Department
Mathematical, Computing & Information Sciences
Document Type
Article
Publication Date
2021
Abstract
The Simple Network Management Protocol (SNMP) is one of the dominant protocols for network monitoring and configuration. The first two versions of SNMP (v1 and v2c) use the Community-based Security Model (CSM), where the community is transferred in clear text, resulting in a low level of security. With the release of SNMPv3, the User-based Security Model (USM) and Transport Security Model (TSM) were proposed, with strong authentication and privacy at different levels. The Raspberry Pi family of Single-Board Computers (SBCs) is widely used for many applications. To help their integration into network management systems, it is essential to study the impact of the different versions and security models of SNMP on these SBCs. In this work, we carried out a performance analysis of SNMP agents running in three different Raspberry Pis (Pi Zero W, Pi 3 Model B, and Pi 3 Model B+). Our comparisons are based on the response time, defined as the time required to complete a request/response exchange between a manager and an agent. Since we did not find an adequate tool for our assessments, we developed our own benchmarking tool. We did numerous experiments, varying different parameters such as the type of requests, the number of objects involved per request, the security levels of SNMPv3/USM, the authentication and privacy protocols of SNMPv3/USM, the transport protocols, and the versions and security models of SNMP. Our experiments were executed with Net-SNMP, an open-source and comprehensive distribution of SNMP. Our tests indicate that SNMPv1 and SNMPv2c have similar performance. SNMPv3 has a longer response time, due to the overhead caused by the security services (authentication and privacy). The Pi 3 Model B and Pi 3 Model B+ have comparable performance, and significantly outperform the Pi Zero W.
Recommended Citation
Gamess, E., & Hernandez, S. (2021). Performance Evaluation of SNMPv1/2c/3 using Different Security Models on Raspberry Pi. International Journal of Advanced Computer Science and Applications, Vol. 12 (11), 1-18.
Publication/Presentation Information
International Journal of Advanced Computer Science and Applications, Vol. 12 (11), 2021, 1-18.