DRL-Based Workload Allocation for Distributed Coded Machine Learning
Yi-Tong Zhou, Qiang Ye, Hui Huang · 2023
Over the past years, Distributed Machine Learning (DML) has been employed to tackle the high complexity problem with many Machine Learning (ML) algorithms. With DML, the original computation task involved in an ML algorithm is first split into multiple subtasks, which are forwarded to a group of computing devices in a distributed environment. Thereafter, the subtask results are collected in order to arrive at the final result for the original task. Despite the advantages of DML, the overall computation time can be seriously increased if some subtask results cannot be collected in a timely manner due to device malfunctions or network glitches. Recently, Distributed Coded Machine Learning (DCML) has been proposed to mitigate the problem with DML. Specifically, DCML employs coding techniques to inject redundancy into the original computation task. With the injected redundancy, DCML does not need to collect all subtask results to construct the result for the original task. So far, how to split the original task and thereafter assign an appropriate workload to each computing device in DCML has been a challenging problem. In this paper, we propose a novel work allocation scheme for DCML, DWA, to tackle the challenging problem. Our experimental results indicate that DWA outperforms the existing DCML schemes.