Optimal Design of the Data Center Environmental Temperature Monitoring ZhiXiang Yuan 1,2, a, ShuangBo Lai 2,b, Ming Liu 1,c, HuiYi Zhang 1,2, d

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1 Applied Mechanics and Materials Online: ISSN: , Vols , pp doi: / Trans Tech Publications, Switzerland Optimal Design of the Data Center Environmental Temperature Monitoring ZhiXiang Yuan 1,2, a, ShuangBo Lai 2,b, Ming Liu 1,c, HuiYi Zhang 1,2, d 1 Modern Education Technology and Network Management Center, Anhui University of Technology, China 2 School of Computer Science and Technology, Anhui University of Technology, China a zxyuan@ahut.edu.cn, b laishuangbo@163.com, c newman@ahut.edu.cn, d hyzhang@ahut.edu.cn Keywords: datacenter; local hotspot; wireless sensor; monitoring; wireless sensor network Abstract: Traditional datacenter temperature monitoring method is to install some temperature sensors which are unable to cover all areas completely, so there are some problems about local hot spot. The method is aiming to find out the position of potential hazard which may exist in the data center on the basis of data center thermal environment evaluation method and characteristics of air distribution. By applying this method into the wireless sensor network monitoring system, it brings a meaningful improvement of the monitoring system. 0 Introduction With the development of the computer technology, IT-Room environment monitoring system has been widely used in telecommunications, electric power, transportation, bank, intelligent residential areas. Monitoring system plays a decisive role in the aspect of ensuring safety and improving work efficiency [1].Data center environmental equipment (power supply, UPS, air-conditioning, fire protection, security and others) must always provide a normal operating environment to computer systems. So a good environmental temperature of IT-room is vital to guarantee the normal operation of equipment and prolong its lifetime. The data center is a very special kind of air condition environment. High temperature phenomenon cannot appear in the equipment work areas (local hot spot), and the design of airflow organization must be reasonable in IT-Room. Meanwhile, air conditioning energy consumption must be low (air conditioning energy consumption accounts for 54% of the whole in data center [2] ). Currently, the areas of local hot spot are mainly based on the IT-Room s equipment (heat source, such as a single frame), characteristics of air distribution and thermal environment evaluation system. The theory representative mature such as SHI (Supple Heat Index) and RHI (Return Heat Index) proposed by Sharma [3], β proposed by Bash [4], RCI (Rack Cooling Index) and RTI (Return Temperature Index) proposed by Herrlin [5], as well as Mixing index evaluation system proposed by Tian Hao [6] et al.. The above data center evaluation method is from characteristics of air distribution. According to actual environment, this paper puts forward a method based on wireless sensor networks and data fitting to find out local hot spot. 1. Design of monitoring system Data center environment monitoring system use the wireless sensor network nodes to achieve. Communication between nodes uses the ZigBee protocol to achieve, coordinator node communicates with PC through the serial. The PC software according to the temperature of the node, using the data fitting method to infer the local hot spot where may exist. In local hot spot which may exist, we should strengthen the monitoring at the same time and avoid important equipment as far as possible. The room is not a large space, so monitoring temperature system changes the star topology to keep real-time, reliable. The monitoring system structure diagram shows in figure 1. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-12/05/16,16:53:03)

2 Applied Mechanics and Materials Vols The terminal acquire temperature nodes Database server Coordinator node Database processing server Monitor terminal Design of wireless sensor network 2.1 Module equipment selection Monitor terminal Fig 1 Structure diagram of monitoring system The system uses the JN5121 chip of Jennic.JN5121 is a wireless microcontroller [7] which has low power, low cost and is fully compatible with the ZigBee protocol. It is integrated in a 32 bit RISC kernel and an IEEE compliant 2.4GH wireless transceiver. This module has high integration and abundant hardware resources which makes the design of peripheral circuit very simple. Temperature and humidity sensor module used in this paper is SHT11 temperature and humidity sensor which is made by Swiss Sensirion. The module temperature measurement accuracy is ± 0.4, and the range is -40 ~ [8] 2.2 Wireless sensor network nodes deployment Wireless sensor nodes are divided into two categories: one category is the coordinator node and another is the terminal node. The main function of coordinator node is receiving terminal node information and integrating data to the host computer. The main function of the terminal node is to collect temperature information of data center room air and transmit the processed information to the coordinator node. The terminal nodes deployment randomly is one of the most simple node deployment, but which may lead to performance and demand not match and waste of resources and other issues. According to the practical application environment, the high quality of the node deployment can better achieve the intelligent of the sensor network. " According to China Telecom data center equipment room power s requirements on air conditioning environment design specification (Provisional)" (China Telecom No. [2005]741), IDC (Internet datacenter) temperature and humidity and dust environment telecommunications, make the following provisions: the AA grade, A grade room temperature is 21 ~ 25, B grade, C grade room temperature is 18 ~ 28 [9]. The monitoring data center in this paper belongs to the C grade room. The terminal node deployment is mainly distributed in the cabinet section s five positions which are up, middle up, middle, middle down, down. According to the data collected by the terminal nodes, gets the maximum air inlet temperature, minimum air inlet temperature and cabinet section average air inlet temperature, average air outlet temperature of each cabinet. The terminal node s operation flow chart is shown below:

3 2812 Mechatronics Engineering, Computing and Information Technology Wait for starting signal from coordinator Start YES Send address and state to coordinator Receive operation signal YES NO NO Send data to coordinator NO 3. Data center local hot spot found Receive end signal End YES Fig 2 ZigBee wireless network terminal node operation flow chart The reason of local hot spot generation: Firstly, due to the increasing number of server, the data center space is more and more intense, more and more server has been placed in the cabinet. Moreover, the development direction of the computer is more and more small, more and more compact, especially the blade server. So the temperature of data center cabinet exceeds the withstand scope of computer equipment. Secondly, in a raised floor data room, cold air condition through the floor beneath channel reaches cabinet and each serve. However, before the cold air reaches remote cabinet, air has been weakened, coupled with elevated floor which is often full of cable and pipeline. Thirdly, the airflow is unreasonable which causes the air to short circuit. For example, the cabinet outlet air without cooling again inhaled inside the cabinet. 3.1 Existing discovery method Currently, the mature methods to find local hot spot are β proposed by Bash and the mixing index proposed by Tian Hao. The β method corrects Computational Method based on SHI proposed by Sharma. Through average temperature of cabinet inlet, average temperature of cabinet outlet and temperature of the air condition outlet as the reference standard,β can reflect the local hot spots caused by IT equipment failure. It is defined as follows: (1) Formula: T in_avg and T out_avg respectively represent average temperature of cabinet air inlet section and average temperature of cabinet air outlet section; T ref represents reference temperature, the air conditioner outlet temperature. The mixing index is defined through evaluating and describing temperature occurs permeably in a single frame. The method uses the highest temperature minus the minimum temperature of rack inlet section divided the average air temperature of rack outlet section minus the average air temperature of rack inlet section. The higher of ratio, the more possibility of the frame is local hot spots. It is defined as follows:

4 Applied Mechanics and Materials Vols (2) Formula: Tin_max represents a maximum temperature of inlet section; Tin_min represents a minimum temperature of inlet section; Tin_avg represents the average temperature of rack inlet section; Tout_avg represents the average temperature of rack outlet section 3.2 Data fitting method In the normal condition, there exists linear relation with power of cabinet and average inlet air temperature minus average outlet air temperature. At the same time, there exists affected by air flow organization of cabinet heat radiation, so proposing the fitting algorithm based on least squares method to find local hot spot. Fitting algorithm is described as follows: Step1: Gets the average temperature of inlet air and outlet air in each cabinet, and then get the temperature of outlet air minus inlet air average that is Ti (1<i<n). Step2: Gets power of each cabinet Pi(1<i<n). Step3: Using least square method fitting of data on Ti, Pi. Step4: Taking the Ti of each cabinet into fitting formula, then get the fitting power that is P 'i. Using the minus of fitting power and actual power, then get the variance that is Si(1<i<n). Step5: Sort the variance of each cabinet. If the variance is bigger then there is a greater probability of local hot spot. Fitting algorithm is defined as follows: (3) Formula: P i is come from the polynomials P = a*t2 + b*t +c. The polynomials is made through polyfit(t,p,2) by matlab. T represents the array of average inlet temperature minus average outlet temperature of cabinet. P represents the array of power of cabinet. 3.3 Evaluation data analysis For example, there is an actual data center whose size is 12m 21m, using down send then up return air ventilation. Each air supply orifice size is 0.6m 0.6m, whose holes rate is 0.4. A single cabinet size is 0.6m 0.6m 2m. The power of cabinets value ranging from 0 to 8 KW, because there are some cabinets idle. The cabinet is divided into two rows which has 16 cabinets. There are two hot air channels and two cold air channels. Fig 3 Plan of data center The measured data, including each cabinet highest inlet temperature, lowest inlet temperature, average of inlet temperature, average of outlet temperature and average of power. From the average of inlet temperature minus average of outlet temperature and average power of each cabinet, through least squares fitting, obtained curve equation as follows: (4) The fitted curve as shown below:

5 2814 Mechatronics Engineering, Computing and Information Technology Fig 4 Least square curve fitting According to the calculation formula of the mixing index of each cabinet and the method of least squares fitting variance, values shown as below. Fig 5 First sets of cabinets mixing index and fitting variance Fig 6 Second sets of cabinets mixing index and fitting variance From the above figure 5, figure 6, the cabinet of number 1, 8, 9, 10, 13, 21, 25 whose mixing index greater than 3 indicates the possibility of local hot spots is larger. The cabinet of number 8, 9, 10, 13, 21, 25 whose fitting variance coefficient greater than 1 indicates the possibility of local hot spots is larger. By the analysis, we should avoid place important equipment and high power devices in the cabinets of the larger variance coefficient fitting as far as possible. The actual data indicates that the method of fitting variance and the mixing index in the local hot spots similar roughly, proving method fitting variance has certain applicability. 4. Conclusion In environmental monitoring of the data center, the wireless sensor network can avoid difficulty of node layout well, and also can be adjusted according to actual need of the appropriate nodes location. Considering the local hot spots may exist in data center, when monitoring cannot cover all

6 Applied Mechanics and Materials Vols regions by the monitoring node within limited, We put forward data fitting by least square method. The method can realize layout points less and find out local hot spot. Data fitting method compared with Tian Hao's the mixing index, which can forecast local hot spot better. In general data center monitoring system, the method has a certainly significance in engineering application. Acknowledgement Fund Program: National science and technology support plan (2012BAK30B04-02). References [1]Fu Yuda.Research and design of wireless monitoring system of computer room[d].heilongjiang university, [2]Yang ping,xiao ying,jian qifei,luo xuewei,liu wenfei. The airflow characteristics and air conditioning cooling capacity allocation scheme of communication room[j].journal of South China University of Technology(Natural Science Edition),2009,37(11): [3]Sharma P K, Bash C E, Patel C D. Dimensionless parameters for evaluation of thermal design and performance of large scale data centers[c]//proceedings of the American Institute of Aeronautics and Astronautics(AIAA), St.Louis,MO,2002,Paper AIAA [4]Bash C E, Patel C D, Sharma P K. Efficient thermal management of data centers immediate and long term research needs[j]. Int. J. Heating, Ventilating, Air Conditioning and Refrigeration Research,2003,9(2): [5]Magunus K. Herrlin. Airflow and Cooling Performance of Data Centers: Two performance Metrics[J].ASHRAE Transactions,2008,114(2): [6]Tian hao,li zhen,liu xiaohua,qian xiaodong. Study on the evaluation index data center thermal environment[j].journal of refrigeration,2012,33(5):5-9. [7]Jennic official website [EB/OL] [8]Sensirion official website [EB/OL]. asheet-humidity-sensor-sht7x.pdf. [9]Wang jinggang,kang ligai,liujie,paolingling. Feasibility analysis of cooling IDC using out door cold source[j]. Heating Ventilation Air Conditioning,2009,39(2):

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