Continuous demands for higher performance and reliability within stringent resource budgets is driving a shift from homogeneous to heterogeneous processing platforms for the implementation of today’s cyber-physical systems (CPSs). These CPSs are typically represented as Directed-acyclic Task Graph (DTG) due to the complex interactions between their functional components that are often distributed in nature. In this article, we consider the problem of scheduling a real-time application modelled as a single DTG, where tasks may have multiple implementations designated as quality-levels, with higher quality-levels producing more accurate results and contributing to higher rewards/Quality-of-Service for the system. First, we introduce an optimal solution using Integer Linear Programming (ILP) for a DTG with multiple quality-levels, to be executed on a heterogeneous distributed platform . However, this ILP-based optimal solution exhibits high computational complexity and does not scale for moderately large problem sizes. Hence, we propose two low-overhead heuristic algorithms called Global Slack Aware Quality-level Allocator ( G-SLAQA ) and Total Slack Aware Quality-level Allocator ( T-SLAQA ), which are able to produce satisfactorily efficient as well as fast solutions within a reasonable time. G-SLAQA , the baseline heuristic, is greedier and faster than its counter-part T-SLAQA , whose performance is at least as efficient as G-SLAQA . The efficiency of all the proposed schemes have been extensively evaluated through simulation-based experiments using benchmark and randomly generated DTGs. Through the case study of a real-world automotive traction controller , we generate schedules using our proposed schemes to demonstrate their practical applicability.

中文翻译：

---

服务质量保证

在严格的资源预算内对更高性能和可靠性的持续需求正在推动从同质处理平台向异构处理平台的转变，以实现当今的网络物理系统 (CPS)。这些 CPS 通常表示为有向无环任务图（DTG）由于它们的功能组件之间通常分布在自然界中的复杂相互作用。在本文中，我们考虑调度建模为单个 DTG 的实时应用程序的问题，其中任务可能具有指定为质量级别的多个实现，更高的质量级别产生更准确的结果并有助于更高的奖励/质量-系统的服务。首先，我们引入一个最优解用于具有多个质量级别的 DTG 的整数线性规划 (ILP)，将在异构分布式平台上执行. 然而，这种基于 ILP 的最优解决方案具有很高的计算复杂性，并且不适用于中等规模的问题。因此，我们提出了两种低开销启发式算法，称为全局 Slack 感知质量级别分配器(G-SLAQA） 和Total Slack 感知质量级别分配器(T-SLAQA)，它们能够在合理的时间内产生令人满意的高效和快速的解决方案。G-SLAQA，基线启发式，比它的对应部分更贪婪和更快T-SLAQA，其性能至少与G-SLAQA. 所有提出的方案的效率都通过使用基准和随机生成的 DTG 的基于模拟的实验进行了广泛的评估。通过真实世界的案例研究汽车牵引力控制器，我们使用我们提出的方案生成时间表以证明其实际适用性。