# Energy Consumption of IT System in Cloud Data Center: Architecture, Factors and Prediction

Haowei Lin, Xiaolong Xu, Xinheng Wang

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Haowei Lin, Xiaolong Xu, Xinheng Wang. Energy Consumption of IT System in Cloud Data Center: Architecture, Factors and Prediction. 16th IFIP International Conference on Network and Parallel Computing (NPC), Aug 2019, Hohhot, China. pp.311-315, 10.1007/978-3-030-30709-7_25. hal-03770559

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International Federation for Information Processing

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manuscript.
Energy Consumption of IT System in Cloud
Data Center: Architecture, Factors and
Prediction*

Haowei Lin¹, Xiaolong Xu¹, and Xinheng Wang²

1. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
   1218043318@njupt.edu.cn, xuxl@njupt.edu.cn
2. University of West London, London W55RF, UK
   Henry.Wang@uwl.ac.uk

**Abstract.** In recent years, as cloud data centers has grown constantly in size and quantity, the energy consumption of cloud data centers has increased dramatically. Therefore, it is of great significance to study the energy-saving issues of cloud data centers in depth. Therefore, this paper analyzes the architecture of energy consumption of IT system in cloud data centers and proposes a new framework for collecting energy consumption. Based on this framework, the factors affecting energy consumption are studied, and various parameters closely related to energy consumption are selected. Finally, the RBF neural network is used to model and predict the energy consumption of the cloud data centers, which is aim to prove the accuracy of the framework for collecting energy consumption and influencing factors. The experimental results show that these parameters under the framework for collecting energy consumption have better accuracy and adaptability to the prediction of energy consumption in cloud data centers than the previous model of energy consumption prediction.

**Keywords:** cloud computing · cloud data center · energy consumption · prediction · architecture.

# 1 Introduction

In recent years, cloud data centers are facing more and more traffic demands, resulting in the continuous formation and expansion of cloud data centers around the world [1]. Although their economic profits are increasing, huge energy consumption has also received more and more attention. Since cloud computing can realize the flexibility and scalability of computing resources [2], and the sheer

* This work was jointly supported by National Key Research and Development Program of China under Grant 2018YFB1003702, Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, and the Talent Project in Six Fields of Jiangsu Province under Grant 2015-JNHB-012.
size of its scale, the problem about energy consumption of cloud data centers has changed from decentralized way in the past to the current centralized approach[3, 4]. In order to optimize the use of energy consumption in cloud data centers, it is necessary to establish a high-precision prediction model of energy consumption for cloud data centers.

In view of the above situation, the RBF neural network is used to model and predict the energy consumption of the cloud data centers, which is aimed to prove the accuracy of the framework for collecting energy consumption and influencing factors.

## 2 Architecture

The framework for collecting energy consumption of IT system proposed in this paper is extended on the basis of the framework for collecting energy consumption of IT system proposed by Zhao, Z. [5] in 2016, and is improved in the details. Figure 1 is the framework for collecting energy consumption of IT system.

<figure><img src="image_3.png" /><figcaption>Fig. 1. The framework for collecting energy consumption of IT system</figcaption></figure>

The framework can be divided into physical layer, resource management layer, virtual layer and application layer. The physical layer is located at the bottom of the framework of cloud environment and is divided into physical resources for building cloud environments and power tester. The physical resources used to build the cloud environment is the infrastructure built by the cloud environment, which are the fundamental source of energy consumption of the IT system, and the main target of cloud data center energy consumption prediction. The resource management layer is located above the physical layer and is divided into the operating system, cloud computing resource management service and system data collector. The virtual layer is the third layer of the framework of cloud environment and is to provide users with virtualization platforms and virtualized resources. The application layer is located above the virtual layer and is
to run the required application according to different requirements on the virtual
machine that the users applies for.

## 3 Factors Affecting Energy Consumption of IT System

The key innovations presented in this paper are: By decomposing the energy
consumption of IT system of the cloud data center and selecting better factors
affecting energy consumption to train the RBF neural network, the prediction
ability of the RBF neural network model is better than the previous model of
energy consumption of IT system, which is aim to prove that these factors play
a crucial role in the energy consumption of IT systems.

Modeling energy consumption based on system usage is a usual method of
energy modeling. According to the related research [6], the top six system pa-
rameters in power consumption of virtual machine have a significant nonlinear
relationship with the energy consumption of virtual machine, which are user
mode runs using the percentage of total CPU time, core state runs using the
percentage of total CPU time, memory utilization, the total amount of I/O
transfer per physical device per second, number of pages missing per second of
system, and the physical machine load of each physical device. Moreover, be-
cause there is a massive scale of the data in the cloud data center, this paper
will eliminate the number of pages missing per second of system, which affects
the model of energy consumption minimally.

Through experiments, it is found that if the system information is collected
for only this part of the equipment, not only can the burden of equipment which
collect system information be reduced, but also a predictive model with higher
fitting degree can be obtained.

As the experiments showed, we found that the energy consumption of IT
systems is extremely sensitive to changes in the energy consumption in a few
seconds nearby. Therefore, in the case of maintaining the energy object model
with strong objectivity and robustness, and improving the accuracy of the en-
ergy consumption model, this paper keeps the modeling method of randomly
disturbing the training samples, and adds the energy consumption in the time
of last unit to the data set of the training model.

## 4 Application and Experiments

This paper compared the data from the above experiments from two aspects.
On the one hand, we observed the result about whether the training data of the
RBF neural network included the compute nodes that enter the virtual layer.
On the other hand, we observed the result about whether the training data of
the RBF neural network included energy consumption data in the time of last
unit.

The data of this experiment were all analyzed using RBF neural network.
The same part of the training data included the system information data of
the control nodes, the network nodes, and the compute nodes that entered the
<figure><img src="image_4.png" /><figcaption>(a) With and without the compute nodes that entered the virtual layer.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(b) With and without energy consumption data in the time of last unit.</figcaption></figure>

<caption>**Fig. 2.** The training results of the RBF neural network</caption>

<table><caption>**Table 1.** Error comparison in two cases</caption><thead><tr><th>Model</th><th>P1</th><th>P2</th><th>P3</th></tr></thead><tbody><tr><td>The sum of squared errors</td><td>38.415</td><td>43.827</td><td>63.181</td></tr><tr><td>The relative error</td><td>4.2%</td><td>4.8%</td><td>6.9%</td></tr></tbody></table>

virtual layer and the energy consumption in the time of last unit. The different parts were that the first training data did not include the compute nodes that did not enter the virtual layer, and the second training data included the compute nodes that did not enter the virtual layer. In Figure 2(a), the predicted value P1 was the training situation of the training data that did not include the compute nodes that did not enter the virtual layer; the predicted value P2 was the training situation of the training data that included the compute nodes that did not enter the virtual layer.

Although the prediction result of P1 at a few special points was extremely extreme, the predicted value P1 had a better fit with the actual value in comparison with the predicted value P2.

As shown in Table 1, we found that there was better predicted effect of the RBF neural network model, which was trained by the training data that did not include the compute nodes that did not enter the virtual layer. From this experiment, we found that when predicting energy consumption in a cloud environment, it was correct to use the system data of the control nodes, the network nodes, and the compute nodes that entered the virtual layer as the training data.

The data of this experiment were all analyzed using RBF neural network. The same part of the training data included the system information data of the control nodes, the network nodes, and the compute nodes that entered the virtual layer. The different parts were that the first training data included the energy consumption in the time of last unit, and the second training data did not include the energy consumption in the time of last unit. In Figure 2(b), the predicted value P1 was the training situation of the training data that included the energy consumption in the time of last unit; the predicted value P3 was the training
situation of the training data that did not include the energy consumption in
the time of last unit.

Although the prediction result of P1 at a few special points was extremely
extreme, the predicted value P1 had a better fit with the actual value in com-
parison with the predicted value P3.

As shown in Table 1, we found that there was better predicted effect of the RBF neural network model, which was trained by the training data that included the energy consumption in the time of last unit. From this experiment, we found that when predicting energy consumption in a cloud environment, it was correct to use the energy consumption in the time of last unit as the part of training data.

5 Conclusion

This paper analyzes the architecture of energy consumption of IT system in cloud data centers and proposes a new framework for collecting energy consumption. Based on this framework, the factors affecting energy consumption are studied, and various parameters closely related to energy consumption are selected.

6 Acknowledgement

This work was jointly supported by National Key Research and Development Program of China under Grant 2018YFB1003702, Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, and the Talent Project in Six Fields of Jiangsu Province under Grant 2015-JNHB-012.

References

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5. Zhou Zhao. *Energy Consumption Acquisition and Prediction Method for Cloud Computing Services*. PhD thesis, East China Normal University, Shanghai, China, 2016.

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