R2: an application-level kernel for record and replay

  • Authors:

  • Zhenyu Guo;Xi Wang;Jian Tang;Xuezheng Liu;Zhilei Xu;Ming Wu;M. Frans Kaashoek;Zheng Zhang

  • Affiliations:

  • Microsoft Research Asia;Tsinghua University;Microsoft Research Asia;Microsoft Research Asia;Tsinghua University;Microsoft Research Asia;MIT CSAIL;Microsoft Research Asia

  • Venue:
  • OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
  • Year:
  • 2008

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Abstract

Library-based record and replay tools aim to reproduce an application's execution by recording the results of selected functions in a log and during replay returning the results from the log rather than executing the functions. These tools must ensure that a replay run is identical to the record run. The challenge in doing so is that only invocations of a function by the application should be recorded, recording the side effects of a function call can be difficult, and not executing function calls during replay, multithreading, and the presence of the tool may change the application's behavior from recording to replay. These problems have limited the use of such tools. R2 allows developers to choose functions that can be recorded and replayed correctly. Developers annotate the chosen functions with simple keywords so that R2 can handle calls with side effects andmultithreading. R2 generates code for record and replay from templates, allowing developers to avoid implementing stubs for hundreds of functions manually. To track whether an invocation is on behalf of the application or the implementation of a selected function, R2 maintains a mode bit, which stubs save and restore. We have implemented R2 on Windows and annotated large parts (1,300 functions) of the Win32 API, and two higher-level interfaces (MPI and SQLite). R2 can replay multithreaded web and database servers that previous library-based tools cannot replay. By allowing developers to choose high-level interfaces, R2 can also keep recording overhead small; experiments show that its recording overhead for Apache is approximately 10%, that recording and replaying at the SQLite interface can reduce the log size up to 99% (compared to doing so at the Win32 API), and that using optimization annotations for BitTorrent and MPI applications achieves log size reduction ranging from 13.7% to 99.4%.