分布式训练异构任务调度算法研究

分布式机器学习中的工作结点在训练过程中经常需要处理异构任务,但任务发布者可能无法根据有效的先验知识确定边缘服务器集群中哪些是处于训练状态的工作结点.针对边缘服务器集群无法同时满足训练性能与服务质量最大化的问题,对异构任务调度算法进行了研究.首先在集群资源约束下分析了分布式训练收敛性能的影响因素;其次建立了最大化训练性能...

Scheduling of heterogeneous tasks for distributed training

Workers in distributed machine learning often need to deal with heterogeneous tasks during the training process. However, the task publisher may not be able to determine which workers in the cluster of edge server (ES) are currently in training based on effective prior knowledge. To tackle the problem that the ES cluster cannot fulfill the maximization of the training performance and the quality of service at the same time, a scheduling algorithm of heterogeneous tasks is proposed. Firstly, the factors influencing the convergence performance of distributed training are analyzed under the constraints about cluster’s resources. Secondly, the optimization objective for maximizing training performance is established. Finally, the optimization problem is transformed into a multidimensional multiple-choice knapsack problem. The simulation results show that the proposed scheduling algorithm of heterogeneous tasks can maximize the performance of distributed training and simultaneously ensure the quality of ser- vice.