 25
- 26. Jon Tedesco IC2E 2013, San Francisco, CA, USA Jon Tedesco, Roman Dudko, Abhishek Sharma, Reza Farivar, Roy Campbell