Impact of workload assignment on powerconsumption in software-defined data center

infrastructure

Takaaki Deguchi∗, Yoshiaki Taniguchi†, Go Hasegawa∗, Yutaka Nakamura‡,Norimichi Ukita§, Kazuhiro Matsuda¶ and Morito Matsuoka∗ §

∗ Graduate School of Information Science and Technology, Osaka University, Osaka 565–0871, Japan† Faculty of Science and Engineering, Kindai University, Osaka 577–8502, Japan

‡ Graduate School of Engineering Science, Osaka University, Osaka 560–0043, Japan§ Advanced Telecommunications Research Institute International, Kyoto 619–0288, Japan

¶ NTT Advanced Technology Corporation, Kanagawa 212–0014, Japan

Abstract—We proposed a workload assignment policy forreducing power consumption by air conditioners in data centers.Power consumption was estimated by computational fluid dy-namics for both the conventional equipment arrangement and thetandem arrangement newly developed to reuse the exhaust heatfrom servers. To reduce the air conditioner power consumptionby raising the temperature set points of the air conditioners,the temperatures of all server back-planes were equalized bymoving workload from the servers with the highest temperaturesto the servers with the lowest temperatures. Consequently, theair conditioners’ power consumption was reduced by 10.4% inthe conventional arrangement. In the tandem arrangement, theair conditioners’ power consumption was reduced by 53%, andalso the total power consumption of the whole data center wasexhibited to be reduced by 23% by reusing the exhaust heat fromthe servers.

Keywords—Data center, Power consumption reduction, Work-load assignment, Air conditioning, Computational fluid dynamicssimulation

I. INTRODUCTION

The dramatic increase in power consumption by data cen-ters has become a major societal problem in recent years. Thisincrease is a result of the popularization of cloud computing,online storage services, social networking, and other suchservices. Consequently, reducing power consumption by datacenters has become an urgent issue. Information and commu-nications technology (ICT) equipment units, air conditioners,and power supply units consume, respectively, 30%, 33%, and18% of the power used in a typical data center [1]. There havebeen various proposals for reducing power consumption of theindividual units in data centers. These proposals includes virtu-alization for workload assignment and a sleep mode for serversnot under load. However, the effect of power conservation inindividual units is that when only some servers are workingand the others are turned off, servers with higher workloadgenerate more heat than other servers, and hot spots occur insome parts of the data center as a result. When the temperatureof servers rises, it is necessary to either increase airflow ordecrease the temperature set points of air conditioners. Thisincreases power consumption by air conditioners. These airconditioners then cool the low-temperature servers more thannecessary, which is a waste of energy. Fig. 1 shows the thermal

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Fig. 1. Hot spots and over-cooled spots caused by workload concentration

effects of workload concentration: the creation of hot and over-cooled spots.

In addition, the difference between server inlet and exhaustair temperature depends on airflow through the servers [2]. Indata centers, the airflow and air temperature in and aroundservers differs by equipment arrangement. Hence, even if thesame workloads are assigned to performance-identical servers,the temperature of each server will differ depending on wherethe server is located. As a general rule, the temperature setpoint of air conditioners is set to a level that will prevent thehottest server from exceeding a certain temperature beyondwhich overheating is likely. When the servers have differingtemperatures, the air conditioners cool low temperature serversmore than is required. Server temperatures depend as wellon the workload quantity assigned to each server. If thetemperature distribution of the servers is narrowed and thetemperatures of the hottest servers drop as a consequence ofcontrolling the workload assigned to each on the basis oflocation, then the temperature set point of the air conditionerscan be raised, which will decrease power consumption by theair conditioners.

The solution for the problem above is coordinated op-eration between the server workload assignment system andthe air conditioners. Moreover, to reduce the overall powerconsumption of the data center, coordinated operation thataccounts for complex dependencies among the units in a data

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center is required. In this paper, we investigate such coordi-nation and propose a server workload assignment policy thatwill reduce power consumption by air conditioners throughnarrowing the distribution of temperature within data centerservers. We show the reduction potential of air conditioningpower.

We also propose a novel arrangement of equipment in datacenters to accommodate reuse of exhaust heat from servers. Insuch a system, the higher the temperature of the exhaust heatfrom the servers, the more efficient the reuse becomes.

II. EXPERIMENTAL PROCEDURE

In this section, we describe the structure of data centersand present the model of power consumption by servers and airconditioners that we used in this paper. The workload swappingprocedure to decrease power consumption by air conditionersis also explained.

A. Unit arrangement in data center

We will discuss the power consumption with two types ofdata center arrangements, which are shown in Fig. 2.

Fig. 2(a) shows the unit arrangement and airflow in aconventional data center. The datacenter consists of two hotaisles and one cold aisle between two rows of 6 racks. Around400 servers are installed in the twelve racks. Those servers’air intakes are facing the cold aisle located between the twolines of racks. The supply sides of the air conditioners facethe cold aisle and the return sides face the hot aisles.

Fig. 2(b) shows a novel arrangement of data center equip-ment with heat-tolerant servers, which allows efficient utiliza-tion of exhaust heat from those servers [3]. Those servers areable to be operated in higher temperature than typical com-modity servers. Additionally, when the heat-tolerant servers areoperated in a high-temperature environment, the temperatureof their exhaust heat is higher than that from ordinary servers.As shown in Fig 2(b), we arrange two rows of racks withthe exhaust side of one row facing the inlet side of the otherrow. With this equipment arrangement, the second row, withheat-tolerant servers, is exposed to the exhaust heat fromservers in the first row. The temperature of the exhaust heatemitted from the second row to an aisle is correspondinglyhigher. We call this aisle super hot aisle [3]. We use thishot air as input to desiccant air conditioners. With desiccantair conditioners, we can utilize the exhaust heat to controlthe humidity or heating in the office area and reduce thepower consumption of the office-area air conditioning. Theexchange efficiency dramatically increases when the temper-ature increases above around 50 ◦C [4]. In this paper, adesiccant-type air conditioner was simulated in the model.Moreover, the temperature of the exhaust air falls after the heatis used. Therefore, the desiccant air conditioners also assistin decreasing the return-air temperature in the data center.We call this data center equipment arrangement a tandemarrangement. In both arrangements, evening out the servertemperature by location-aware workload assignment decreasesthe temperature difference between the exhaust from differentservers and raises the total heat from exhaust. In a tandemarrangement data center, the increase in exhaust heat improvesthe efficiency of desiccant air conditioners and decreases powerconsumption in the office area.

Air

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Fig. 2. Arrangement and airflow in data centers designed for this study

B. Server model

In this paper, we suppose that a data center has N servers,{s1, s2, · · · , sN} with identical performance and that eachserver can execute from 0 to Lmax workloads simultaneously.All workloads are identical, independent, and suitable for anyserver in the data center. Generally, the power consumptionby a server is linearly related to the CPU utilization rate [5].For this reason, we suppose that the number of workloadsrunning on a server and the CPU utilization rate of the serverare linearly related. The power consumption pi of server siunder load li can be written as

pi = Pidle + liPload (1)

where Pidle is the power consumption of a server with no load,and Pload is the marginal power consumption to execute oneworkload. We suppose here that each server is a uniform heatsource and that all energy used by the server is emitted as heat.We call the average temperature of the exhaust side of serversi the server temperature ti and suppose that servers overheatwith probability 1 when the temperature exceeds Tbreak.

C. Power consumption of air conditioners

Each air conditioner removes heat ph from air by consum-ing power pc. The performance of an air conditioner can beexpressed as ph divided by pc and is called the coefficient ofperformance (COP). COP, which represents the performanceof air conditioners, is a function of temperature set point,volumetric airflow rate, load, outside air temperature, and otherfactors. In this paper, we estimated the power consumptionof air conditioners by a Gaussian process [6] modeled onexperimental data from an actual data center. We suppose thatthe load and temperature set point of the air conditioner, fanspeed, and outside air temperature are explanatory variables forthe dependent COP. Fig. 3 shows the relation between load and

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4060

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Air conditionerload

Temperatureset point

COP

4 6 8 10

Fig. 3. COP of air conditioners estimated by a Gaussian process

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Air conditioning System!

Fig. 4. Directly supplying air conditioning system

temperature set point and COP. In the figure, a fan speed of811 revolutions per minute and an outside air temperature is of10 ◦C is assumed. We implemented an air conditioning systemto supply air directly into the cold aisle of the datacenter, asshown in Fig. 4. This type of system consumes less power forthe fans than a raised-floor air conditioning system does [7].

D. Workload assignment procedure

In this section, we describe the server workload assignmentprocedure to narrow the server temperature distribution. LetLtotal be the number of workloads to be assigned in a datacenter. In this procedure, we assign server workloads with theaim of achieving a narrow server temperature distribution asfollows.

1) Let j, the pattern number, be equal to 0. Assignworkloads to each server equally. Each server exe-cutes Ltotal

N workloads and consumes power Pidle +LtotalN Pload. We call this initial workload assignment

pattern L0.2) Simulate the heat flow with workload assignment

pattern Lj and record the temperature of each serveras Tj = {t1, t2, · · · , tN} where ti is the temperatureof server i at time j. Save the workload assignmentpattern Lj = {l1, l2, · · · , lN} and the associatedserver temperatures Tj .

3) Find a highest-temperature server shigh and a lowest-temperature server slow from among all servers.

4) If server shigh has a load of at least 1 and slow hasload less than Lmax, then decrease the load of shighby 1 and increase the load of slow by 1, effectivelyreassigning a workload from shigh to slow. Update

the power consumption values accordingly. If servershigh does not have any workloads or slow has themaximum number of workloads, then go to step 6)instead. To simplify, we choose slow from the samerow as shigh.

5) If the current workload pattern has been seen before,go to step 6). If not, increase the pattern number jby 1 and go to step 2).

6) Let τj = maxti∈Tj be the maximum temperature ofeach temperature distribution and find the temperaturedistribution Tmin that gives the minimum τj . Theworkload assignment pattern Lmin is the result of theprocedure. We call the workload assignment patternfound at this step, Lmin, the resultant workloadassignment pattern.

If the maximum temperature of the initial workload assign-ment pattern τ0 exceeds the temperature limit Tbreak and themaximum temperature of the resultant workload assignmentpattern τmin is lower than τ0, then we can raise the temperatureset point of the air conditioners and thereby reduce theirpower consumption. Moreover, narrowing the server temper-ature distribution and raising the temperature set point raisesthe temperature of the exhaust from servers and improves theefficiency of the reuse system. The benefit of high ambienttemperature in the data center as a result of increment inoperating temperature of the air conditioners is also pointedout in [8]. In this study, we indicate the reduction potential ofair conditioning system by mean of the workload arrangement.Eventually, the heat reuse efficiency was tried to improved.

III. EVALUATION

In this section, we evaluate the power consumption forthe proposed server-workload-assignment policy by a compu-tational fluid dynamics simulation [9]. The policy was appliedto models of a conventionally arranged data center and a datacenter with the tandem arrangement and a system to reuseexhaust heat from the servers. Schematic diagrams of themodels are shown in Figs. 5(a) and 5(b), respectively, for theconventional and tandem arrangements. The two data centermodels consist of two rack rows with six racks in each rowand two air conditioners. Each rack is 40U (177.8 cm) talland accommodates five blocks of servers. Therefore, the datacenter has sixty blocks of servers. We call the front-side racksand air conditioner in the figure rack row A and air conditionerA, respectively, and the other row and air conditioner are rowB and air conditioner B, respectively. The server racks arenumbered from 1 for the rack nearest to the same-row airconditioner to 6 for the rack farthest from the same-row airconditioner.

In the evaluation, we use the following constants: Pidle, thepower consumption by one server block without a workload,is 800 W; Lmax, the maximum number of workloads thatone server block can execute, is 6; Pload, the per-workloadmarginal power consumption of a server block, is 200 W.We assume that the data center must execute 240 workloads(Ltotal = 240), which is two-thirds of the maximum numberof workloads, and the whole server power consumption inthe data center is 96 kW. Each server block executes 4workloads and consumes 1600 W in the initial workloadassignment pattern. We assume that ordinary servers overheat

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Racks BAir conditioner B

Racks A Air conditioner A

(a) Conventional arrangement with cold aisle andhot aisle

Racks BAir conditioner B

Racks A Air conditioner A

(b) Proposed tandem arrangement with coldaisle, hot aisle, and super-hot aisle to reuseexhaust heat from servers

Fig. 5. Simulation models of data centers

with probability 1 when their temperature exceeds 35 ◦C andthat heat-tolerant servers do so at a threshold of 50 ◦C, asmeasured at the back-plane.

A. Evaluation of the conventional arrangement

In the conventionally arranged data center model, themaximum server temperature with all servers executing thesame number of workloads is 35 ◦C for an air conditionertemperature set point of 19.8 ◦C and volumetric airflow of2.1 m3/s. We call this state the initial state. We applied theprocedure and obtained a workload assignment pattern suchthat the maximum server temperature is 32.9 ◦C. We raised thetemperature set point of the air conditioners by 2.1 ◦C, whichincreased the maximum server temperature to 35 ◦C. We callthis state the raised state. The back-plane temperatures of eachserver block are shown in Figs. 6(a) and 6(b) for the initialstate and the raised state, respectively. The five blocks of thehorizontal axis correspond to the server blocks of each rackfrom rack 1 to rack 6. For each rack, 5 data items are shownfrom left to right, these correspond to the server blocks frombottom to top of the rack.

As an additional check, we evaluated the air condition-ing system experimentally at an actual data center with anarrangement similar to that of this model to estimate the powerconsumption of the air conditioning system. The COP valueof the air conditioning system is measured under several con-ditions including heat load, temperature set point, fan speed,and outside air temperature. We suppose that the volumetric airflow is linearly proportional to the fan speed in the model andthat the outside air temperature is 10 ◦C. The total heat loadon the air conditioners is 96 kW, corresponding to the powerconsumption of servers. The load on the air conditioners can be

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Fig. 6. Back-plane temperatures of each server block in traditional arrange-ment

TABLE I. INFORMATION ON AIR CONDITIONERS IN CONVENTIONALARRANGEMENT

Initial state raised stateSide A Side B Side A Side B

Supply air temperature 19.8 19.8 21.9 21.9Return air temperature 31.6 31.6 32.8 32.8Temperature difference 11.8 11.8 11.8 11.8

Load (kW) 48 48 48 48COP 6.8 6.8 7.6 7.6

Power consumption (W) 7050 7050 6316 6316

approximated as linearly proportional to the temperature dif-ference between supply air and return air (∆t) [2]. Therefore,the load ratio of two air conditioners is equal to the ratio oftemperature difference ∆t of each air conditioner. We assumethat the supply air temperature and return air temperatureare equal to the average temperature of the supply side andthe return side of the air conditioner, respectively. We modelCOP of the air conditioners by quadratic least squares fit forthe result of a Gaussian process with experimental data. Weobtained the relation of supply air temperature T and COP ϵwith a 48 kW load as shown in Eq. (2). The calculated relationsfor the air conditioner between the supply air temperature,return air temperature, temperature difference of supply airand return air, load, COP, and power consumption are shownin Table I.

ϵ = −0.0171T 2 + 1.09T − 8.07 (2)

Power consumption by the air conditioners in the initialstate is 14.1 kW, and that in the raised state is 12.6 kW.

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Fig. 7. Back-plane temperatures of each server block in tandem arrangement

Therefore, the power consumption by air conditioners in theconventional arrangement is reduced by 10.4% as a result ofusing the proposed workload assignment procedure.

B. Evaluation of the tandem arrangement

In the tandem arrangement data center model, the work-loads were assigned in the same manner as in the conven-tional arrangement described above. The maximum servertemperature reaches 50 ◦C when all servers execute the samenumber of workloads with the temperature set point of airconditioner A at 24.1 ◦C and that of air conditioner B at37.1 ◦C, and the volumetric airflow of each air conditionerat 2.1 m3/s. We call this state the initial state. The maximumserver block temperature after the procedure is 48.4 ◦C. Weraised temperature the set point of each air conditioner by1.6 ◦C, which increased the maximum server temperature to50 ◦C. We call this state the raised state. Figs. 7(a) and 7(b)show the sever temperature of each server in the initial stateand the raised state, respectively. Also, Figs. 8(a) and 8(b)show the simulation results of the back-plane temperature ofeach server block in row B in the initial state and in the raisedstate, respectively.

Table II shows the typical relations for the air conditionerbetween the supply air temperature, return air temperature,temperature difference of the supply air and return air, load,COP, and power consumption for each air conditioner inthe initial and raised states. The relations were obtained byquadratic least squares fitting of data obtained from experi-ments on supply air temperature T and COP ϵ with 43 kW

Temperature (°C)

(a) Initial state

Temperature (°C)

(b) Raised state

Fig. 8. Simulation results of rack B back-plane temperature

TABLE II. INFORMATION ON AIR CONDITIONERS IN TANDEMARRANGEMENT

initial state raised stateSide A Side B Side A Side B

Supply air temperature 24.1 37.1 25.7 38.7Return air temperature 37.2 47.7 38.8 49.3Temperature difference 13.1 10.6 13.1 10.6

Load (kW) 53 43 53 43COP 8.0 7.9 8.4 7.4

Power consumption (W) 5377 5444 5091 5845

load. This results in Eq. (3); with 53 kW load, Eq. (4) isobtained instead.

ϵ=−0.0203T 2 + 1.20T − 8.68 (3)ϵ=−0.0137T 2 + 0.962T − 7.23 (4)

When the exhaust heat from the servers is reused, thepower consumption of the air conditioner is decreased, as isthe total power consumption of the data center. In this study,we assumed that the reuse efficiency of a desiccant-type reusesystem is 20% when the exhaust heat temperature from theservers is higher than 48 ◦C. The total power consumption inthe data center as a function of return air temperature of airconditioner B and exhaust heat temperature is shown in Fig. 9.The vertical axis represents the return air temperature when theheat-tolerant servers are at 50 ◦C.

In the data center with a tandem arrangement, powerconsumption by the air conditioners is reduced by 53% throughreusing exhaust heat. Moreover, by including the effect ofexhaust heat reuse, we will be able to reduce total powerconsumption in the data center by 23%.

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Fig. 9. Effect of exhaust heat reuse system

IV. DISCUSSION

A. Power consumption by server fans

The power consumption by servers fan is proportional tothe cube of fan speed, and modern servers control fan speedaccording to the ambient and CPU temperatures as a meansof reducing power consumption [10]. In this evaluation, weset average power consumption of 400 servers as two-thirdsof total power consumption. Therefore, we suppose that fanpower consumption depends on CPU temperature only andtake the total power consumption by fans as fixed. We reducethe power consumption by the air conditioners and improve theefficiency of the exhaust heat reuse by raising the temperatureset point of the air conditioners. However, this also increasesfan speed and increases the power consumption by fans. Wemust consider this trade-off more precisely in future works.

B. Energy proportionality and workload consolidation onservers

The CPU utilization and the marginal power consumptionby servers are proportional to one another, and servers consumea certain amount of power even when the server is idle [5].As a result, methods that concentrate server workload and putidle servers into a sleep or powered-off mode are commonlyproposed. However, except in low-volume cases such as forserver rooms, these methods are not applied in typical datacenters because of the operational risk of having insufficientcapacity. Therefore, we did not consider methods of reducingpower consumption by concentrating workload and turning offidle servers.

C. Use case of workload relocation

In this paper, we used a workload swapping procedure toreduce air conditioner power consumption. In some data cen-ters, however, dynamic workload relocation between serversis undesirable because dynamic relocation obviously increasesthe risk of service interruption. A proactive assignment pro-cedure exhibits promise for decreasing power consumption bydata centers. In such a procedure, workloads would be assignedon the basis of estimated temperatures and loads, which wouldallow operation with no unusual risk.

V. CONCLUSION

In this paper, we proposed a workload assignment policythat is intended to reduce the power consumption by air con-

ditioners in two different data center equipment arrangements.One arrangement is the conventional arrangement, with a coldaisle and two hot aisles; the other is a tandem arrangementwith a cold aisle, a hot aisle and a super-hot aisle, which allowsreusing the exhaust heat from the servers. We simulated thetemperature distribution by computational fluid dynamics andestimated COP of air conditioners by a Gaussian process. TheCOP increases with the temperature set point. The narrowingof the distribution of exhaust heat from servers enables raisingthe temperature set point of air conditioners, and the powerconsumed by them is thereby reduced for both the conventionaland tandem arrangements of data centers.

As a result, the power consumption by air conditionerswas reduced by 10.4% for the conventionally arranged datacenter. For the data center with a tandem arrangement, theair conditioner power consumption was reduced by 53%,and the total power consumption of the whole data centerwas reduced by 23% through reusing exhaust heat from theservers. Moreover, this policy has high potential not only fordynamic workload-shifting according to workload, but also forproactive workload assignment on the basis of workload trendestimations at the data center. We will apply a machine learningprocedure to optimize the workload assignment in our futurework.

ACKNOWLEDGMENTS

This work was supported by the CO2 emission reductionproject of the Ministry of the Environment, Japan. We thankMr. Naoki Aizawa at Takasago Thermal Engineering for sup-port on air conditioning techniques and aisle arrangement. Wethank Dr. Tadahisa Kondo, Dr. Akira Utsumi, and Dr. YoichiOtubo at Advanced Telecommunications Research InstituteInternational for their help in obtaining experimental data.

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