Science Information Network (SINET) is a Japanese academic backbone network for more than 800 research institutions and universities. In this paper, we present a multi-GPU-driven pipeline for handling huge session data of SINET. Our pipeline consists of ELK stack, multi-GPU server, and Splunk. A multi-GPU server is responsible for two procedures: discrimination and histogramming. Discrimination is dividing session data into ingoing/outgoing with subnet mask calculation and network address matching. Histogramming is grouping ingoing/outgoing session data into bins with map-reduce. In our architecture, we use GPU for the acceleration of ingress/egress discrimination of session data. Also, we use a tiling design pattern for building a two-stage map-reduce of CPU and GPU. Our multi-GPU-driven pipeline has succeeded in processing huge workloads of about 1.2 to 1.6 billion session streams (500GB-650GB) within 24 hours.
@article{arxiv.2106.12863,
title = {Choice of Parallelism: Multi-GPU Driven Pipeline for Huge Academic Backbone Network},
author = {Ruo Ando and Youki Kadobayashi and Hiroki Takakura},
journal= {arXiv preprint arXiv:2106.12863},
year = {2021}
}
Comments
This is an Accepted Manuscript of an article published by Taylor & Francis Group in the International Journal of Parallel, Emergent & Distributed Systems on 24/06/2021 av lable online: http://www.tandfonline.com/ DOI: 10.1080/17445760.2021.1941009