Coded Edge Computing
Coded Edge Computing
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DOI：
10.1109/infocom41043.2020.9155226
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Kwang Taik Kim;Carlee Joe-Wong;M. Chiang
中科院分区：
文献类型：
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作者：
Kwang Taik Kim;Carlee Joe-Wong;M. Chiang
在第五代边缘设备移动的网络上运行密集型计算任务会带来分布式计算挑战：边缘设备在计算、存储和通信能力方面是异构的;并且可能会出现不可预测的离散效应和故障。在这项工作中，我们提出了一种纠错码启发的策略，以在边缘计算环境中执行计算任务，该策略旨在减轻响应时间的变化和边缘设备的异构性和缺乏可靠性造成的错误。与以前的编码方法不同，我们将部分未完成的编码任务纳入我们的计算恢复中，这使我们能够在编码任务在具有固定截止日期的边缘设备上运行时，以低复杂度解码实现平滑的性能下降。通过在边缘设备和主节点上进一步进行编码，所提出的计算方案还消除了数据洗牌期间的通信瓶颈，并且适合于在高度可变和有限的网络中的分布式实现。这种分布式编码迫使我们解决新的解码挑战。使用基于联邦多任务学习框架的代表性实现，进行了广泛的性能模拟，这表明所提出的策略在延迟和准确性方面比传统的编码计算方案有显著的提高。
Running intensive compute tasks across the fifth generation mobile network of edge devices introduces distributed computing challenges: edge devices are heterogeneous in the compute, storage, and communication capabilities; and can exhibit unpredictable straggler effects and failures. In this work, we propose an error-correcting-code inspired strategy to execute computing tasks in edge computing environments, which is designed to mitigate variability in response times and errors caused by edge devices’ heterogeneity and lack of reliability. Unlike prior coding approaches, we incorporate partially unfinished coded tasks into our computation recovery, which allows us to achieve smooth performance degradation with low-complexity decoding when the coded tasks are run on edge devices with a fixed deadline. By further carrying out coding on edge devices as well as a master node, the proposed computing scheme also alleviates communication bottlenecks during data shuffling and is amenable to distributed implementation in a highly variable and limited network. Such distributed encoding forces us to solve new decoding challenges. Using a representative implementation based on federated multi-task learning frameworks, extensive performance simulations are carried out, which demonstrate that the proposed strategy offers significant gains in latency and accuracy over conventional coded computing schemes.