Name
Abstract | ||
|---|---|---|
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 1.2-1.6 billion session streams (500-650 GB) within 24 hours.[GRAPHICS]. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1080/17445760.2021.1941009 | INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS |
Keywords | DocType | Volume |
Multi-GPU, streaming data pipeline, massive session data, map reduce, tiling pattern | Journal | 36 |
Issue | ISSN | Citations |
6 | 1744-5760 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruo Ando | 1 | 0 | 0.68 |
| Youki Kadobayashi | 2 | 0 | 0.34 |
| Hiroki Takakura | 3 | 0 | 0.34 |