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Efficient parallel multi-objective optimization for real-time systems software design exploration

机译：实时系统的高效并行多目标优化软件设计探索

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摘要

Real-time embedded systems may be composed of a large number of time constrained functions. During software architecture design, these functions must be assigned to tasks that will run the functions on the top of a real-time operating systems (RTOS). This is a challenging work due to the large number of valid candidate functions to tasks assignment solutions. Moreover, the impact of the assignment on the system performance criteria (often conflicting) should be taken into account in the architecture exploration. The automation of the design exploration by the use of metaheuristics such as multi-objective evolutionary algorithm (MOEA) is a suitable way to help the designers. MOEAs approximate near-optimal alternatives at a reasonable time when compared to an exact search method. However, for large-scale systems even a MOEA method is impractical due to the increased time required to solve a problem instance. To tackle this problem, we present in this article a parallel implementation of the Pareto Archived Evolution Strategy (PAES) algorithm used as a MOEA for the design exploration. The proposed parallelization method is based on the well-known Master-Slave paradigm. Additionally, it involves a new selection scheme in the PAES algorithm. Results of experimentations provide evidence that, on one hand, the parallel approach can considerably speed up the design exploration and the optimization processes. On the other hand, the proposed selection strategy improves the quality of obtained solutions as compared to the original PAES selection schema.
机译：实时嵌入式系统可能由大量时间受限的功能组成。在软件体系结构设计期间，必须将这些功能分配给将在实时操作系统（RTOS）顶部运行这些功能的任务。由于任务分配解决方案具有大量有效的候选功能，因此这是一项具有挑战性的工作。此外，在架构探索中应考虑分配对系统性能标准的影响（通常是冲突的）。通过使用诸如多目标进化算法（MOEA）之类的元启发法来进行设计探索的自动化是帮助设计人员的合适方法。与精确搜索方法相比，MOEA在合理的时间近似于最佳选择。但是，对于大型系统，由于解决问题实例所需的时间增加，因此即使采用MOEA方法也是不切实际的。为了解决这个问题，我们在本文中提出了并行排列的Pareto存档演化策略（PAES）算法，该算法用作MOEA进行设计探索。所提出的并行化方法基于众所周知的Master-Slave范例。此外，它在PAES算法中涉及一种新的选择方案。实验结果证明，一方面，并​​行方法可以大大加快设计探索和优化过程。另一方面，与原始的PAES选择方案相比，所提出的选择策略提高了获得的解决方案的质量。

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