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Efficiently computing derived performance data

  • Special Issue On Knowledge Based Software Engineering
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Abstract

In this paper, we examine the problem of generating instrumentation that efficiently computes derived performance data of program execution. The instrumentation computes these derived data at run time as the directly accessible data is collected. It tests conditions defined on the collected data and eliminates the data that does not satisfy the stated conditions, thus, avoiding recording irrelevant data. We examine this problem in terms of generating instrumentation for computing the answers to monitoring questions. Our solution is based on the use of temporal conditions defined on the events in the questions to determine which data to record and to be used as relevant run time filters on the events producing that data.

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References

  • Bates, P. 1987. Shuffle Automata: A Formal Model for Behavior Recognition in Distributed Systems. Technical Report COINS Technical Report 87-27, Computer and Information Science Department, University of Massachusetts at Amherst.

  • Bates, P. C. 1988. Debugging heterogeneous distributed systems using event-based models of behavior. InProceedings of ACM SIGPLAN and SIGOPS Workshop on Parallel and Distributed Debugging, pp. 11–22. Madison, Wisconsin.

  • Bruegge, B., and Hibbard, P. 1983. Generalized path expressions: A high level debugging mechanism. InProceedings of the ACM SIGSOFT Software Engineering Symposium on High Level Debugging, pp. 34–44. Pacific Grove California.

  • Cohen, D. 1986. Automatic compilation of logical specifications into efficient programs. InProceedings of the 5th National Conference on Artificial Intelligence, pp. 20–25. Philadelphia, PA: AAAI.

    Google Scholar 

  • Cohen, D. 1988.AP5 user's manual. ISI/USC.

  • Cohen, D. 1989. Compiling complex database transition triggers. InProceedings of the ACM SIGMOD Conference on Management of Data, pp. 225–234.

  • Forgy, C. L. 1979.On the Efficient Implementation of Production Systems. Ph.D. thesis, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA 15213.

    Google Scholar 

  • Graham, S., et al. 1983. An execution profiler for modular programs.Software Practice and Experience, 13:671–683.

    Google Scholar 

  • Hseush, W., and Kaiser, G. 1988. Data path debugging: Data-oriented debugging for a concurrent programming language. InProceedings of ACM SIGPLAN and SIGOPS Workshop on Parallel and Distributed Debugging, pp. 236–247. Madison, Wisconsin.

  • Hseush, W., and Kaiser, G. 1990. Data path expressions and predecessor automata.SIGPLAN Notices, pp. 11–20.

  • King, J. 1981. QUIST: A system for semantic query optimization in relational databases. InProceedings of the 7th International Conference on Very Large Data Bases, pp. 510–517. Cannes, France.

  • Knuth, D. 1971. An empirical study of Fortran programs.Software-Practice and Experience, 1:105–133.

    Google Scholar 

  • Leung, T. Y. C., and Muntz, R. R. 1990. Query processing for temporal databases. InProceedings of the International Conference on Data Engineering, pp. 200–208. Los Angeles, California.

  • Liao, Y. 1991. Requirement directed automatic instrumentation generation for program monitoring and measuring. InProceedings of the 6th Knowledge Based Software Engineering Conference, pp. 179–190. Syracuse, New York.

  • Liao, Y. 1992.An Automatic Programming Approach to High Level Program Monitoring and Measuring. Ph.D. thesis, Department of Computer Science, University of Southern California, Los Angeles, CA 90089.

    Google Scholar 

  • Liao, Y., and Cohen, D. 1992. PMMS: A framework and system for high level program monitoring and measuring. InProceedings of the IFIP Congress '92, Madrid, Spain.

  • Plattner, B., and Nievergelt, J. 1981. Monitoring program execution: A survey.IEEE Computer, pp. 76–93.

  • CLF Project.CLF manual. USC/Information Science Institute, 4676 Admiralty Way, Marina del Rey, CA 90292, August 1988.

  • Snodgrass, R. 1987. The Temporal Query Language TQuel.ACM Transactions on Database Systems, 12(2):247–298.

    Google Scholar 

  • Snodgrass, R. 1988. A relational approach to monitoring complex systems.ACM Transactions on Computer Systems, 6(2):157–196.

    Google Scholar 

  • Steele, L. J. Jr. 1990.COMMON LISP: The Language. Digital Press, second edition.

  • Ullman, J. D. 1989.Principles of Database and Knowledge-Base Systems, volume 2. Computer Science Press.

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Authors and Affiliations

  1. NEC Systems Laboratory, Inc., 4 Independence Way, 08540, Princeton, New Jersey

    Yingsha Liao

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  1. Yingsha Liao
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Liao, Y. Efficiently computing derived performance data. Autom Software Eng 1, 11–29 (1994). https://doi.org/10.1007/BF00871690

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