A wideband approach to integrating performance prediction into a software design environment

Abstract

Performance predictions for software designs are needed to give early warnings of problems such as resource saturation or excessive delays. A wideband approach to performance prediction is one which can be applied to a wide range of design descriptions and parameters, ranging from incomplete early descriptions and estimates, to target system measurement data. The paper describes a practical development of such an approach, designed to be integrated with an existing software design environment called ObjecTime. It uses a performance model to make predictions about performance measures such as the average time an actor or processor is busy, the communication overhead of a scenario, or the delays in processing an input event. Automation shrinks the cost and time for model building, and synchronizes the design and model as the design develops. The wideband approach can be applied throughout the software life-cycle, from early sketchy designs until after deployment. PositioningThere is an increasingly evident need for better methods and tools for predicting and improving the performance of software products. The difficulty is most acute in the early stages of design, or for extrapolation beyond the present status of the design (e.g., for scalability analysis). Performance models are the natural way to provide predictions for a range of environmental conditions or design alternatives and these predictions can be used to detect problems and suggest corrections. However, modelling is at present too slow and too costly. We describe a new, evolutionary, low cost wideband approach to performance analysis using models, including how it can be integrated into a typical high-level design environment. The domain of application of this environment is real-time interactive software, such as communications software. In this domain there are significant uncertainties in the execution demands and delays, and performance evaluation is statitisical, based on means, variances and percentiles. Hard deadlines, in which missing a deadline means a system failure, are not a major concern here. Intuition is a poor guide to creating a design with good performance. There are too many factors, and it is confusing that some of them apply only for certain situations or data configurations. Real-time software designers often have only the vaguest idea of the performance impact of certain key design decisions. It is not uncommon in practice to find that performance predictions made by design teams are off by an order of magnitude and more! Even when formal performance predicting models of software are actually constructed they may not be trusted.Under such circumstances, designing for performance is a question of trial and error where the intuition and experience of the design team plays a key role. Putative configurations are proposed, the corresponding performance models derived and evaluated, and the prediction results compared against requirements and previous results. To make this process more effective as much of it should be automated as pos...

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