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Shimin Chen LBA Reading Group Presentation PowerPoint Presentation
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Shimin Chen LBA Reading Group Presentation

Shimin Chen LBA Reading Group Presentation

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Shimin Chen LBA Reading Group Presentation

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  1. Triage: Diagnosing Production Run Failures at the User’s SiteJ.Tucek, S.Lu, C.Huang, S.Xanthos, Y.Zhou, SOSP’07 Shimin ChenLBA Reading Group Presentation

  2. Motivation • Software failures at end users’ sites • Released SW still contain bugs • Major contributor to down time and security holes

  3. Previous work: offsite diagnosis (At the development site with programmers) • Cannot send programmers onsite to debug each failure • Privacy concerns limit the release of information (e.g. coredumps) to programmers • Difficult to reproduce failures in house for diagnosis • Cannot provide timely guidance to choose recovery strategy or security defense against attacks  Automatically diagnosing software failures occurring in end-user site production runs

  4. Difference between Detection and Diagnosis • Detection • “blindly” screens for possible problems • sees bug manifestation • Diagnosis • aims to understand a particular failure • finds root causes

  5. Failure Diagnosis

  6. More on Related Work • Diagnosis • Interactive debuggers: gdb • Program slicing: tools for removing unrelated source code lines • PSE: offline partial execution path constructors from a core dump 100X Overhead or rely on human guidance • Onsite SW failure diagnosis is primitive: • Dr.Watson, Mozilla Quality Feedback Agent • Collect core dumps and other simple raw information • Extracting more detailed information: • Traces network connections, system call traces, traces of predicated values • Deterministic replay tools for uniprocessor systems

  7. Challenges for Onsite Diagnosis • Efficiently reproduce the occurred failure • Impose little overhead during normal execution • Require no human involvement • Require no prior knowledge

  8. Contributions: • Conduct just-in-time diagnosis with checkpoint-reexecution support • New diagnosis techniques: delta generation and delta analysis • Automated, top-down, human-like diagnosis protocol • Leverage previous failure analysis techniques for onsite and post-hoc diagnosis • Real system experiments: Linux with 9 applications (including MySQL, Apache, etc.) • User study with 15 programmers

  9. Outline • Introduction • Triage Architecture Overview • Diagnosis Protocol • Delta Generation and Analysis • Other diagnosis Techniques • Evaluations • Limitations and Extensions • Conclusion

  10. Architecture

  11. Architecture Rx Assertions & exceptions

  12. Outline • Introduction • Triage Architecture Overview • Diagnosis Protocol • Delta Generation and Analysis • Other diagnosis Techniques • Evaluations • Limitations and Extensions • Conclusion

  13. Diagnosis Information • Failure type and nature • Failure-triggering input and environmental conditions • Failure-related code/variables and fault propagation chain

  14. Extensions • Above is the implemented protocol • Can have many extensions • Bug diagnosis techniques, diagnosis order • Automatically fixing the bug • Filter failure-triggering inputs • Generate a patch? Dynamically deleting code or changing variable values?

  15. Outline • Introduction • Triage Architecture Overview • Diagnosis Protocol • Delta Generation and Analysis • Other diagnosis Techniques • Evaluations • Limitations and Extensions • Conclusion

  16. Intuition • Identify what differs between failing and non-failing runs • Automatic, repetitive delta replays with controlled variation and manipulation to execution environments

  17. Goals of Delta Generation • Generate many similar failing and non-failing replays • Collect info with DBI for delta analysis • Identify signatures of failure-triggering inputs and execution environments • Send to programmers • Guide online recovery and security defense

  18. Delta Generation: changing inputs • At failure time, has a list of seen requests • Figure out which subset causes the failure • Then try: delete character or randomly change character in the requests

  19. Delta Generation: changing environments • (similar to Rx and diehard) • Pad or zero-fill new allocations, change message order, drop messages, manipulate thread scheduling, modify system environment • Based on previous knowledge on error type can focus on some of these changes

  20. Delta Generation: speculative changes (preliminary) • Force a non-taken branch • Forcefully change data value • Still being explored

  21. Results of Delta Generation • Path: Basic block sequence • Basic block vector (counts)

  22. Delta Analysis • Basic block vector comparison • Path comparison • Intersection with backward slice

  23. Basic block Vector (BBV) Comparison • BBV contains dynamic count for each basic block • First calculate the Manhattan distance of each pair of failing and non-failing BBVs • Find out the minimal distance failing and non-failing BBVs

  24. Path Comparison • Given the two BBVs, find the minimum edit distance (insertion/deletion/substitution) between the two corresponding paths

  25. Backward Slicing and Result Intersection

  26. Data Delta Analysis (unimplemented) • Compare the values of key variables

  27. Outline • Introduction • Triage Architecture Overview • Diagnosis Protocol • Delta Generation and Analysis • Other diagnosis Techniques • Evaluations • Limitations and Extensions • Conclusion

  28. Other Diagnosis Techniques Used • Core dump analysis: • Register state, signal, basic summaries of the stack and heap • Unwind stack for call-chain • Walk malloc’s internal data structure for heap problems • Dynamic bug detection during replay • Memory bug detector • Data race detector (PIN +happens before)

  29. Outline • Introduction • Triage Architecture Overview • Diagnosis Protocol • Delta Generation and Analysis • Other diagnosis Techniques • Evaluations • Limitations and Extensions • Conclusion

  30. Implementation • Linux 2.4.22 • PIN: dynamically attached to target program in the beginning of every reexecution attempt

  31. Machine Environments • Single processor 2.4GHz Pentium-4, 512KB cache, 1GB memory • For server application, two such machines connected with 100Mbps ethernet • Take checkpoints every 200ms, keeps 20 checkpoints

  32. Applications and Failures

  33. Results • Successfully found fault-triggering input • Delta analysis reduces dynamic basic blocks: avg 63%

  34. Case Study: Apache

  35. Normal Execution Overhead

  36. Diagnosis Efficiency

  37. User Study • 5 bugs in 3 toy programs and 2 real programs • 15 Programmers X 5 bugs randomly given Triage output or not

  38. Outline • Introduction • Triage Architecture Overview • Diagnosis Protocol • Delta Generation and Analysis • Other diagnosis Techniques • Evaluations • Limitations and Extensions • Conclusion

  39. Limitation and Extensions • Privacy policy: easier to understand the info in a Triage report in order to specify privacy policy • Automatic patch generation: limited success – patching for buffer overflow for a particular allocation point • Difficult bugs: do not crash or take long time to manifest (checkpoints may not be kept long enough)

  40. Limitation and Extensions • Deterministic replay on multiprocessors • Deployment on highly-loaded machines • Cannot afford to full-fledged Triage analysis • Background? On a separate machine? Deferred? Simplified (better than nothing) • Handle false positives • Never encountered in the experiments • But may be solved by more sophisticated consistency checks among results produced by different diagnosis techniques

  41. Conclusion • Onsite SW failure diagnosis • Lightweight checkpoint and recovery • Failure diagnosis protocol • Delta generation and analysis