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Reliability-Guaranteed Task Assignment and Scheduling for Heterogeneous Multiprocessors Considering Timing Constraint

机译:考虑时序约束的异构多处理器的可靠性保证任务分配和调度

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Heterogeneous multiprocessors have become the mainstream computing platforms nowadays and are increasingly employed for critical applications. Inherently, heterogeneous systems are more complex than homogeneous systems. The added complexity increases the potential of system failures. This paper addresses this problem by proposing a reliability-guaranteed task assignment and scheduling approach for heterogeneous multiprocessors considering timing constraint. We propose a two-phase approach to solve this problem. In the first phase, we determine assignments for heterogeneous multiprocessors such that both reliability requirement and timing constraint can be satisfied with the minimum total system cost. Efficient algorithms are proposed to produce optimal solutions for simple-path and tree-structured task graphs in polynomial time. When the input graph is a directed acyclic graph (DAG), the task assignment problem is NP-Complete. In this situation, we first develop an Integer Linear Programming (ILP) formulation to generate optimal solutions. Then, we propose a polynomial-time heuristic algorithm to find near optimal solutions. In the second phase, based on the assignments obtained in the first phase, we propose a minimum resource scheduling algorithm to generate a schedule and a feasible configuration that uses as little resource as possible. Experimental results show that the proposed algorithms and the ILP formulation can effectively reduce the total cost compared with the previous work.
机译:异构多处理器已成为当今的主流计算平台,并越来越多地用于关键应用。本质上,异构系统比同类系统更为复杂。增加的复杂性增加了系统故障的可能性。本文通过提出一种考虑时序约束的异构多处理器可靠性保证任务分配和调度方法,解决了这一问题。我们提出了一种两阶段方法来解决此问题。在第一阶段,我们确定异构多处理器的分配,以便可以用最小的总系统成本来满足可靠性要求和时序约束。提出了有效的算法以在多项式时间内为单路径和树状任务图产生最佳解决方案。当输入图是有向无环图(DAG)时,任务分配问题是NP-Complete。在这种情况下,我们首先开发一个整数线性规划(ILP)公式以生成最佳解决方案。然后,我们提出了一种多项式时间启发式算法来寻找最佳解。在第二阶段中,基于在第一阶段中获得的分配,我们提出了一种最小资源调度算法来生成调度和使用尽可能少的资源的可行配置。实验结果表明,与以前的工作相比,本文提出的算法和ILP公式可以有效降低总成本。

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