Boosting Service Availability for Base Stations of CellularNetworks by Event-driven Battery Profiling

Xiaoyi FanSchool of Computing Science

Simon Fraser UniversityBurnaby, BC, Canada

xiaoyif@sfu.ca

Feng WangDepartment of Computer and

Information ScienceThe University of Mississippi

University, MS, USA

fwang@cs.olemiss.edu

Jiangchuan LiuSchool of Computing Science

Simon Fraser UniversityBurnaby, BC, Canada

jcliu@cs.sfu.ca

ABSTRACTThe 3G/4G cellular networks as well as the emerging 5Ghave led to an explosive growth on mobile services across theglobal markets. Massive base stations have been deployedto satisfy the demands on service quality and coverage, andtheir quantity is only growing in the foreseeable future. Giv-en the many more base stations deployed in remote rural ar-eas, maintenance for high service availability becomes quitechallenging. In particular, they can suffer from frequen-t power outages. After such disasters as hurricanes or s-now storms, power recovery can often take several days oreven weeks, during which a backup battery becomes the onlypower source. Although power outage is rare in metropoli-tan areas, backup batteries are still necessary for base sta-tions as any service interruption there can cause unafforablelosses. Given that the backup battery group installed on abase station is usually the only power source during poweroutages, the working condition of the battery group there-fore has a critical impact on the service availability of a basestation. In this paper, we conduct a systematical analysison a real world dataset collected from the battery groupsinstalled on the base stations of China Mobile Ltd co., andwe propose an event-driven battery profiling approach toprecisely extract the features that cause the working con-dition degradation of the battery group. We formulate theprediction models for both battery voltage and lifetime andpropose a series of solutions to yield accurate outputs. Byreal world trace-driven evaluations, we demonstrate that ourapproach can boost the cellular network service availabilitywith an improvement of up to 18.09%.

1. INTRODUCTIONThe deep penetration of cellular mobile wireless technolo-

gies have made anytime anywhere communications becomemore available than ever. Indeed, with the wide deploymentof 4G network as well as the emerging of 5G, mobile applica-tions are experiencing an explosion while the bandwidth ofcellular network becomes higher than ever. To afford suchgreat demands, in the cellular network infrastructure, moreand more base stations have been strategically construct-ed and deployed to satisfy the service coverage and quality(e.g., bandwidth) requirements. The base stations are thenaggregated into the carrier network through the backhaul

Copyright is held by author/owner(s).

infrastructure by fiber lines or microwave links. However,this renders that the network service downtime will cascadeto all the dependent base stations if any part experiencespower outages [1]. In particular, in the rural areas or duringsevere weather conditions such as hurricanes or snow storm-s, the power recovery may take several days or even weeksdepending on the difficulty to reach the repairing area.

Lead-Acid

Battery Group

Diesel

Generator

DC

Distribution

Air

Conditioner

Misc

Loads

Rectifier

ACDC

Monitoring

SwitchUtility

Grid

Data

Transmission

Figure 1: The power sources and loads in the cellular net-work base stations

To improve the service availability, besides connecting tothe utility grids, each cellular tower is also equipped with abattery group as illustrated in Fig. 1. When a power outagehappens in the utility grid, to avoid any service interrup-tion, the battery group discharges to support the communi-cation equipment until the generator is delivered to provideenough power supply. This makes the battery group and itsworking condition play a critical role. During this period,if the condition of the battery group deteriorates, a poweroutage can easily take down the service. The emergency re-pairing service arrives may take several days or even weeksdepending on the difficulty to reach and locate the repair-ing section, especially in rural areas or during severe weath-er. Thus, being able to predict the condition of the batterygroup is of immense technical and commercial importancefor system maintenance, so that battery replacement canbe planned ahead of failure to minimize the interruptionsof service availability. A successful prediction requires notonly the knowledge of the battery ageing processes, but also

a good understanding of the stress events that may induceand accelerate the aging process.

To tackle those issues, we collaborate with China MobileLtd co., which is the world’s largest mobile phone operatorin term of subscriber and currently with about 806 millionusers. We have collected 42,527 equipment data with totally1,313,533,557 rows from July 28th, 2014 to July 31st, 2015including 105 categories of events, which are further pro-cessed and analyzed by our cloud computing platform withHadoop 1.2.1 and Hive 1.2.1. Based on the data analysis,we propose an event-driven battery profiling approach toprecisely predict the battery group working conditions andschedule maintenance accordingly, so as to guarantee thehigh service availability for the cellular networks. We for-mulate the prediction models for both battery voltage andlifetime and propose a series of solutions to yield accurateoutputs. By real world trace-driven evaluations, we demon-strate that our approach can precisely predict the batteryvoltage and lifetime with the RMS error no more than 0.01v, which can further the improves the service availability ofthe cellular towers up to 18.09%.

The rest of the paper is organized as follows. Section 2provides our detailed observations on the battery properties.Section 3 presents our approach to efficiently solve the prob-lem. Section 4 discusses the performance evaluation resultson our approach. We provide a literature review in Section 5and we conclude this paper in Section 6.

2. MOTIVATION AND DATA ANALYSISTo better understand the battery working condition and

its deterioration process, we have closely worked with theengineers from China Mobile to collect the dataset for ourstudy. In this section, we first describe the collected datasetand then discuss our observations for aging profiling in timeseries battery data.

2.1 BackgroundWe explain the details of power supply in the cellular net-

work base stations, as Fig. 1 shows. The equipment in basestations is supported by the utility grid, where the batterygroup is installed as the backup power. In case that the u-tility grid interrupts, the battery discharges to support thecommunication switching equipment during the period ofthe power outage. As Fig. 2(a) shows, a battery group inthe cellular network base station contains 24 cell batteries.In Fig. 2(b), the monitoring system has sensors on each cel-l of the battery group and periodically collects the voltageand events in both normal and abnormal situations. Whenthe monitoring system reports the alert event, e.g. the pow-er outage, the emergency repairing service is scheduled de-pending on the accident severity. Since few base stationshave the diesel generators permanently installed on site, theengineers have to drive the emergency diesel generator toprovide the power, which can take time to arrive at the site.The power outage can occur frequently and severely in therural areas and developing countries due to the unstableutility grid. To make it even worse, the construction of in-frastructure often makes that the base stations are difficultto reach, e.g. slippery rock trails in the mountains, wherethe workers have to manually carry the heavy generators tothe site. As a result, the cellular carriers may have to tradeoff between the cost and the quality of service, so that theyeven abandon the base stations in the tough surroundings

until the utility grid is restored. On the other hand, con-sidering the labor costs for the mobile telecom carries, theperiodical maintenance for the battery group is generally oflong intervals, which further exaggerates the possibilities ofbattery accidents during the outage of utility grid. If the re-pairing engineers cannot arrive at the site before the batterygroup is exhausted, the availability of the base stations can-not be guaranteed. Thus prediction for the lifetime of bat-tery group is meaningful for the service availability, whichis helpful for maintenance engineers to solve the potentialissues in advance during the periodical maintenance.

(a) Two battery groupswith 24 cells

(b) The cable of monitoringsystems

Figure 2: The battery group and monitoring systems

Despite the Li-ion and NiCd batteries demonstrate thelatest development in battery technology due to their s-maller size, lower weight and better storage efficiency, ma-jor drawbacks of these types of batteries are the high cost.Lead-acid batteries in Fig. 2(a) have large capacities andthus have been widely used for storage in backup powersupplies in base stations. The aging mechanism of Li-ionbatteries attracts many efforts [2], where the frequent ac-tivities of Li-ion batteries produce lots of log and providepossibilities to measure the battery working conditions. Yetthe lead-acid batteries in base stations normally keep in thefloat-charging status, where float-charging status representsthat a battery maintains the capacity by compensating forself-discharge after being fully charged. The monitoring sys-tem collects the float voltage from the float-charging batter-ies twice per day, which makes the dataset consideratelysparse. Therefore extracting the features from such a sparsedata source and predicting the working conditions of leadacid batteries pose many challenges and here we take thefirst attempt to tackle these issues.

2.2 Data AnalysisThe log data that we have collected is from July 28th,

2014 to July 31st, 2015. In our dataset, we have identified105 categories of event codes and obtained 42,527 equipmentdata with totally 531 tables and 1,313,533,557 rows.

2.2.1 Voltage Readings of BatteriesThe voltage of each cell battery is the most important

feature that we have measured, as it reflects the power out-put pattern of the battery. In general, we have observedtwo representative kinds of cell batteries, where we manu-ally choose 1578 batteries as the newly-installed group andput 1459 batteries into the nearly-dead group depending onthe repair records. As mentioned, there are 24 cell batter-ies in one battery group, where the rated voltage of cell isaround 2.23v and the rated voltage of battery group is 53.5v.Based on this, we further analyze the typical status of the

July Sep Nov Jan Mar May July

Date

1.2

1.4

1.6

1.8

2

2.2

2.4T

he m

ean

of d

aily

vol

tage

(V

)GoodPoor

Figure 3: Mean voltage versus battery status

voltage patterns inside the two representative cell batterycategories. Fig. 3 shows the significant differences betweenthe newly-installed and nearly-dead batteries.The blue sol-id line plots the mean voltage of newly-installed batteries,which judders between 2.21v and 2.25v. The red dotted lineshows the decay trend on the mean voltage of the nearly-dead batteries. There is a clear downward trend close to thefailure date, where the battery power frequently falls downand becomes quickly exhausted, causing many issues andalerts in the cellular network base station, which indicatesthat the voltage have strong correlations with the batterylife.

July Sep Nov Jan Mar May JulyDate

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

The

var

ianc

e of

dai

ly v

olta

ge (

V) Good

Poor

Figure 4: Voltage variances versus battery status

As Fig. 4 shows, the blue solid line represents the newly-installed battery can output a steady power and the varianceof the voltage keeps very close to zero. The red dotted lineillustrates that the variance of the nearly-dead batteries in-creases much faster than the newly-installed batteries. Fig. 4illustrates that the voltage variance also has the correlationwith the length of remaining lifetime. The variance of theoutput voltage from the batteries over time also reflects the

aging trend of battery quality degradation. These obser-vations motivate our battery working condition predictionbased on the battery historical voltages.

2.2.2 Battery Events

0 20 40 60 80 100

Categories

0

5

10

15

Fre

quen

cy

#105

Figure 5: Distribution of battery events categories

We perform an analysis on the battery events in the logsto explore their potential relationship with the battery work-ing conditions. Fig. 5 also shows the frequency distributionamong all the 105 categories. We can see that the distribu-tion is highly skewed: the most popular category is Alert,at about 28.09%, the second is Battery premature failure,at about 20.42%; and the third is Discharging, at about10.70%.

0 50 100 150 200 250

Average number of events per day

0

100

200

300

400

Rem

aini

ng li

fetim

e

Figure 6: Correlation between the remaining life and thenumber of low float voltage events

We take three events as example to further investigate thecorrelations between events and battery remaining lifetime,which are Alert to low float voltage, Discharge and fault cellshown in Fig. 6, 7 and 8, respectively. We count the specificevents number for each battery until the batteries are re-

0 1 2 3Average number of events per day#104

0

100

200

300

400R

emai

ning

life

time

Figure 7: Correlation between the remaining life and thenumber of discharge events

placed, and pick up the top-30 batteries with the maximumnumber of events. From July 28th, 2014 to the end of July2015, there are 366 days in our dataset, where the remaininglifetime of most batteries in our dataset is longer than 366days, therefore dash lines represent that those batteries onit have longer remaining lifetime than 366 days. Fig. 6 and 7plot the correlation between Alert to low float voltage, Dis-charge and remaining lifetime. This clearly demonstratesthat there exists a strong correlation between battery re-maining lifetime and Alert to low float voltage, as well asbetween battery remaining lifetime and Discharge. We fur-ther plot the failure rates against the number of fault cellevents in the system in Fig. 8, which does not show anynoticeable correlation between them. This implies that thefailure is affected by some specific events. The observation-s suggest that the diverse events have different influenceson the battery working conditions, thus it is necessary todiscriminatingly differentiate these events for the accurateprediction.

0 100 200 300

Average number of events per day

0

100

200

300

400

Rem

aini

ng li

fetim

e

Figure 8: Correlation between the remaining life and thenumber of fault cell events

3. SOLUTION DESCRIPTIONThe battery working conditions can be predicted by it-

s relevant historical voltage records and event logs, whichmotivates us and serves as the basis for the prediction of bat-tery remaining lifetime. Although the monitoring systemscollects sufficient messages from batteries with the voltageand events data, there still remain several challenges to ac-curately predict the battery lifetime, especially when thereare massive events in logs with some noises existing. More-over, our observations suggest that the historical events arecorrelated with the battery working conditions, yet singleevent record is not a reliable factor for the prediction. Thusour approach filters out a large amount of noise and onlypulls the relevant data from the database, including the his-torical float voltage and meaningful events, as mentioned inprior section, e.g., we only select the float voltage from thetable named historybattery. Let V = {v1,v2, ...,vN} denotethe float voltage set and E = {e1, e2, ..., eN} represent theevent set, where N is the number of batteries. Each volt-age records vi = {v1i , v2i , ..., vti , ...} is a sequence of voltagerecords in time series for battery i, where each vti is the meanvoltage value of battery i in time t and the time interval isone day. Similarly, let ei = {e1i , e2i , ..., emi

i } represent theevent set of battery i, which has mi events. Our goal is toprofile the aging trends for the battery working conditions,and predict the battery remaining lifetime. Given the bat-tery data with historical events E and float voltage V in timeseries, we can predict the future voltage and then estimatethe battery remaining lifetime.

To achieve this, we extract the aging trend via from a giv-en time series vi. We partition the voltage records vi intoK segments, and use the polynomial regression to fit eachsegment, where each voltage segment k having the initialvoltage vkia and the slope of line with value ski to represen-t the voltage aging trend. To ensure that the trend via ismonotonic, we can simply write the constraint as vkia ≥ vk+1

ia .Given the aging trend via = {(v1ia, s1i ), (v2ia, s

2i ), ...}, we pro-

pose a concept of penalty pki to represent the influences ofa group of events on the voltage aging trend, which can bedefined as pki = ski −sk−1

i . Then we utilize the learning algo-rithm with events data E to predict the penalty value pi onthe voltage aging trend via. Based on the predictive voltagein coming months, we can estimate the battery remaininglifetime. To this end, we further transform the problem intoa Multi-Instance Multi-labels Learning [3] problem.

Let E denote the instance space for the historical events,and P define the set of penalty labels. We denote thetraining data by {(e1,p1), (e2,p2), ..., (eN,pN)} that con-sist of N examples, where a set of events ei is called abag in the MIML model and has mi instances, i.e., ei ={e1i , e2i , ..., emi

i }. A set of penalty labels pi is associated with

the bag pi, where each pji ∈ pi is one possible penalty valueled by these events ei. In particular, each battery grouphas 24 cells with different voltage aging trend, where pi ={p1i , p2i , ..., p24i } is a subset of all possible labels and pi ∈ P.Then the objective is to learn a function fMIML : 2E → 2P

from a given data set {(e1,p1), (e2,p2), ..., (eN,pN)} to ac-curately predict the penalty label pi for the bag ei.

Instead of receiving a set of independent labeled instancesas in standard classification, MIML model receives a set ofbags {e1, e2, ...} which are labeled with the penalty values{p1,p2, ...}. Given bags obtained from different batteries atdifferent dates, the goal is to build a classifier that will la-

bel other bags correctly. Based on the MIML algorithm, wepropose a modified approach with an effective approxima-tion of the original MIML problem. Specifically, to utilizethe relations among multiple labels, our approach first learna shared space for all the labels from the original features,and then trains label specific linear models from the sharedspace. To make the learning efficient, stochastic gradient de-scent is used to optimize an approximated ranking loss. Intesting phase, our approach returns a subset of all possiblelabels with the prediction value, and we obtain the penal-ty label for each bag by selecting the one with the largestprediction value.

With the domain knowledge and our observations that thevoltage is the criterion of the battery condition, the remain-ing lifetime can be easily predict with the voltage thresholdT for pre-defined battery failure. We say the working con-dition of battery i is good, when the float voltage value vi ishigher than pre-defined threshold T . With the aging trend(vkia, s

ki ) and penalty pk of the voltage in time segment k,

the battery remaining lifetime lki for the battery i at time

segment k can be calculated as lki =vkia−T

ski +pki.

4. EXPERIMENTSIn this section, we evaluate the prediction accuracy of

the event-driven battery profiling approach using real-worldtrace collected from China Mobile Ltd Co and demonstratethe performance in estimating the remaining battery life-time. To evaluate the prediction quality, we run experimenton the real-world data with 2673 batteries data, 2082 exam-ples in the training set and 593 examples in the test set.

10 20 30 40 50 600

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Days

RM

S E

rror

(v)

ArimaMIMLLinearWavelets

Figure 9: Root-mean-square Errors

Then we compute the root-mean-square error between thepredictive and actual trends. We compare our method withARIMA [5], Linear Regression [6] and wavelets [7], as Fig 9shows. We can see that our method performs best among themajority of the compared schemes with the smallest root-mean-square errors, where our approach can precisely pre-dict the battery voltage with the RMS error less than 0.01 v.Moreover, the aging trend extracted by our scheme is mono-tonic and satisfies the nature of the aging behavior, whileLR, ARIMA, or Wavelets does not have such advantage asthey have no monotonic constraint in extracting the voltage

aging trend.

0 16 48 80 112 144 176 208 2400

20

40

60

80

100

Days

Per

cent

age

(%)

Figure 10: Survival days after our failure alert

As the battery replacement is the consequence of a bat-tery failure [4], we choose 112 batteries with the replace-ment records in the log to verify the prediction accuracy forremaining lifetime. We count the survival days for each bat-tery after the failure alert. A plot of the relation betweenthe percentage and the survival days after our failure alertis shown in Fig. 10. We can see that 87% batteries are re-placed in three months after the alters, which demonstratesour approach is a strong predictor for battery working con-ditions. We also observe that there are still a small numberof batteries not replaced after our failure alerts. We have aclose look at those batteries, and find out there are redun-dant battery groups in their cellular network base stations.Therefore repairing engineers postponed the maintenanceservice for those batteries, which also demonstrates our bat-tery profiling approach can help the maintenance engineersto detect the potential issues in the battery groups.

0 100 200 3000.5

0.6

0.7

0.8

0.9

1

Days

Qua

lity

of s

ervi

ce

MIMLReal Trace

Figure 11: Service Availability

Fig. 11 plots the service availability of the base stationsin the case of the power outage. We assume that the bat-tery will be replaced after the failure alert and the voltagegoes back to the normal level. The result demonstrates thatthe event-driven battery profiling approach together withour proposed battery maintenance and replacement scheme,

can boost the cellular network service availability with animprovement of up to 18.09%.

5. RELATED WORKThe time series research has attracted significant efforts

in recent decades. In this section, we highlight some rele-vant techniques. The most intuitive approach is smoothing,filtering and prediction, which can be done using differen-t techniques, including ARIMA [5], Linear Regression [6],wavelets [7] and SSA [8]. Luo et al. [9] presented an ap-proach to find the correlation between actual events andtime series in order to diagnose incidents. Their approachmatched the events with certain subsequences of time seriesin order to give a real explanation of the time series shape.Xu et al. [10] [11] combined textual messages in console logsto construct performance features and conducted the Prin-cipal Component Analysis (PCA) [12] to detect anomalies.Makanju et al. [13] proposed IPLoM by creating event de-scriptions based on clustering text messages in the logs, butinterpreting them requires unavailable domain knowledge.Predicting failures on a data stream processing system [14]could be done by observing system’s status, which uses anensemble of decision tree classifiers and is applicable in anonline setting. Finite state automata [15] can be used tomodel sequential dependencies between messages and de-tect anomalies. Most recently, Sipos et al. [4] consideredlog-based predictive analysis in order to monitor the con-ditions of the operating equipment, yet they ignored thepossibility of fine temporal patterns to simplify the model.Different with the prior approaches, our event-driven bat-tery profiling approach focuses on a large amount of batteryand utilizes not only the signal value but also the events datato comprehensively predict the battery working conditions.

6. CONCLUSIONIn this paper, we propose an event-driven battery profil-

ing approach to precisely extract the features that cause theworking condition degradation of the battery group. Weformulate the prediction models for both battery voltageand lifetime and propose a series of solutions to yield accu-rate outputs, which further enable us to propose a schemeto timely schedule battery maintenance and replacement tominimize the service interruptions caused by power outages.By real world trace-driven evaluations, we demonstrate thatour approach can achieve much higher prediction accuracyon the battery voltage and lifetime, which together withour proposed battery maintenance and replacement scheme,can boost the cellular network service availability with animprovement of up to 18.09%.

7. ACKNOWLEDGMENTSThis work is supported by a Canada NSERC Discovery

Grant, an NSERC Strategic Project Grant, and an NSERCE.W.R. Steacie Memorial Fellowship.

Feng Wang’s work is supported by a Start-up Grant fromthe University of Mississippi.

8. REFERENCES[1] W. Balshe, “Power system considerations for cell tower

applications,” Cummins Power Generation, 2011.

[2] J. Vetter, P. Novak, M. Wagner, C. Veit, K.-C. Moller,J. Besenhard, M. Winter, M. Wohlfahrt-Mehrens,C. Vogler, and A. Hammouche, “Ageing mechanismsin lithium-ion batteries,” Journal of power sources,vol. 147, no. 1, pp. 269–281, 2005.

[3] Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li,“Multi-instance multi-label learning,” arXiv preprintarXiv:0808.3231, 2008.

[4] R. Sipos, D. Fradkin, F. Moerchen, and Z. Wang,“Log-based predictive maintenance,” in Proceedings ofthe 20th ACM SIGKDD international conference onknowledge discovery and data mining, pp. 1867–1876,ACM, 2014.

[5] G. E. Box, G. M. Jenkins, and G. C. Reinsel, “Linearnonstationary models,” Time Series Analysis, FourthEdition, pp. 93–136, 1976.

[6] A. Charnes, E. Frome, and P.-L. Yu, “The equivalenceof generalized least squares and maximum likelihoodestimates in the exponential family,” Journal of theAmerican Statistical Association, vol. 71, no. 353,pp. 169–171, 1976.

[7] A. Grossmann and J. Morlet, “Decomposition ofhardy functions into square integrable wavelets ofconstant shape,” SIAM journal on mathematicalanalysis, vol. 15, no. 4, pp. 723–736, 1984.

[8] J. B. Elsner and A. A. Tsonis, Singular spectrumanalysis: a new tool in time series analysis. SpringerScience & Business Media, 2013.

[9] C. Luo, J.-G. Lou, Q. Lin, Q. Fu, R. Ding, D. Zhang,and Z. Wang, “Correlating events with time series forincident diagnosis,” in Proceedings of the 20th ACMSIGKDD international conference on Knowledgediscovery and data mining, pp. 1583–1592, ACM,2014.

[10] W. Xu, L. Huang, A. Fox, D. A. Patterson, and M. I.Jordan, “Mining console logs for large-scale systemproblem detection.,” SysML, vol. 8, pp. 4–4, 2008.

[11] W. Xu, L. Huang, A. Fox, D. Patterson, andM. Jordan, “Online system problem detection bymining patterns of console logs,” in Data Mining,2009. ICDM’09. Ninth IEEE International Conferenceon, pp. 588–597, IEEE, 2009.

[12] I. Jolliffe, Principal component analysis. Wiley OnlineLibrary, 2002.

[13] A. A. Makanju, A. N. Zincir-Heywood, and E. E.Milios, “Clustering event logs using iterativepartitioning,” in Proceedings of the 15th ACMSIGKDD international conference on Knowledgediscovery and data mining, pp. 1255–1264, ACM,2009.

[14] X. Gu, S. Papadimitriou, P. S. Yu, and S.-P. Chang,“Online failure forecast for fault-tolerant data streamprocessing,” in Data Engineering, 2008. ICDE 2008.IEEE 24th International Conference on,pp. 1388–1390, IEEE, 2008.

[15] Q. Fu, J.-G. Lou, Y. Wang, and J. Li, “Executionanomaly detection in distributed systems throughunstructured log analysis,” in Data Mining, 2009.ICDM’09. Ninth IEEE International Conference on,pp. 149–158, IEEE, 2009.