A profile-driven statistical analysis framework for the design optimization of soft real-time applications

ABSTRACT

Soft real-time applications lack a formal methodology for their design optimization. Well-established techniques from hard real-time systems cannot be directly applied to soft real-time applications, without losing key benefits of the soft real-time paradigm.

We introduce a statistical analysis framework that is well-suited for discovering opportunities for optimization of soft real-time applications. We demonstrate how programmers can use the analysis provided by our framework to perform aggressive soft real-time design optimizations on their applications.

The paper introduces the Context Execution Tree (CET) representation for capturing the statistical properties of function calls in the context of their execution in the program. The CET is constructed from an offline-profile of the application. Statistical measures are coupled with techniques that extract runtime distinguishable call-chains. This combination of techniques is applied to the CET to find statistically significant patterns of activity that i) expose slack in the execution of the application with respect to its soft real-time requirements, and ii) can be predicted with low overhead and high reliability during the normal execution of the application.

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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques

General Terms:
Design, Measurement, Performance

Keywords:
behavior prediction, pattern detection, profiling, signatures, soft real-time, statistical analysis