HPC_LogFile_Analysis

#step 1

• Collect the bhist logs（Use python or shell script）

#step 2

• Parse the bhist log for each user

• Modify each user's logfile to a proper excel format

#step 3

• Visulize excel data tables, analyze user's submission habits and properties
  • job_accuracy
  • job_runtime
  • job_total_CPU_time
  • job_submit_moment
  • job_pend_time
  • job_memory_used
  • job_full_states_by_day
  • job_long_pending_by_day
  • job_submission_counts_by_day
  • job_latest_features

#step 4

• Extract queue's features, analyze the queue's recent status
  • User
  • Status
  • DateRange
  • Days_Recorded
  • Jobs_Recorded
  • Accuracy
  • MEAN_Memory
  • MEAN_CPU_Time
  • MEAN_Real_Time
  • MEAN_Pend
  • MEAN_Submission_Count_Recorded
  • MEAN_Submission_Count
  • latest_job_submission_counts
  • latest_long_pending_jobs

#step 5

• Correlation analysis

#step 6

• Further exploration, to be continued...
  • user feature's cluster analysis
  • bjobs logfile analysis
  • Real-time sorting of long-pending jobs
  • Perhaps use ELK tools

#Summary

• 难点在于业务梳理,以及指标制定，工具使用还属其次
• 完成日志数据分析全流程：数据提取—数据清洗—特征定义—数据可视化—指导决策
• 代码规范、优化什么的没考虑

# Results presentation