Paper Info
Title
Efficient pipelined flow classification for intelligent data processing in IoT
Abstract
The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things (IoT). In recent years, researchers have tried to develop hardware-based solutions for the classification of Internet packets. Due to higher throughput and shorter delays, these solutions are considered as a major key to improving the quality of services. Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput. The proposed architectures, however, cannot reach a compromise among power consumption, memory usage, and throughput rate. In view of this, the architecture proposed in this paper contains a pipeline-based micro-core that is used in network processors to classify packets. To this end, three architectures have been implemented using the proposed micro-core. The first architecture performs parallel classification based on header fields. The second one classifies packets in a serial manner. The last architecture is the pipeline-based classifier, which can increase performance by nine times. The proposed architectures have been implemented on an FPGA chip. The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput. The architecture has a power consumption of is 1.294w, and its throughput with a frequency of 233 ​MHz exceeds 147 Gbps.
Year
DOI
Venue
2022
10.1016/j.dcan.2022.04.010
Digital Communications and Networks
Keywords
DocType
Volume
Efficiency,Intelligent flow processing,IoT,Packet classification,Pipeline
Journal
8
Issue
ISSN
Citations 
4
2352-8648
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Seyed Navid Mousavi100.34
Fengping Chen200.34
Mahdi Abbasi341.42
Mohammad R. Khosravi4267.55
Milad Rafiee500.34