Distributed Model Training based on Data Parallelism in Edge Computing-enabled Elastic Optical Networks
Artikel i vetenskaplig tidskrift, 2020
IEEE The emergence of edge computing provides an effective solution to execute distributed model training (DMT). The deployment of training data among edge nodes affects the training efficiency and network resource usage. This letter aims for the efficient provisioning of DMT services by optimizing the partition and distribution of training data in edge computing-enabled optical networks. An integer linear programming (ILP) model and a data parallelism deployment algorithm (DPDA) are proposed to solve this problem. The performance of the proposed approaches is evaluated through simulation. Simulation results show that the proposed algorithm can deploy more DMT services compared with benchmark.
distributed model training
Training
Parallel processing
edge computing
Task analysis
Data parallelism
Training data
Data models
optical networks
Computational modeling
Optical fiber networks
Författare
Yajie Li
Beijing University of Posts and Telecommunications (BUPT)
Zebin Zeng
Beijing University of Posts and Telecommunications (BUPT)
Jun Li
Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk
Boyuan Yan
Beijing University of Posts and Telecommunications (BUPT)
Yongli Zhao
Beijing University of Posts and Telecommunications (BUPT)
Jie Zhang
Beijing University of Posts and Telecommunications (BUPT)
IEEE Communications Letters
1089-7798 (ISSN)
Vol. In PressÄmneskategorier
Datorteknik
Kommunikationssystem
Datavetenskap (datalogi)
DOI
10.1109/LCOMM.2020.3041453