CAREER: Practical Coded Computation Mechanisms for Distributed Computing

新兴的物联网（IoT）产生了大量数据，包括自动驾驶汽车、无人机、机器人、智能手机、无线传感器、智能仪表、健康监测设备。这些数据预计将在许多时间敏感的物联网应用中实时处理，这对于现有的集中式云来说即使不是不可能的，也是极具挑战性的。例如，自动驾驶汽车每英里产生约10GB的数据。从终端设备（如自动驾驶汽车）向集中式云传输如此海量的数据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，其中计算密集型方面在终端设备上分布式和安全地处理，并可能得到边缘服务器（靠近终端设备）和云的帮助，可能是解决这个问题的更好方法。在此背景下，该奖项研究了实用的分布式计算机制，这些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，该项目代表了一种独特的尝试，探索编码计算新理论的机会以及局限性，该理论从未来分布式计算系统的实际角度研究通过数据冗余设计擦除和纠错码。在积极讨论分布式计算系统和边缘计算的未来的时候，这个项目开始了解编码计算如何适应这一情况。该项目的重点是（i）描述实际边缘计算系统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性和动态性，（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，以及（iii）开发延迟敏感的编码计算，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。该奖项反映了NSF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.