The purpose of this study is to test the effectiveness of current straggler
mitigation techniques over different important iterative convergent machine
learning(ML) algorithm including Matrix Factorization (MF), Multinomial
Logistic Regression (MLR), and Latent Dirichlet Allocation (LDA) . The
experiment was conducted to implemented using the FlexPS system, which is the
latest system implementation that employ parameter server architecture. The
experiment employed the Bulk Synchronous Parallel (BSP) computational model to
examine the straggler problem in Parameter Server on Iterative Convergent
Distributed Machine Learning. Moreover, the current research analyzes the
experimental arrangement of the parameter server strategy concerning the
parallel learning problems by injecting universal straggler patterns and
executing latest mitigation techniques. The findings of the study are
significant in that as they will provide the necessary platform for conducting
further research into the problem and allow the researcher to compare different
methods for various applications. The outcome is therefore expected to
facilitate the development of new techniques coupled with new perspectives in
addressing this problem.

当前研究旨在测试当前延迟问题缓解技术在不同重要的迭代收敛机器学习算法（包括矩阵分解、多项逻辑回归和潜在狄利克雷分布）上的有效性，并通过使用 FlexPS 系统实现了实验，该系统采用参数服务器架构进行最新的系统实现。本实验采用批同步并行计算模型来研究迭代收敛分布式机器学习中参数服务器上的延迟问题。此外，本研究还分析了参数服务器策略在并行学习问题上的实验安排，通过注入通用延迟模式并执行最新的缓解技术。该研究的发现具有重要意义，因为它们为进一步研究该问题提供了必要的平台，并允许研究人员比较不同的方法用于各种应用。因此，预计该研究结果将促进新技术的发展，并以新的视角解决这一问题。