Task Allocation Optimization for Multicore Embedded Systems

• Main Author: Feljan, Juraj
• Format: Doctoral Thesis
• Language: English
• Published: Mälardalens högskola, Inbyggda system 2015
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-29407; http://nbn-resolving.de/urn:isbn:978-91-7485-241-7

Modern embedded systems are becoming increasingly performance intensive, since, on the one hand, they include more complex functionality than before, and one the other hand, the functionality that was typically realized with hardware is often moved to software. Multicore technology, previously successfully used for general-purpose systems, is penetrating into the domain of embedded systems. While it does increase the performance capacity, it also introduces the problem of how to allocate software tasks to the cores of the hardware platform, as different allocations exhibit different extra-functional properties. An intuitive example is allocating too many tasks to a core --- the core will be overloaded and tasks will miss their deadlines. This thesis addresses the issue of task allocation in multicore embedded systems. The overall goal of the thesis is to advance the way soft real-time multicore systems are developed, by providing new methods and tools that enable deciding already at design-time which task to run on which core, with respect to a number of timing-related extra-functional properties. To achieve this goal, we developed a model-based framework for task allocation optimization. The framework uses model simulation in order to obtain performance predictions for particular task allocations. This in turn enables testing a large number of allocation candidates in search for one that exhibits good timing-related performance. Apart from defining and implementing the framework, three additional contributions are provided, each tackling a particular aspect of the framework: the influence of task allocation on communication duration is studied and interpreted in the context of design-time model-based analysis; a novel heuristic for guiding task allocation optimization is defined; and finally, a novel optimization method combining performance prediction and performance measurement is defined.