On Performance and Scalability of Cost-Effective SNMP Managers for Large-Scale Polling
EntityUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
PublisherInstitute of Electrical and Electronics Engineers Inc. (IEEE)
10.1109/ACCESS.2021.3049310IEEE Access 9 (2021): 7374-7383
Funded byThis work was supported by Ayudas para la formación de doctores en empresas, Doctorados Industriales, under Grant DI-16-08979
SubjectsAdaptive polling; Networking; Parallelism; SNMP; Telecomunicaciones
Rights© The author(s)
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
As networks grow larger in size and complexity, their monitoring is becoming an increasing challenge because of the required polling performance and also due to heterogeneity of devices. As it turns out, SNMP (Simple Network Management Protocol) is by far the most popular monitoring protocol. However, due to the increase in the number of network devices, it becomes necessary to employ multiple SNMP managers, which is not cost-effective due to the hardware requirements. Additionally, the different proprietary SNMP implementations require custom configuration very often, as new devices are being incorporated into the network. Therefore, current SNMP managers not only require capabilities for large-scale monitoring but also a high degree of flexibility and programmability. In response, we propose an SNMP manager architecture with a flexible multi-threaded architecture, which effectively reduces the hardware resources necessary to poll the increasing number of SNMP agents. In addition, it features a scripting component to deal with the different data representations caused by proprietary implementations. Our experience has shown that SNMP agents can have high variability in their response times. Actually, our findings show a strong correlation between high response times and CPU load. As a solution, we propose and analyze novel adaptive polling algorithms that decrease the load on agents' CPUs while keeping the desired polling rate for fast agents. Finally, we present several real-world use cases where we show the benefits of the polling algorithms and the scripting component, by means of extensive measurement campaigns
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