@article{Niu2021, 
author = {Songjie Niu and Shimin Chen},
title = {TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance},
year = {2021},
journal = {Journal of Computer Science and Technology},
volume = {36},
number = {4},
pages = {778-791},
keywords = {deep learning, transfer learning, performance modeling, distributed graph computation},
url = {https://www.sciopen.com/article/10.1007/s11390-021-1356-2},
doi = {10.1007/s11390-021-1356-2},
abstract = {It is challenging to model the performance of distributed graph computation. Explicit formulation cannot easily capture the diversified factors and complex interactions in the system. Statistical learning methods require a large number of training samples to generate an accurate prediction model. However, it is time-consuming to run the required graph computation tests to obtain the training samples. In this paper, we propose TransGPerf, a transfer learning based solution that can exploit prior knowledge from a source scenario and utilize a manageable amount of training data for modeling the performance of a target graph computation scenario. Experimental results show that our proposed method is capable of generating accurate models for a wide range of graph computation tasks on PowerGraph and GraphX. It outperforms transfer learning methods proposed for other applications in the literature.}
}