Improving Energy Efficiency in NFV Clouds with Machine Learning

被引：6

作者：

Zorello, Ligia M. M. ^{[1
]}

Vieira, Migyael G. T. ^{[1
]}

Tejos, Rodrigo A. G. ^{[1
]}

Rojas, Marco A. T. ^{[2
]}

Meirosu, Catalin ^{[3
]}

Carvalho, Tereza C. M. B. ^{[1
]}

机构：

[1] Univ Sao Paulo, Escola Politecn, Sao Paulo, Brazil

[2] Inst Fed Santa Catarina, Cacador, Brazil

[3] Ericsson, Stockholm, Sweden

来源：

PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD) | 2018年

关键词：

energy efficiency; NFV; machine learning; Dynamic Voltage and Frequency Scaling;

D O I：

10.1109/CLOUD.2018.00097

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Widespread deployments of Network Function Virtualization (NFV) technology will replace many physical appliances in telecommunication networks with software executed on cloud platforms. Setting compute servers continuously to high-performance operating modes is a common NFV approach for achieving predictable operations. However, this has the effect that large amounts of energy are consumed even when little traffic needs to be forwarded. The Dynamic Voltage-Frequency Scaling (DVFS) technology available in Intel processors is a known option for adapting the power consumption to the workload, but it is not optimized for network traffic processing workloads. We developed a novel control method for DVFS, based observing the ongoing traffic and online predictions using machine learning. Our results show that we can save up to 27% compared to commodity DVFS, even when including the computational overhead of machine learning.

引用

页码：710 / 717

页数：8

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