Driven by recent advances in networking and computing technologies, distributed application scenarios are increasingly deployed on resource-constrained processing platforms. This includes networked embedded and cyber-physical systems as well as edge computing in mobile applications and the Internet of Things (IoT). In such resource-constrained Networks-of-Systems (NoS), computation and communication workloads need to be carefully co-optimized yet are tightly coupled. How to optimally partition and schedule application tasks among an appropriately designed NoS architecture requires a simultaneous consideration of design parameters from applications and processing platforms all the way to network configurations. Traditionally, however, systems and networks are designed in isolation and combined in an ad hoc manner, which ignores joint effects and optimization opportunities. To systematically explore and optimize NoS design spaces, a higher level of design abstraction on top of traditional system and network design is required. In this article, we propose a novel network-level design methodology for resource-constrained NoS optimization and design space exploration. A key component in such a design flow is fast yet accurate network/system co-simulation to rapidly evaluate NoS parameters with high fidelity. We first introduce a novel NoS simulator (NoSSim) that integrates source-level simulation models of applications with a host-compiled system simulation platform and a reconfigurable network simulation backplane to accurately capture system and network interactions. The co-simulation platform is further combined with model generation tools and a multi-objective genetic search algorithm to provide a comprehensive and fully automated NoS design space exploration framework. Finally, we apply our network-level design flow on several state-of-art IoT/mobile design case studies. Results show that NoSSim can achieve more than 86% simulation accuracy on average as compared to a real-world edge device cluster, where sensitivities to various design parameters are faithfully captured with high fidelity. When applying our network-level design space exploration methodology, design decisions are automatically optimized, where non-obvious NoS configurations are discovered outperforming manually designed solutions by more than 45%.

中文翻译：

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资源受限系统网络的网络级设计空间探索

在网络和计算技术的最新进展的推动下，分布式应用场景越来越多地部署在资源受限的处理平台上。这包括联网嵌入式和网络物理系统以及移动应用程序和物联网 (IoT) 中的边缘计算。在这种资源受限的系统网络 (NoS) 中，计算和通信工作负载需要仔细地协同优化，同时又要紧密耦合。如何在适当设计的 NoS 架构中优化划分和调度应用程序任务需要同时考虑从应用程序和处理平台一直到网络配置的设计参数。然而，传统上，系统和网络是孤立地设计的，并以自组织的方式组合在一起，这忽略了联合效应和优化机会。为了系统地探索和优化 NoS 设计空间，需要在传统系统和网络设计之上进行更高层次的设计抽象。在本文中，我们提出了一种新颖的网络级设计方法，用于资源受限的 NoS 优化和设计空间探索。这种设计流程中的一个关键组成部分是快速而准确的网络/系统协同仿真，以快速评估高保真度的 NoS 参数。我们首先介绍了一种新颖的 NoS 模拟器 (NoSSim)，它将应用程序的源级仿真模型与主机编译的系统仿真平台和可重新配置的网络仿真背板集成在一起，以准确捕获系统和网络交互。协同仿真平台进一步结合模型生成工具和多目标遗传搜索算法，提供全面、全自动的NoS设计空间探索框架。最后，我们将我们的网络级设计流程应用于几个最先进的物联网/移动设计案例研究。结果表明，与真实世界的边缘设备集群相比，NoSSim 平均可以实现超过 86% 的仿真精度，其中对各种设计参数的敏感性以高保真度忠实地捕获。应用我们的网络级设计空间探索方法时，会自动优化设计决策，其中发现的非显而易见的 NoS 配置比手动设计的解决方案高出 45% 以上。