Automatic Generation of a Software Performance Model Using an Object-Oriented Prototype

Early performance estimates for a new software system aid the design process by providing feedback when design decisions can be easily revised. Unfortunately, constructing a performance model of a distributed and concurrent software system can require significant effort. We propose an approach that reduces the model building effort by providing easy specification of performance test cases, empirical estimates for model parameters, automated model generation, and support for different types of models. A prototype is used to describe an object-based system, for which causal traces (angio traces) are recorded during execution. The traces are then processed into sequences of resource demands (workthreads), aggregated into system execution descriptions (workthread classes), and combined to generate performance models. The technique can also be applied at other stages of the development process, including the redesign of existing software. It should be possible to study a new software system's performance behavior under alternative designs and scenarios, especially at an early stage when design decisions can be easily revised. As the design evolves, increasing detail should be easily incorporated into the performance model to allow more complex performance concerns to be examined. We propose a prototype based approach that has these qualities. The two main approaches to combining software design and performance engineering are empirical and predictive. The fully empirical approach is to implement the system in some form, measure its performance, and then (if necessary) modify the software. The predictive approach, as described by Smith in [22] and [23], derives a model of software execution patterns (called an execution graph) from the design, constructs a second model to solve for performance predictions, and then uses the predictions to guide the modification of the design. This gives earlier warnings and supports more effective adjustments, which can be followed by an empirical study of the completed software. A weakness of the predictive approach arises in determining the performance parameters of the execution graph, without an implementation as a guide. Experience can be a help, and Smith provides guidelines for assigning values, but this has limitations. To derive the parameters the software execution path needs to be examined. This is difficult to do in object-based systems because it is obscured by polymorphism, inheritance, and class structures. The performance parameters may also depend on system data values that interact in complex ways which are difficult to predict and are resolved only during execution. This is particularly true of what we …