两类异构多处理器上约束期限并行任务的调度

Björn Andersson, Gurulingesh Raravi
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引用次数: 5

摘要

考虑在多处理器上调度任务集以满足所有截止日期的问题。假设有约束截止日期的零星任务和阶段并行(即,一个任务包括一个或多个阶段,其中一个阶段包含一个或多个段,以便允许同一阶段中的段并行执行,并且只有在前一个阶段的所有段都完成时才允许执行一个段)。还假设一个两种类型的异构多处理器平台(即,有两种类型的处理器,类型1和类型2,并且对于每个任务,在类型1处理器和类型2处理器上都有其执行速度的规范)和类型内迁移(即,作业可以在相同类型的处理器之间迁移,但对于任务,该任务的所有作业必须在相同的处理器类型上执行)。我们提出了一种算法来解决这个问题;它将每个任务分配给一种处理器类型,然后使用global-Earliest-Deadline-First对每种类型的处理器上的任务进行调度。它具有伪多项式时间复杂度,加速界不超过5。我们还提出了该算法的一个变体;它具有相同的时间复杂度和相同的加速范围,但具有更好的平均情况性能。我们在多达256个任务和256个处理器的随机生成系统上评估了这两种算法,并发现对于大多数系统,算法最多花费1秒,并且从未花费超过42秒来完成。此外,对于几乎100%的系统,如果给定的处理器的速度比满足必要的可行性测试所需的速度快两倍,后一种算法就会成功(因此算法的行为几乎就像加速界限是2一样)。这是在异构多处理器上调度并行实时任务的第一个工作,具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scheduling Constrained-Deadline Parallel Tasks on Two-type Heterogeneous Multiprocessors
Consider the problem of scheduling a taskset on a multiprocessor to meet all deadlines. Assume constrained-deadline sporadic tasks and stage-parallelism (i.e., a task comprises one or more stages with a stage comprising one or more segments so that segments in the same stage are allowed to execute in parallel and a segment is allowed to execute only if all segments of the previous stage have finished). Also assume a two-type heterogeneous multiprocessor platform (i.e., there are processors of two types, type-1 and type-2, and for each task, there is a specification of its execution speed on a type-1 processor and on a type-2 processor) and intra-type migration (i.e., a job can migrate between processors of the same type but for a task, all jobs of this task must execute on the same processor type). We present an algorithm for this problem; it assigns each task to a processor type and then schedules tasks on processors of each type with global-Earliest-Deadline-First. It has pseudo-polynomial time complexity and speedup bound at most 5. We also present a variant of this algorithm; it has the same time complexity and same speedup bound but has better average-case performance. We evaluate both algorithms on randomly-generated systems with up to 256 tasks and 256 processors and find that for most systems the algorithms took at most 1 second and they never took more than 42 seconds to finish. Also, for almost 100% of the systems, the latter algorithm succeeds if given processors twice faster than the speed needed to satisfy a necessary feasibility test (hence the algorithm behaves almost as if the speedup bound is two). This is the first work on scheduling parallel real-time tasks on a heterogeneous multiprocessor with provably good performance.
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