CAREER: D3: Addressing Emerging Data-Induced Challenges in Embedded and Real-Time Systems

数据驱动的嵌入式系统就在这里。创建处理大量实时传感器捕获数据的算法的能力使许多行业的设计人员能够开发智能嵌入式系统，使操作和决策自动化，例如自动驾驶汽车。这种新兴的数据密集型嵌入式计算范式带来了一系列由数据引起的新挑战，这些挑战围绕着保证时间可预测性和使延迟约束能够在设计时进行分析验证。本研究的目标是克服嵌入式系统中海量数据的实时处理所带来的挑战，以保证时间的可预测性。D3是一个全面的资源管理生态系统，具有可预测地处理大量实时数据密集型工作负载的能力，在操作系统中实现。D3带来了一套新颖的基本系统级技术，可以实现智能数据过滤和表征，透明和监督流执行并发优化，以及在异构架构下可预测的内存管理。由于协同调度内存和高度异构的计算资源，诸如对时间可预测性的分析保证变得可量化，因此将解决算法方面的困难挑战。开发一个全面的异构资源管理生态系统，以可预测地处理大量实时数据密集型工作负载，对于许多应用领域（如交通和机器人）来说将是一个重要的结果。下一代汽车系统就是一个很好的例子，可以从拟议的研究中受益匪浅。该项目的成果将为基于异构平台的安全关键型自动驾驶汽车的可认证性铺平道路。一个创新的本科研究项目，开发一种教育工具，使用实时数据驱动的系统设计概念作为其基础，帮助实现教育目标。所有源代码和评估数据都可以在开源Gnu公共许可证下，从德克萨斯大学达拉斯分校（UTD）计算机科学系维护的公共网络服务器上免费直接下载。其他案例研究测试程序和脚本将在开源BSD许可下免费提供，并可直接从UTD计算机科学系维护的公共web服务器上下载。在这个项目中产生的所有数据产品也将存档于UTD计算机科学计算储存库，以便永久储存。该存储库可从www.utdallas.edu/~cong/CareerRepo获得。

Data-driven embedded systems are here. The ability to create algorithms that process massive amounts of real-time sensor-captured data is enabling designers in many industries to develop intelligent embedded systems that automate actions and decisions, e.g., self-driving vehicles. This emerging data-intensive embedded computing paradigm brings a new set of data-induced challenges around guaranteeing timing predictability and enabling latency constraints to be analytically validated at design time. The goal of this research is to overcome challenges due to real-time processing of massive data in embedded systems in order to guarantee timing predictability. D3, a comprehensive resource management ecosystem with the capability of predictably processing massive real-time data-intensive workloads, is implemented in the operating system. D3 brings in a novel set of fundamental system-level techniques, which enable smart data filtering and characterization, transparent and supervised streaming for execution concurrency optimization, and predictable memory management under heterogeneous architectures. The hard algorithmic challenges due to co-scheduling memory and highly heterogeneous computing resources such that analytical guarantees on timing predictability become quantifiable will be addressed. Developing a comprehensive heterogeneous resource management ecosystem for predictably processing massive real-time data-intensive workloads would be a significant result for many application domains, such as transportation and robotics. The next-generation automotive system is a good example that could greatly benefit from the proposed research. The outcome of this project will pave the way to certifiability of safety-critical autonomous vehicles based on heterogeneous platforms. An innovative undergraduate research project to develop an educational tool that uses real-time data-driven system design concepts as its foundation assists in realizing the educational objectives. All source code and evaluation data will be freely available for direct download from public web servers maintained by the University of Texas at Dallas (UTD) Computer Science Department under an open source Gnu Public License. Additional case-study test programs and scripts will be freely available under the open source BSD license and directly downloadable from public web servers maintained by the UTD Computer Science Department. All data products produced in this project will also be archived in the UTD computer science computing repository for permanent storage. The repository is available at www.utdallas.edu/~cong/CareerRepo.