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Cloud training platforms, such as Amazon Web Services and Huawei Cloudprovide users with computational resources to train their deep learning jobs.Elastic training is a service embedded in cloud training platforms thatdynamically scales up or down the resources allocated to a job. The coretechnique of an elastic training system is to best allocate limited resourcesamong heterogeneous jobs in terms of shorter queueing delay and higher trainingefficiency. This paper presents an optimal resource allocator for elastictraining system that leverages a mixed-integer programming (MIP) model tomaximize the training progress of deep learning jobs. We take advantage of thereal-world job data obtained from ModelArts, the deep learning trainingplatform of Huawei Cloud and conduct simulation experiments to compare theoptimal resource allocator with a greedy one as benchmark. Numerical resultsshow that the proposed allocator can reduce queuing time by up to 32% andaccelerate training efficiency by up to 24% relative to the greedy resourceallocator, thereby greatly improving user experience with Huawei ModelArts andpotentially enabling the realization of higher profits for the product. Also,the optimal resource allocator is fast in decision-making, taking merely 0.4seconds on average.

An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud