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CAREER: Practical Coded Computation Mechanisms for Distributed Computing

职业：分布式计算的实用编码计算机制

基本信息

批准号：
1942878
负责人：
Hulya Seferoglu
金额：
$ 50.43万
依托单位：
University of Illinois at Chicago
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942878&HistoricalAwards=false
关键词：
CAREER Practical Coded Computation Mechanisms

项目摘要

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.

新兴的物联网(IoT)产生了海量数据，包括自动驾驶汽车、无人机、机器人、智能手机、无线传感器、智能电表、健康监测设备。这些数据预计将在许多时间敏感型物联网应用中实时处理，这在现有的集中式云中即使不是不可能，也是极具挑战性的。例如，自动驾驶汽车每英里产生约10 GB的数据。将如此海量的数据从终端设备(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，因为最终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这一问题的更好方法，在这种系统中，计算密集型方面在终端设备上进行分布式和安全地处理，并可能得到边缘服务器(接近终端设备)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，该机制安全地获取分散在终端设备、边缘服务器和云中的计算能力、存储、电池、网络资源等异类资源。该项目代表了一种独特的尝试，从未来分布式计算系统的实用角度探索编码计算的新理论的机会和局限性，该理论研究通过数据冗余来设计擦除和纠错码。在对分布式计算系统和边缘计算的未来进行积极讨论的时候，这个项目开始了解编码计算如何适应这种情况。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-收益权衡，并开发网络算法和协议以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特性；(Ii)探索用于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)通过利用延迟、冗余量、编码计算的隐私和安全。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Adaptive and Resilient Model-Distributed Inference in Edge Computing Systems

DOI：
10.1109/ojcoms.2023.3280174
发表时间：
2023
期刊：
IEEE Open Journal of the Communications Society
影响因子：
7.9
作者：
Pengzhen Li;Erdem Koyuncu;H. Seferoglu
通讯作者：
Pengzhen Li;Erdem Koyuncu;H. Seferoglu

Adaptive Coding for Matrix Multiplication at Edge Networks

边缘网络矩阵乘法的自适应编码

DOI：
10.1109/isit45174.2021.9517801
发表时间：
2021
期刊：
2021 IEEE International Symposium on Information Theory (ISIT
影响因子：
0
作者：
Vedadi, Elahe;Seferoglu, Hulya
通讯作者：
Seferoglu, Hulya

PolyDot Coded Privacy Preserving Multi-Party Computation at the Edge

DOI：
10.1109/spawc51304.2022.9833997
发表时间：
2021-06
期刊：
2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)
影响因子：
0
作者：
Elahe Vedadi;Yasaman Keshtkarjahromi;H. Seferoglu
通讯作者：
Elahe Vedadi;Yasaman Keshtkarjahromi;H. Seferoglu

Model-Distributed Inference in Multi-Source Edge Networks

多源边缘网络中的模型分布式推理