CAREER: Practical Coded Computation Mechanisms for Distributed Computing

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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.