A Machine Learning-Based Framework to Estimate the Lifetime of Network Line Cards

被引：3

作者：

Herrera, Juan Luis ^{[1
]}

Polverini, Marco ^{[2
]}

Galan-Jimenez, Jaime ^{[1
]}

机构：

[1] Univ Extremadura, Dept Comp Syst & Telemat Engn, Badajoz, Spain

[2] Univ Rome Sapienza, DIET Dept, Rome, Italy

来源：

NOMS 2020 - PROCEEDINGS OF THE 2020 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2020: MANAGEMENT IN THE AGE OF SOFTWARIZATION AND ARTIFICIAL INTELLIGENCE | 2020年

关键词：

Machine Learning; line card; lifetime; estimation; QoS;

D O I：

10.1109/noms47738.2020.9110455

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the increasing tendency on data rates in forthcoming communication networks, availability is a crucial aspect to guarantee Quality of Service (QoS) requirements. The possibility of predicting the lifetime of networking hardware can be a key to improve the overall network QoS. This paper proposes a generic Machine Learning (ML) based framework that learns how to mimic the mathematical model behind the lifetime of network line cards. Results show that a good precision (85%) and recall (close to 100%) on the estimation can be achieved regardless the type of line cards the network is composed of.

引用

页数：5

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