On the Optimality of Data Exchange for Master-Aided Edge Computing Systems

Edge computing has recently garnered significant interest in many Internet of Things (IoT) applications. However, the excessive overhead during data exchange still remains an open challenge, especially for large-scale data processing tasks. This paper considers a master-aided distributed computing system with multiple edge computing nodes and a master node, where the master node helps edge nodes compute output functions. We propose a coded scheme to reduce the communication latency by exploiting computation and communication capabilities of all nodes and creating coded multicast opportunities. More importantly, we prove that the proposed scheme is always optimal, i.e., achieving the minimum communication latency, for arbitrary computing and storage abilities at the master. This extends the previous optimality results in the extreme cases (either the master could compute all input files or compute nothing) to the general case. Finally, numerical results and TeraSort experiments demonstrate that our schemes can greatly reduce the communication latency compared with the existing schemes.