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Mahdi Abbasi

Mahdi Abbasi

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 54902628100
HIndex:
Faculty: Faculty of Engineering
Address:
Phone: 09183176343

Research

Title
Efficient pipelined flow classification for intelligent data processing in IoT
Type
JournalPaper
Keywords
Efficiency Intelligent flow processing IoT Packet classification Pipeline
Year
2022
Journal Digital Communications and Networks
DOI
Researchers ، Fengping Chen ، Mahdi Abbasi ، Mohammad Reza Khosravi ، Milad Rafiee

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. The 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 reduction in memory usage as well as 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.