CAREER: Practical Coded Computation Mechanisms for Distributed Computing

自动驾驶汽车、无人机、机器人、智能手机、无线传感器、智能电表、健康监测设备等新兴物联网（IoT）产生了大量数据。这些数据预计将在许多时间敏感的物联网应用中进行实时处理，这对于现有的集中式云来说即使不是不可能，也是极具挑战性的。例如，自动驾驶汽车每英里产生大约10GB的数据。将如此庞大的数据从终端设备（如自动驾驶汽车）传输到集中式云并期望及时处理是不现实的，因为终端用户和集中式云之间的带宽有限。分布式计算系统可能是解决此问题的更好方法，在该系统中，计算密集型方面在终端设备上得到分布式和安全的处理，并可能得到边缘服务器（靠近终端设备）和云的帮助。在此背景下，该奖项研究了实用的分布式计算机制，该机制可以安全地获取异构资源，包括计算能力、存储、电池、网络资源等，分散在终端设备、边缘服务器和云上。这个项目代表了一个独特的尝试，探索机会，以及限制，编码计算的新理论，研究通过数据冗余的擦除和纠错码的设计，从未来分布式计算系统的实际角度。在一个关于分布式计算系统和边缘计算的未来的积极讨论的时代，这个项目开始理解编码计算如何适应这一图景。该项目的重点是(i)描述实际边缘计算系统的编码计算的成本效益权衡，并开发网络算法和协议，使编码计算框架适应边缘计算系统和资源的异构和动态特性，（ii）探索边缘分布式学习的编码计算，以降低通信成本并提供弹性、隐私和安全性。（iii）通过利用编码计算的延迟、冗余量、隐私和安全性之间的多重权衡，开发延迟敏感的编码计算。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A massive amount of data is generated with the emerging Internet of Things (IoT) including self-driving cars, drones, robots, smartphones, wireless sensors, smart meters, health monitoring devices. These data are expected to be processed in real-time in many time sensitive IoT applications, which is extremely challenging if not impossible with existing centralized cloud. For example, self-driving cars generate around 10GB of data per mile. Transmitting such massive data from end devices (such as self-driving cars) to the centralized cloud and expecting timely processing are not realistic with limited bandwidth between the end users and the centralized cloud. A distributed computing system, where computationally intensive aspects are distributively and securely processed at the end devices with possible help from edge servers (close to end-devices) and the cloud, might be a better approach to solving this problem. In this context, this award investigates practical distributed computing mechanisms that securely harvest heterogeneous resources including computing power, storage, battery, networking resources, etc., scattered across end devices, edge servers, and cloud.This project represents a unique attempt to explore the opportunities, as well as the limitations, of the new theory of coded computation, which studies the design of erasure and error-correcting codes through data redundancy, from a practical perspective in future distributed computing systems. In a time of active discussion about the future of distributed computing systems and edge computing, this project sets out to understand how coded computation fits into this picture. The focus of the project is on (i) characterizing the cost-benefit trade-offs of coded computation for practical edge computing systems, and developing networking algorithms and protocols to make the coded computation framework adaptive to heterogeneous and dynamic nature of edge computing systems and resources, (ii) exploring coded computation for distributed learning at the edge to reduce communication cost and provide resilience, privacy and security, and (iii) developing delay-sensitive coded computation by exploiting the multiple trade-offs among latency, amount of redundancy, privacy and security for coded computation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.