Pareto-optimization-based run-time task scheduling for embedded systems

First IEEE/ACM/IFIP International Conference on Hardware/ Software Codesign and Systems Synthesis (IEEE Cat. No.03TH8721) Pub Date : 2003-10-01 DOI:10.1145/944645.944680

Peng Yang, F. Catthoor

引用次数: 81

Abstract

Pareto-set-based optimization can be found in several different areas of embedded system design. One example is task scheduling, where different task mapping and ordering choices for a target platform will lead to different performance/cost tradeoffs. To explore this design space at runtime, a fast and effective heuristic is needed. We have modeled the problem as the well known Multiple Choice Knapsack Problem (MCKP) and have developed a fast greedy heuristic for the run-time task scheduling. To show the effectiveness of our algorithm, examples from randomly generated task graphs and realistic applications are studied. Compared to the optimal dynamic programming solver, the heuristic is more than ten times faster while the result is less than 5% away from the optimum. Moreover, due to its iterative feature, the algorithm is well suitable to be used as an online algorithm.

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基于pareto优化的嵌入式系统运行时任务调度

基于帕累托集的优化可以在嵌入式系统设计的几个不同领域中找到。一个例子是任务调度，其中针对目标平台的不同任务映射和排序选择将导致不同的性能/成本权衡。为了在运行时探索这个设计空间，需要一种快速有效的启发式方法。我们将该问题建模为众所周知的多选题背包问题(Multiple Choice backpack problem, MCKP)，并开发了一种用于运行时任务调度的快速贪婪启发式算法。为了证明算法的有效性，本文还对随机生成任务图的实例和实际应用进行了研究。与最优动态规划求解器相比，启发式算法的求解速度提高了10倍以上，而求解结果与最优解的误差小于5%。此外，由于其迭代特性，该算法非常适合作为在线算法使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

First IEEE/ACM/IFIP International Conference on Hardware/ Software Codesign and Systems Synthesis (IEEE Cat. No.03TH8721)

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