Optimal Task Allocation and Coding Design for Secure Edge Computing With Heterogeneous Edge Devices
Optimal Task Allocation and Coding Design for Secure Edge Computing With Heterogeneous Edge Devices
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DOI：
10.1109/tcc.2021.3050012
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发表时间：
2021-01
影响因子：
6.5
通讯作者：
Jin Wang;Chunming Cao;Jianping Wang;K. Lu;A. Jukan;Wei Zhao
中科院分区：
文献类型：
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作者：
Jin Wang;Chunming Cao;Jianping Wang;K. Lu;A. Jukan;Wei Zhao
近年来，边缘计算引起了人们的极大关注，因为它可以有效地支持许多延迟敏感的应用。尽管有这样一个突出的特点，边缘计算也面临着许多挑战，特别是在效率和安全性方面，因为边缘设备通常是异构的，可能是不可信的。为了应对这些挑战，我们提出了一个统一的框架，以提供效率和保密性的编码分布式计算。在所提出的框架内，我们使用矩阵乘法，许多分布式机器学习算法的基本构建块，作为代表性的计算任务。为了在实现信息理论安全的同时最大限度地减少资源消耗，我们研究了两个高度耦合的问题：（1）任务分配，将计算任务中的数据块分配给边缘设备;（2）线性代码设计，通过使用随机信息对原始数据进行编码来生成数据块。具体来说，我们首先从理论上分析了最优解的必要条件。在理论分析的基础上，我们提出了一种有效的任务分配算法，以获得一组选定的边缘设备和分配给它们的编码向量的数量。利用任务分配的结果，设计了两种情况下的安全编码计算方案：（1）有冗余计算和（2）无冗余计算，均满足可用性和安全性条件。此外，我们还从理论上分析了所提出的方案的优化。最后，我们进行了大量的仿真实验，以证明所提出的计划的有效性。
In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challenges, we propose a unified framework to provide efficiency and confidentiality by coded distributed computing. Within the proposed framework, we use matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task. To minimize resource consumption while achieving information-theoretic security, we investigate two highly-coupled problems, (1) task allocation that assigns data blocks in a computing task to edge devices and (2) linear code design that generates data blocks by encoding the original data with random information. Specifically, we first theoretically analyze the necessary conditions for the optimal solution. Based on the theoretical analysis, we develop an efficient task allocation algorithm to obtain a set of selected edge devices and the number of coded vectors allocated to them. Using the task allocation results, we then design secure coded computing schemes, for two cases, (1) with redundant computation and (2) without redundant computation, all of which satisfy the availability and security conditions. Moreover, we also theoretically analyze the optimization of the proposed scheme. Finally, we conduct extensive simulation experiments to demonstrate the effectiveness of the proposed schemes.