2012 IEEE Conference on Control, Systems and Industrial Informatics (ICCSII) Bandung, Indonesia, September 23-26,2012

Towards Greening a Campus Grid: Free Cooling During Unsociable Hours

lun Okitsu Central Research Laboratory,

Hitachi, Ltd.,

Yokohama, Kanagawa, Japan

Ken Naono R&D Department,

Hitachi Asia Malaysia Sdn. Bhd.,

Kuala Lumpur, Malaysia

Abstract-Free cooling has become widely used in the area of computer room thermal control, especially where the temperature constraint can be relaxed. However, in tropical regions, such as Malaysia, free cooling cannot be applied all of the time. This paper presents a strategy for grids that allows jobs to be executed on resources that are free cooled. The paper describes how a recommended period of time for using free cooled resources is predicted. The period is predicted from a historical analysis by using machine learning. Experiments on a classroom used for campus grid computing showed that, typically, free cooled resources can be used for 5 hours per day, when the temperature is less than 28 degrees Celsius. The result is of use to those developing campus grids in tropical countries.

Keywords- Free cooling; campus grid; machine learning

I. INTRODUCTION

While cooling of computer hardware has been one of the central issues concerning computer technologies for many decades, it is now even more important in the sense of "green IT." IT plays a very important role in many areas, such as in the efficiency of power generation and the reduction of transportation costs; however, IT itself requires ever more power-hungry cooling systems as the scale of computing worldwide increases. The technologies associated with the "greening of IT" are therefore fundamental technologies in today's worldwide efforts towards a sustainable society.

Cooling technologies for computer hardware mainly comprise of coping with local heat sources, location being pre­determined by job scheduling, and removal of the emitted heat by the flow of either air or water. Among them, "free cooling," which uses natural air instead of chilled air, has recently been drawing considerable attention. This is because data center management is conscious of the high cost of operating chiller systems.

Free cooling technologies have been used in data centers in areas where cold outside air is available; however, as far as the authors are aware, they have not been applied to a campus grid in a hot country. This challenge is of particular interest to the authors as they wish to extend the area in which processing is

978-1-4673-1023-9/12/$31.00 ©2012 IEEE 202

Shaharin Anwar Sulaiman Mechanical Engineering,

Universiti Teknologi PETRONAS,

Perak, Malaysia

Nordin Zakaria, Alan Oxley Computer & Information Sciences,

Universiti Teknologi PETRONAS,

Perak, Malaysia

done from the data center by including campus labs, and to explore the issues relevant to deployment in a tropical country.

The campus grid computing infrastructure at a university campus in Malaysia uses a variety of computing resources. The PCs (personal computers) linked to the campus grid are distributed across several dozen classrooms, totaling several thousand PCs in all. A campus grid can reduce the power usage per unit of processing by utilizing unused computer resources on the campus. On the campus, a central air conditioning system is used for cooling the rooms. To reduce power consumption, the system is scheduled to only operate during working hours on weekdays. It can be operated on weekends or weekday nights by request, where the requester specifies the location where air conditioning is required, and the duration. The performance of the campus grid is significantly reduced if its operation is limited to the times when the air conditioning system is operating.

To improve campus grid performance, we free cooled the campus labs at certain times when the air conditioning was off. In a tropical country such as Malaysia the outside temperature at night is not too high, from a PC's perspective, so free cooling by natural ventilation can be applied for part of the night. A continuous period of time when a lab is cooled adequately is referred to as a "valid cooled period," (denoted VCP henceforth). The difficulty is deciding on the times each day when processing can take place under free cooling. Selecting a wrong time may cause heat-related problems as well as grid performance problems. In the present study, we aimed to improve grid performance whilst protecting the PCs from the heat. Our work can be termed "free cooling during unsociable hours." It involves predicting temperature and relaxing the constraint normally imposed on the computer lab temperature.

Through an experiment, the applicable constraints and cooling conditions to meet both the performance and the heat protection requirements were determined. It is concluded, from the experimental results, that free cooling is effective for greening a campus grid in a tropical country similar to Malaysia.

A. Related work

II. LITERATURE REVIEW

To reduce the total power consumption of computing it is necessary to reduce the power consumption of the air conditioning used for that computing. There are many ways to reduce power consumption [1]. Some examples involve improving the efficiency of the air conditioning system itself, others involve improving efficiency by controlling the IT workload [2] [3] [4], whilst others use free cooling to reduce the power consumption of chillers.

Free cooling is an effective technology for reducing power consumption [5]. The leading operators of large-scale data centers have thus been adopting it [6] [7]. However, these data centers are limited to low temperature and low humidity locations. Free cooling technology is also being applied in a wide area of applications in countries with a temperate climate. To popularize this technology to data centers worldwide, ASHRAE have been relaxing its constraints on the operating temperature and humidity [8]. Accordingly, the applicability of free cooling in Japan [9] and Turkey [10] has been studied. With these studies the air conditioner setting was adjusted according to the outside environment but was operational all of the time. The applicability of water economizer, one of free cooling technology, in United States has also been studied [II]. The study showed water economizer provided more energy savings relative to the conventional systems but the setting was also adjusted according to the outside environment. Free cooling supplemented air conditioning whereas in our research we focus on free cooling with the air conditioning system switched off. Natural ventilation is a kind of free cooling that exploits the difference between the indoor temperature and the outdoor temperature [12] [13].

The efficiency of free cooling can be improved by relaxing operational constraints. As described above, ASHRAE have relaxed the temperature and humidity constraints, and products that comply with these new constraints are being released. Note that some of the products not only comply with the constraints based on the ASHRAE guidelines, but also comply with even more relaxed constraints, provided that this mode is used sparingly (10% of the time, say) [14]. Short term relaxed constraints can improve the effect of free cooling significantly and are becoming more widely adopted.

Much research has been conducted on a related topic -"thermally-aware job scheduling" [3] [4]. It has been, however, based on the assumption that facilities such as air conditioners have responsibility for preventing heat-related problems and operate continuously, as is commonly the case with traditional data centers. On the other hand, our strategy is also responsible for preventing heat-related problems when processing is taking place with the air conditioners switched off.

There are many methods for predicting indoor temperature. By using a variant of the "bin method," an immediate-future indoor temperature can be predicted [IS]. However, this method supposes that the indoor temperature is constant; in other words it assumes that an air conditioning system is being used. As a result, this method is not suitable for a free cooling environment in which the indoor temperature varies according

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to the outdoor temperature. Ghiaus et al. proposed a suitable method for a free cooling environment [13]; however, the method requires a lot of information, such as the building type, the comfort range, and past records of the local climate over a long period. Moore et al. proposed a temperature prediction method that uses machine learning [3]; however, the method does not consider the outdoor temperature.

B. Challenges

The aim is to significantly increase the number of grid job executions by making use of the computer labs when the air conditioning is switched off. The IT equipment must be protected from heat-related problems and so one challenge of this study is the prediction of indoor temperature. The timings of when the grid can be used under free cooling depend on the accuracy of the indoor temperature prediction. The accuracy target was thus set to "more than 90%" (as described in section TTT.A). The second challenge is to clarify the optimal conditions for operating the campus grid under free cooling.

TIT. PROPOSED STRATEGY FOR USING AIR CONDITIONING

AND FREE COOLING

To achieve a large amount of grid job executions, free cooling (which executes jobs on the basis of indoor temperature) was used with a relaxed temperature constraint. The temperature constraint is expressed as threshold to determine whether the period is VCP. That is, VCP is period when indoor temperature is less than the constraint. Combination of free cooling and relaxed temperature constraint can extend VCP when the air conditioning is off and the temperature constraint is satisfied, and in these circumstances a larger number of grid jobs can be executed.

To achieve a larger number of grid job executions, free cooling using natural ventilation through closed windows, at night, was used. A combination of the exclusive use of air conditioning during certain times and the exclusive use of free cooling during certain times ("air-conditioning-plus-free­cooling" hereafter) can extend VCP so that a larger amount of grid jobs can be executed.

Let us turn our attention to the prediction of VCP. Because cooling of the campus grid is intermittent, the temperature constraint is not satisfied during certain times, namely, outside VCPs. To protect PCs from heat, the job scheduler has to identifY these times before executing jobs. Accordingly, VCPs must be predicted.

A. Thermally-aware job scheduling

Thermally-aware job scheduling is explained in Fig. I. The thermally-aware job scheduler determines job scheduling on the basis of the indoor temperature, the temperature constraint, and the job execution time so as to protect PCs from heat­related problems.

To protect PCs from heat-related problems, the temperature constraint for each room must be satisfied while the PCs are operating. This occurs either when the air conditioning is operating or when there is free cooling and the indoor temperature is less than the threshold temperature (x°C).

Grid performance is a measure of how much computing time is used within a specific duration. In an environment where there is not one single VCP, grid performance depends on the proportion of time in which the grid can operate, the grain size of VCPs and the job lengths. VCPratio denotes the proportion of time in which the grid can operate, as a percentage. The grain size of VCPs (VCPsize) is the average VCP duration. The grain size of jobs (Jobsize) is the average execution time of a job. As Jobsize increases, grid performance decreases. As VCPsize increases, grid performance increases. Grid performance can be estimated with a function called GridPer/ormance defined as the proportion of time that the grid is doing useful work.

G 'd'P .r; "C'P ' ( (VCPsize % JObSiZe)XJObSize

J rz elJonnance = v " ratlOx

VCPsize

where the "%" symbol denotes integer division.

(1)

Grid performance can be improved by relaxing the temperature constraint. This is because VCPs becomes extended, and VCPsize and VCPratio increase, as the constraint is relaxed. However, as the constraint is relaxed, the possibility of heat-related problems increases. An acceptable temperature constraint was determined by the experiment described in Section IV.

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Figure I. Thennally-aware job scheduling

B. Free cooling using natural ventilation

Natural ventilation is an effective technique to free cool. Its potential for cooling may be assessed according to the difference between indoor temperature and outdoor temperature. If the indoor temperature is higher than the outdoor temperature, heat in a room will escape to the outside through the closed windows of the room.

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Figure 2. One day transition of temperature and dew point in Tpoh, Malaysia.

An example of a one working day record of indoor and outdoor temperatures and the maximum dew points is shown in Fig. 2, which is extracted from a one month long record. The

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data was gathered from room sensors on campus and a weather station sensor in Ipoh, Malaysia. As for the room, the operation time of the air conditioning was from 8:00AM to 8:00PM on weekdays, and for the rest of the time it was inoperative. The figure shows that the indoor temperature was less than 28"C for most of the time, irrespective of whether or not the air conditioning was on. The reason for acceptable nighttime temperatures is due to the natural ventilation.

Both the indoor temperature (less than 28"C) and the maximum dew point (around 24"C) meet the ASHRAE 2011 Class B guidelines [8], which typically cover offices or homes having minimal control of the environmental parameters. Products typically designed for this environment are personal computers, workstations, laptops, and printers. These guidelines and the results in Fig. 2 confirm that a temperature constraint of "< 28"C" will not negatively influence the PCs of a campus grid. It is also confirmed that the maximum dew point, of around 24 "C, satisfies the guidelines, which specify "less than 28"C." Hence the effect of humidity can be neglected. It can therefore be expected that by adopting a relaxed temperature constraint, such as "< 28"C," free cooling is applicable at night in tropical countries similar to Malaysia.

C. Prediction o/valid cooled period

Because there are times when the temperature constraint is not satisfied (namely, those between VCPs), the scheduler needs to know when these times are before scheduling jobs. The timings of VCPs are predicted by a decision tree machine­learning method. A decision tree is used to facilitate understanding of the prediction algorithm [16]. This method decides, for each one-hour period, whether or not it is part of a VCP. The training data used for decision tree learning makes use of the target variable to be classified and input variables obtained by observation.

The target variable is defined as the function F vcP(hours, temp), where hours means a predicted period and temp means the threshold temperature of the VCPs. FvcP(hours, temp) determines whether a specific period of time (measured in hours) on a specific day in the immediate future is a VCP or not. It is defined as

{I temperature < temp tor a period of hours trom start time (2) FVCT = . o othefWlse

The input values are defined as the "leading factors" of indoor temperature change. Note that the choice of leading factors often makes a big difference to the accuracy of the prediction. Using a number of factors, such as the air conditioning operation schedule, the current time, and the weather forecast, can improve the prediction accuracy. However, so that this method can be adapted to several kinds of rooms, only information on temperatures and the current state of the air conditioning is used. These leading factors include:

• Tmax; Maximum current indoor temperature.

• Tmin; Minimum current indoor temperature.

• Tdi!!; Temperature difference between Tmax and outside temperature.

• Ll Tmax; Difference between the current value of Tmax and its value one hour ago.

• Tmax - Tmin; Temperature difference in a room.

• Fair/lmv; Flag indicating whether air conditioning system is currently blowing.

• Faircon; Flag indicating whether an air conditioning system is currently running.

The magnitude of Tdi/f should reduce prior to theuse of free

cooling. Ll Tmax shows the trend of Tmax. Tmax - Tmin shows

whether cooling of the room is working efficiently; if cooling is working efficiently then Tmax - Tmin has a small value.

The machine-learning algorithm CHART (Classification and Regression Trees) [16] is used to make the decision trees. A tree is made for each combination of the predicted period, the temperature constraint and the room being studied. An entropy calculation is used to detect the information gain. Pruning was not used because its effect is insignificant for this training data set.

Figure 3. Example of decision tree (hours=4, temp=26)

An example decision tree is shown in Fig. 3. By simply following the path down the tree, with the current sensing data, it is possible to determine whether there is a VCP that covers the specified hours.

D. Flow chart of job scheduler with temperature prediction

The flow chart of the job scheduler is shown in Fig. 4. Making use of this chart requires that decision trees are created in advance and updated regularly.

Figure 4. Flow chart of job scheduler

When the scheduler schedules a job, it estimates the job­running period and identifies the rooms that have a VCP covering the period, by using the decision trees. If rooms are available to run the job, the scheduler selects the best PC in

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those rooms by taking into account other factors (such as PC performance) and allocates the job to the PC. If there is no room to run the job, the scheduler puts the job into "pending."

IV. EXPERIMENTS

The experiment to verify the prediction method has two purposes. The first of these is to identify the dominant factors among the leading factors listed above. The second purpose is to shed light on optimizing grid computing performance when a computer lab is cooled as described in this paper.

A. Campus room used for experiments

The computing resources of the Campus Grid consist of HPC (high performance computing) computers, GPGPU (General-purpose computing on graphics processing units) tuned computers in server rooms, and PCs for student use in computer labs. The PCs are distributed across dozens of computer labs, so the PCs number several thousand. The effective use of the PCs in the computer labs was investigated.

Two features characterize the computer labs: (i) the window size and (ii) the air conditioning schedule. The computer labs have large windows, and the outside air temperature significantly affects their indoor temperature. Air conditioning in the computer labs operates during working hours on weekdays in accordance with the activities of students, but does not operate at weekends (in order to reduce power consumption). Experiments on the cooling of the computer labs were carried out under the following two scenarios:

• Air-conditioning-plus-free-cooling; Air conditioning was operating from 8:00AM to 8:00PM on weekdays. Outside the period, free cooling was used.

• Free-cooling-only; Air conditioning was not operating for whole period. Only free cooling was used.

B. Prediction accuracy

Prediction accuracy is defined as the proportion of correctly predicted period in the entire period. The correct predict means it does not cause neither "false negative" error nor "false positive" error. If there is a period in which the temperature constraint has wrongly been predicted to be satisfied then the PCs can be damaged. This situation is referred to as a "false negative" error. On the other hand, if there is a period in which the temperature constraint has wrongly been predicted to have not been satisfied then jobs cannot run, and so grid performance is diminished. This situation is referred to as a "false positive" error. Thus, the accuracy must be high from the viewpoints of grid performance and the protection of PCs from heat. Improving the accuracy and reducing the likelihood of these errors can improve grid performance and the heat protection capability.

The accuracy was evaluated by using a four-week history of sensor data. The decision tree was trained on three weeks of data, and forward testing of the decision tree was performed to cover one week. The predictions were compared to the historical data for that week.

The relationship between the duration of the predicted period and the prediction accuracy is plotted in Fig. 5. Different training period durations were used, from four to 20

days. The results represent only the air-conditioning-plus-free­cooling scenario.

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Figure 5. Relationship between predicted period and accuracy

For training periods of 14 days or less, as the training period is increased, prediction accuracy increases. The accuracy does not increase after the training period exceeds 14 days. The reason behind this phenomenon will now be explained. In a tropical country similar to Malaysia, the daily outdoor temperature change follows nearly the same pattern all year, and this feature affects the training period. That is, the training data of the machine learning method is simplified, and the learning converges in a short (14 day) training period. In addition, this means that the same decision trees can be used throughout the year. It can be concluded that the accuracy of the prediction comes from a feature of a tropical country, that is, a monotonic change in outdoor temperature.

As the duration of the period being predicted is increased, accuracy is decreased. This result shows that the prediction with these leading factors is suitable for predicting the next few hours. Also, within five hours of a VCP, in other words, within the grain size of jobs taking less than five hours, the accuracy is 90%. It is thus concluded that a small grain size of jobs is preferred in terms of protecting PCs from heat.

Note that accuracy can be improved in several ways. Firstly, the leading factors shown in section TTT.C are suitable for predicting the next few hours. Introducing leading factors that are suitable for long-term prediction, such as the air conditioning operation schedule, the current time, and the weather forecast, can improve accuracy of long-term predictions. Secondly, methods to suppress sudden changes in indoor temperature, such as controlling air circulation and avoiding use of PCs that contribute to heat recirculation in the room [2], can improve accuracy.

C. Prediction algorithm

The relationship between predicted period and prediction accuracy for the air-conditioning-plus-free-cooling and free­cooling-only scenarios, with a training period of four days, is shown in Fig. 6. The accuracies are about the same.

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Figure 6. Relationship between predicted period and accuracy

206

With reference to Fig. 6, Table 1 lists the frequency with which the leading factors were referenced by the top and second level nodes of each decision tree. The frequency of leading factor is defined as the proportion of nodes that has the leading factor in the top and second level nodes. Total of frequencies of leading factors is 100%. The algorithm for each cooling scenario can be determined from the frequencies.

TABLE 1. REFERENCE FREQUENCIES OF LEADING FACTORS

Leading Reference Frequency factor Air-conditioning-plus- Free-cooling-on(v [ree-coolin/.! Tmax 41.6% 57.0%

Tmin 29.0% 3.5%

Tdiff 7.1% 27.8%

Ll Tmax 12.9% 7.0%

Tmax - Tmin 6.3% 3.5%

Fairf!Oll' 2.7% 1.2%

Faircon 0.4% 0.0%

Total 100.0% 100.0%

Under the free-cooling-only scenario, Tmax and TditT are often referred to. The result shows that indoor temperature is heavily influenced by outdoor temperature, on the other side of closed windows; free cooling is expected to be effective in the case of a campus grid. On the other hand, under the air­conditioning-plus-free-cooling scenario, TlIla< and Tmin are often referred to but Tdiff is not often referred to. The reason behind this can be explained by the assumption that indoor temperature is less influenced by outdoor temperature while air conditioning is operating.

Note that the important leading indicators of the algorithms change according to the scenario. Thus, the decision tree machine-learning method can adapt to different environments such as other computer labs on campus and labs in other countries. The overall analysis of the algorithm has shown that (1) a decision tree with leading factors that include TlIlax;, Tmin and TditT is a suitable method for predicting temperature and (2) free cooling is expected to be effective for a campus grid.

D. Grid performance

The relationship between the temperature constraint and the proportion of time that computing can take place (VCPratio) for the air-conditioning-plus-free-cooling and free-cooling-only scenarios is shown in Fig. 7 This figure indicates that, as the constraint is relaxed, VCPratio increases, and that the values of VCPratio for the two scenarios are almost the same (95.8% and 98.5%) under the constraint "< 30°C."

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Figure 7. Relationship between temperature constraint and VCPratio

The relationship between temperature constraint and VCP grain size (VCPsize) for the air-conditioning-plus-free-cooling and free-cooling-only scenarios is plotted in Fig. 8 It is clear that as the constraint is relaxed, grain size rises and the difference between the two scenarios increases.

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Figure 8. Relationship between temperature constraint and VCPsize.

A further explanation of the graph shapes in Figs. 7 and 8 is now given. Tn the air-conditioning-plus-free-cooling scenario, there is one period each weekend, of about two hours duration, when computing cannot take place. Tn the free-cooling-only scenario, there is one period every two days when computing cannot take place. Because the size of these breaks is short, they affect VCPsize but not VCPratio.

100 -�

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/"' � � 40 / /

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Figure 9. Relationship between temperature constraint and grid performance.

The relationship between temperature constraint and grid performance, with a job grain size of five hours or less, for the two scenarios is plotted in Fig. 9. Grid performance is calculated from Equation (1) and so Fig. 9 is dependent on Figs. 7 and 8.

As shown in Fig. 9, as the constraint is relaxed, grid performance increases. The reason why there is positive correlation between the constraint and the performance is as follows: As the constraint is relaxed, VCPsize increases as shown in Fig.8. Then as VCPsize increases, the performance increases as described in Section TTT.A.

The grid performance is similar for both scenarios when the constraint is relaxed to "< 28'C" and more. However, in order to take advantage of natural ventilation at night, "< 28'C" is the preferred constraint, as mentioned section m.B.

V. CONCLUSION

To achieve higher grid performance and lessen the temperature constraints in a hot region, the job scheduler of a campus grid follows three procedures: first, combine air conditioning and free cooling in order to expand VCP; second, predict the timings of VCPs based on a historical analysis of

207

outdoor and indoor temperatures; third, execute thermally­aware job scheduling based on predictions made with fine­grained job sizes. To balance grid performance with air conditioning energy consumption, our experiment suggested operating the campus grid under a temperature constraint of "<

28'C" in the air-conditioning-plus-free-cooling scenario. We can confirm that free cooling is effective for greening a campus grid in a tropical country.

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