Abstract
Embedded systems are becoming more and more complex, thus demanding innovative means to tame their challenging development. Among others, early architecture optimization represents a crucial activity in the development of embedded systems to maximise the usage of their limited resources and to respect their real-time requirements. Typically, architecture optimization seeks good architecture candidates based on model-based analysis. Leveraging abstractions and estimates, this analysis usually produces approximations useful for comparing architecture candidates. Nonetheless, approximations do not provide enough accuracy in estimating crucial extra-functional properties. In this article, we provide an architecture optimization framework that profits from both the speed of model-based predictions and the accuracy of execution-based measurements. Model-based optimization rapidly finds a good architecture candidate, which is refined through optimization based on monitored executions of automatically generated code. Moreover, the framework enables the developer to leverage her optimization experience. More specifically, the developer can use runtime monitoring of generated code execution to manually adjust task allocation at modeling level, and commit the changes without halting execution. In the article, our architecture optimization mechanism is first described from a general point of view and then exploited for optimizing the allocation of software tasks to the processing cores of a multicore embedded system; we target extra-functional properties that can be concretely represented and automatically compared for different architectural alternatives (such as memory consumption, energy consumption, or response-time).
Highlights
Embedded systems can be found in most electronic and electrical products
Model-based analysis, which naturally relies on abstractions and estimations, provides approximations that in some cases need a verification at runtime in the form of execution-based measurements
We describe our architecture optimization mechanism which combines model-based analysis and system execution and demonstrate its usefulness by employing it for optimizing allocation of software tasks to the cores of a multicore embedded system
Summary
Embedded systems can be found in most electronic and electrical products. Software running on them is becoming more complex at daunting pace. Model-based analysis, which naturally relies on abstractions and estimations, provides approximations that in some cases need a verification at runtime in the form of execution-based measurements These measurements are gathered by monitoring the execution of automatically generated code. We describe our architecture optimization mechanism which combines model-based analysis and system execution and demonstrate its usefulness by employing it for optimizing allocation of software tasks to the cores of a multicore embedded system. The expert’s knowledge still plays an important role in optimization activities, we introduce the infrastructure needed for the developer to manually tune allocation of tasks to cores at modeling level and dynamically apply changes at runtime.
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