Efficient scheduling of tasks in cyber-physical systems or internet-of-things is a challenging prospect primarily due to their demands to meet critical performance goals in the face of stringent resource constraints. In addition, to enhance ease of implementation and more efficient usage of resources, these schedulers are many-a-times restricted to be non-preemptive, where jobs once started must be continuously executed until completion. In this work, we address the following resource allocation issue. Given, (i) a set of functionalities (tasks) whose performance qualities are directly proportional to the rates at which they receive service from a resource, and (ii) a discrete set of allowable alternative execution rates for each task, the objective is to determine a non-preemptive execution schedule for the tasks with appropriately chosen rates over time, such that the performance of the overall system combining all functionalities, is optimized. In this work, performance of the system is considered to be directly proportional to the time taken to re-stabilize all functionalities within stipulated thresholds, subsequent to reconfigurations in the desired outputs of a subset of these functionalities. We first propose branch and bound based techniques for determining optimal schedules under different restrictions on the adaptability of execution rates for the tasks. However, although optimal, branch and bound based solutions incur significant computational overheads, which often make them prohibitively expensive towards online application, especially for large task-set sizes. Hence, we further propose two fast and efficient heuristic strategies to quickly obtain near optimal schedules. Experimental results show that the proposed schemes are able to achieve significant performance gain, 30–55% in case of optimal strategy and 10–50% for heuristic methods compared to traditional fixed rate execution mode.

中文翻译：

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控制计划协同设计，可在面临重复配置的实时系统中实现快速稳定

网络物理系统或物联网中任务的高效调度是一个具有挑战性的前景，这主要是由于面对严格的资源限制，任务需要满足关键的性能目标。另外，为了提高实现的便利性和资源的更有效利用，这些调度程序多次被限制为非抢先式，其中一旦启动的作业就必须连续执行直到完成。在这项工作中，我们解决了以下资源分配问题。鉴于（i）一组功能（任务）的性能质量与它们从资源中接收服务的速率成正比，并且（ii）每个任务的一组离散的可允许执行速率，目的是确定用于与适当选择率随着时间的推移，使得整个系统相结合的所有功能的性能，优化的任务的非抢占执行调度。在这项工作中，在重新配置这些功能的子集的所需输出之后，系统的性能被认为与将所有功能重新稳定在规定阈值内所花费的时间成正比。我们首先提出基于分支和边界的技术，用于在任务执行率适应性的不同限制下确定最佳计划。但是，尽管基于分支和边界的最佳解决方案会产生大量的计算开销，但这通常使它们在在线应用程序方面的成本过高，尤其是对于大型任务集。因此，我们进一步提出了两种快速有效的启发式策略来快速获得接近最优的时间表。实验结果表明，与传统的固定速率执行模式相比，所提出的方案能够实现显着的性能提升，在采用最佳策略的情况下达到30-55％，对于启发式方法则达到10-50％。