optimising resource costs of cloud computing for education

Future Generation Computer Systems ( )

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Future Generation Computer Systems

journal homepage: www.elsevier.com/locate/fgcs

Optimising resource costs of cloud computing for education

Fernando Koch a, Marcos D. Assuno b, Carlos Cardonha c, Marco A.S. Netto c,a SAMSUNG Research Institute, Brazilb INRIA, LIP, ENS de Lyon, Francec IBM Research, Brazil

h i g h l i g h t s

Context-aware algorithm for allocating computing resources for classrooms. Experiment setup based on real-world school data. Evaluation analysis considering security margin, costs, and QoS.

a r t i c l e i n f o

Article history:Received 22 April 2014Received in revised form11 March 2015Accepted 12 March 2015Available online xxxx

Keywords:Cloud computingEducationResource allocationQoS

a b s t r a c t

There is a growing interest around the utilisation of cloud computing in education. As organisations in-volved in the area typically face severe budget restrictions, there is a need for cost optimisation mecha-nisms that explore unique features of digital learning environments. In this work, we introduce a methodbased on Maximum Likelihood Estimation that considers heterogeneity of IT infrastructure in order todevise resource allocation plans that maximise platform utilisation for educational environments. Weperformed experiments using modelled datasets from real digital teaching solutions and obtained costreductions of up to 30%, compared with conservative resource allocation strategies.

2015 Elsevier B.V. All rights reserved.

1. Introduction

Digital teaching requires new methods to continuously evalu-ate student performance [1]. These methods revolve around col-lecting, classifying, and understanding events that happen duringin-classroom activities [2]. They require the instrumentation oflearning environments to generate multi-dimensional signals ca-pable to define key contextual elements. This approach gener-ates large amounts of data that need intense computing powerand storage. As a solution, Sclater envisions that the majority ofeducational services will be hosted in the cloud and institutionsno longer host their own data centres with expensive hardware,power bills, staff salaries and computing resourceswhich are rarelyfully utilised [3]. A challenge in this context is to balance resourcedemand, expected quality of services, and operational costs thusmaking the use of technology viable for the education environ-ment.

Corresponding author.E-mail address:[email protected] (M.A.S. Netto).

In terms of cloud computing, this means to minimise the num-ber of allocated resources subject to keeping quality of service atan acceptable level [4]. We claim that, given the unique featuresof digital education, one can devise mechanisms of resource allo-cation tailored for this domain. For instance, it is possible to es-timate the number of resources required by a classroom during aspecific class based on information such as features of the learningobjects in digital education material, number of students, and his-torical resource demand. Traditional methods, however, estimatethe peak usage and allocate resources considering a safety marginover theworst-case scenario; this over-allocation approach resultsin large andundesired resourcewaste. Thework presented byKochet al. compared different allocation strategies and evaluated theirexpenditures and impact upon Quality of Service (QoS) [5].

In order to optimally exploit the cost-effectiveness of cloudcomputing in education, we propose a probabilistic method thatallows fine-grained adjustments of load forecastmodels and henceenables significant cost reductions in a pay-as-you-go businessmodel. We consider the number of resources wc for delivering aclass c , a prime r of demand fluctuation based on limitations ofthe cloud infrastructure supporting activities in classroom r , and a

http://dx.doi.org/10.1016/j.future.2015.03.0130167-739X/ 2015 Elsevier B.V. All rights reserved.

2 F. Koch et al. / Future Generation Computer Systems ( )

prime of safety margin which is adjusted according to the con-fidence level of the estimations. Special care must be taken withsuch methods, though, as they bring larger risks to QoS. The re-search questions addressed in this paper are:

How to adjust prime r in order to optimise resource alloca-tion?

How to adjust prime to achieve acceptable QoS levels?To address these research questions, we constructed scenar-

ios based on real-world digital teaching initiatives. In particular,we evaluated these scenarios considering fluctuations of infras-tructure availability, which is typical in developing countries. Theproposed method is analysed via discrete-event simulations con-sidering various numbers of classrooms and using resource savingsand QoS violation as metrics.

In summary, the contributions of the paper are the following:

A probabilistic resource allocation method for educationalinstitutions to optimise their use of cloud resources;

An evaluation of themethod under several scenarios using databased on existing digital teaching initiatives.

The article is structured as follows. Section 2 introduces themo-tivation of this work by describing how resource demand behavesin a real world implementation of a digital teaching platform andpresents the formal description of the problem. Section 3 describesthe probabilistic algorithmused for resource allocation. Results of acomputational evaluation involving the proposed method are pre-sented in Section 4. Section 5 contains the description of relatedwork in the literature, and Section 6 presents our conclusion.

2. Motivation and problem description

Digital teaching provides means for instrumenting learning en-vironments and novel methods to collect, classify, and understandin-classroom learning activities. The workload of such systemsvaries over time depending on the context and elements compos-ing the delivered education material. For example, there are de-mand peaks throughout the delivery of a class when the materialcomprises videos, pictures, tests, screen sharing, and so forth.Moreover, fluctuations of network availability highly influence theflow of incoming requests, which leads to an undesired decreasein resource demand.When using resources from a cloud, these nu-ances must be considered when optimising allocation of resourcesin order to minimise cost and avoid waste.

The scenario considered here is that of a service provider oreducational organisation that needs to automatically allocate re-sources from a cloud to deliver education services required by aschool or university. We considered Samsung School solutions inthis article, a real world implementation of a digital teaching plat-form. The addressed problem can formally be defined as follows.Let R denote a set of classrooms and C denote a set of classes.We assume that all classes are presented in each classroom ex-actly once over T time-slots, so that we denote by Sr,t the class cbeing taught in classroom r at the t-th time-slot, 1 t T . Letus consider that there is a set of learning objects L(c) associated toclass c . Each object l is a media element of type m(l), where typein this context may refer to text, image, and video. Let us denotethe amount of resources consumed by learning objects of type mbyw(m). The sequence of events are as follows:

1. Educator signs into a classroom r at time-slot t and confirmsthat class c = Sr,t will be delivered.

2. Students located in r sign in and the applications running ontheir devices load the links to content in L(c).

3. Educator starts the class.4. Educator requests students to go to specific objects or pages, act

upon objects, respond to tests, watch videos, etc.

5. Students react to educators command in heterogeneousways, depending on the behaviour of the cloud infrastructuresupportingmaterial delivery in r and their level of engagement.

6. The cycle loops to Step 4 until the class ends (i.e., until the endof time-slot t).

7. Applications upload log files reporting all activities during theclass.

8. Students and educators are prepared to start activities sched-uled for time-slot t + 1.

9. The cycle loops to Step 1, if more classes exist.

It is clear that peak load can happen at distinct points, such aswhen the applications load thematerial (Step 2),when students actupon the content (Step 5), and when the application uploads thelog files for processing (Step 7). Thus, the maximum resource de-mand per student throughout class c is roughly maxlL(c)w(m(l)),and if the number of students located in classroom r is n N, thenthe maximum resource demand of c is given by

wc = nmaxlL(c)

w(m(l)).

Onemay infer c from r and t given S. Wewill usewr,t andwc inter-changeablywhenever c = Sr,t .We remark thatmaximumresourcedemand wc is achieved if all students access the most resource-demanding learning content simultaneously.

Ideally, each class c is expected to have maximum demand wc ,independently from the classroom where it is being presented.However, as classrooms may be located in different regions and,consequently, subject to different IT infrastructure, deviations onwc mayoccur. For instance, in placeswhere data transmission is in-efficient, students may not be able to access the content smoothly.In these situations, allocated resources may be underused.

The goal of an optimum resource allocation method is tomaximise system utilisation while delivering good QoS, whereQoS is harmed whenever the number of allocated resources isinsufficient for the load requirements.

We propose a method that considers usage variations causedby cloud infrastructure issues to reduce over-allocation and setadequate safety margins that reduce risk of QoS degradation.This technique is specially useful in emerging countries such asBrazil, where fluctuations of data communication availability is acommon reality; moreover, a successful implementation of thistechnique will rationalise the cost factor around cloud computingfor education.

3. Probabilistic workload-aware dynamic resource allocation

Letwr,t = wc denote the actual resource consumption demandof class c = Sr,t for classroom r , as explained in Section2. This valuecan be smaller thanwc in cases where the underlying IT infrastruc-ture r does not deliver optimal service. That is, quality of infras-tructure influences directly upon resource utilisation. This scenarioemerges in schools with poor Internet connection, as execution ofspecific content may be impacted by the slow communicationthus students give up fromplaying the content, consequently curb-ing the data load. Moreover, for each time-slot t , let

wt =rR

wr,t

denote the total expected number of resources and

wt =rR

wr,t

denote the actual resource demand at t .We assume that deviations on wc for each class c presented in

classroom r are given by a multiplicative factor r drawn from aGaussian distribution N (r , 2r ) whose values are truncated on

F. Koch et al. / Future Generation Computer Systems ( ) 3

the interval [0, 1], that is, wr,c = rwc . Parameters r and 2r de-pend only on the classroom r , i.e., we assume that the variations aredependent exclusively on the classrooms, and not on the classes.

We extend the algorithm presented in our previous work byconsidering values r for the estimation of the required number ofresources from the cloud [5]. In real-world settings, it is typicallynot possible to know a priori the values of r and 2r , so we em-ploy Maximum Likelihood Estimation (MLE) to compute the valuesof these parameters for each r and for each time-slot t based on theactual values of that have been observed in previous time-slots.Namely, our approach guesses a resource consumption valuewr,t for each pair (r, t), so it assumes that the total allocation willbe given by

wt =rR

wr,t .

The algorithm also employs a safety margin for resource alloca-tion, which changes over time according to the quality of forecastresults but never goes below a given constant (e.g., = 1.3represents a minimum margin of 30%). Therefore, the actual allo-cation will be given by twt , where t denotes the current valueof at time-slot t .

The pseudo-code of the resource allocation algorithm ispresented in Algorithm 1. As input data, it receives parametersR,C, S, and T and as output it delivers aQoS violation value Q , whichis given by

Q = 1T

t[1,T ]

max(wt twt , 0)wt

.

Q can be interpreted as follows: in each time-slot t , if twt < wt ,the number of resources is insufficient, so a fraction of the students,given by (wt twt )/wt , will suffer with bad QoS. The value of Qis the sum of these fractions for every time-slot t in [1, T ] dividedby T , so it is equal to the average percentage of students that willreceive bad QoS per time-slot.

Variables Q and are initialised with 0 and 0.2, respectively(Lines 1 and 2). Moreover, the algorithm maintains a list of lists that, in each time-step t , will be given by r = {r,1, r,2, . . . ,r,t1}, with

r,t =wSr,twSr,t

for 1 t < T . For each r , r is initialised as an empty list (Lines3-4).

Algorithm 1 main loop iterates over each time-step t (Lines617). Initially, it obtains an estimation of resource demand usingAlgorithm 2, described below (Line 7). Afterwards, it computesthe actual resource demands (Lines 8 and 10) and updates byappending r,t to each list r (Line 10). Once the difference between the estimated and the actual resources demand has beencomputed (Line 12), the algorithm adjusts the value of accordingto the progression of this error. Namely, if the error in t is greaterthan 0, becomes the maximum between (1.0 s) , s 0, and (Line 14). Both s and s are constants that contain the value 0.005.Conversely, if it is smaller than 0, is multiplied by (1.0 + s) andQ is incremented by/(wtT ) (Lines 16 and 17). Finally, after theend of the loop, Q is returned.

Algorithm 2 is used to estimate the number of required re-sources. As input data, it receives the set R of classrooms, the setC of classes, the schedule S, the time-slot t for which the alloca-tion is being performed, and . Initially, it sets wt to zero (Line1). Then, for each classroom r (Lines 26), the algorithm retrievesthe class c that is going to be presented at r in time-step t (Line 3).Afterwards, it estimates the values of parametersr and rR charac-terising the normal distribution that describes r using MLE on r

Algorithm 1: The proposed resource allocation algorithm.Input:R, C, S, TOutput: QoS estimation

1 0.2;2 Q 0 ;3 = // administrator specified minimum safety margin (e.g1.3);

4 for r inR do5 r = [];6 for t in [1, T ] do7 wt = Algorithm 2(R,C, S, t, );8 wt 0;9 for r inR do

10 wt wt + wr,t ;11 r .append(r,t);12 = wt wt ;13 if > 0 then14 max((1.0 s), );15 else16 (1.0+ s);17 Q Q /wtT ;18 return Q ;

Algorithm 2: Estimation of resources demand.Input:R, C, S, time-slot t , Output: Numberwt of resources to be allocated

1 wt 0;2 for r inR do3 c = Sr,t ;4 r ,

2r MLE(r);

5 r = draw(N (r , 2r ));6 wt wt + wcr ;7 returnwt ;

(Line 4). Oncer and 2r have been computed, the algorithmdrawsa value r from N (r , 2r ) (Line 5) and w

t is incremented by the

product of the estimated resource consumptionwc and r (Line 6).For a class r for instance, if r = 0.7 and the maximum numberof resources for delivering the class wc = 100, then wt is incre-mented by 70 resources. After the end of themain loop, themethodreturns the numberwt of resources required in time-slot t (Line 7).

We assume that the behaviour of the IT infrastructure is stablealong the delivery of material in each classroom r . That is, r and 2r do not change over time. We remark that the method can beeasily adapted to situations where this assumption does not holdby simply restricting the application of MLE to the last values ofvector r , eliminating hence the influence of valueswc/wc that donot reflect the current distribution.

4. Performance evaluation

4.1. Experimental setup and metrics

In order to perform experiments in a controlled and repeatablemanner we generated a set of synthetic workloads that resemblethe log files created by the digital teaching problem described inSection 2.We consider a number of classrooms, with each having amaximum number of students who attend a number of classes perday. As detailed later, a generated workload contains the hourlydemand for number of resources for each classroomover a numberof days.


We associate the conduction of each class in a classroom to atime-step in T , so an iteration of Algorithm 1s main loop (Lines516) is executed before all classes taking place in the associatedtime-step. In the beginning of each iteration, Algorithm2 estimatesthe number of resources that will be required by the set of classesbeing conducted in the associated time-step. Estimations made inAlgorithm2, safetymargin , andQoS violation are updated in eachiteration according to actual demands registered in previous steps.

To emulate the difficulty that students located in a given class-roommay experience while accessing the educational content dueto infrastructure issues, we sort parameters r [0.5, 1.0] and 2r [0.01, 0.2] uniformly for each classroom r . Then, in eachtime-slot t , we drawr,t from the Gaussian distributionN (r , 2r )truncated at the interval [0, 1] and multiply it by the maximumnumber of students in the class [6]. If r = 0.8, only 80% of thestudents in the class are able to properly access the educationalcontent.

Wegenerate the set of classesC considering the average contentused by Samsung School solutions in Brazil.We vary the number ofclasses rather than subject areas and teacher behaviour in order toenable a more fair comparison of the allocation models. Each sub-ject consists of a number of pages uniformly distributed between18 and 25; each page is accessed at a specific time during class andcontains from 6 to 12 learning objects, which can be text, image,or video. Typically, approximately 20% of the learning objects aretext, 30% are images, and 50% are videos. In our experiments, wepurposefully extended the amount of video considering new ped-agogical models being applied in digital teaching solutions and thefact that this type of learning object constitutes the de facto stan-dard of add-on support material. For the sake of this analysis, letus consider that the objects sizes are pre-established: texts are be-tween 2 and 8 kB; images are between 180 and 1.5 MB, and videosare between 6 and 15 MB. These numbers are aligned with actualparameters observable in Samsung School solutions. Peak load iscomputed based on (i) the number of students able to access thecontent during a class, and (ii) the number of HTTP requests (i.e.,HTTP chunks) required to transfer the content into the studentsdevices. The peak parameter is applied to determine themaximumresource demand of the class.

We evaluate two performance metrics to determine the pro-vided QoS levels and the resource savings achieved by the pro-posed allocation method:

Resource saving: the ratio between the amount of resources al-located by the algorithm, given by

t[1,T ]wt , and

the maximum estimated amount of resources, given byt[1,T ]wt .

QoS violations: the value Q defined in Section 3.

Finally, for the sake of comparability,we remark that conserva-tive approaches allocate resources according to

t[1,T ]wt (pos-

sibly with an additional safety margin) and, therefore, typicallyhave Q = 0, since they avoid situations with insufficient resourceallocation.

4.2. Results and analysis

We evaluate the performance of the proposed allocationmethod by considering scenarios with various numbers of class-rooms. In the first set of experiments |R| varies from1 to 301 (withstep size 10) and different values for the minimum safety margin are considered. We argued in our previous work that a safetymargin of 30% (i.e., = 1.3) is commonplace in the industry [5],and therefore we included it for comparison. Note that the marginis necessary not only because of possible errors with predictions,but also because Cloud resources may not offer a steady perfor-mance [7].

Fig. 1. Resource savings under various numbers of classrooms ( 2r [0.01, 0.2]);where is the minimum resource safety margin.

Fig. 1 shows resource savings under various numbers of class-rooms and values of , where the savings are computed as the dif-ference between the resources required to handle peak load andthe actual provision. Initially,we observe that, not surprisingly, lowvalues of lead to large savings, while = 1.3 is less effective. Inmost cases, savings are above 25%, except under certain scenarioswhere the number of classrooms is small.

This difference between instances with small and large valuesof |R| can be explained by the possibilities that large |R| pro-vides to error cancellation. In each time-slot t , our algorithm as-sumes that the amount of resources that will be used by classroomr is wr,t = wr,tr , where r is a random variable drawn froma truncated Gaussian distribution. It is clear that wr,t will almostalways be wrong, but since errors in the estimation of the distri-bution decrease over time, deviations tend to decrease. Moreover,wr,t wr,t in some cases and wr,t wr,t in others, so underesti-mations are frequently compensated by overallocation. For highervalues of |R|, such cancellations happen frequently, whereas ex-treme cases with just 1 classroom depend strongly on the accuracyof its distributions estimation. This strategy fits very well in digi-tal teaching environments as it is possible to have a fairly accurateestimate on resource usage since classes happen in pre-definedtime slots and their content are known in advance. Apart from that,classes happen in parallel, thus amortising error among the class-rooms. It is also important to notice that we included experimentswith small numbers of classrooms for the sake of completeness,even though the average number of classrooms in public schools inBrazil is often greater than the point where the proposed methodpresents substantial savings.

Fig. 2 presentsQoS violations for scenarioswith varying numberof classrooms; for the sake of better visualisation the valueshave been multiplied by 100. Two observations should be maderegarding these results. First, the accumulated QoS violation isconsiderably low (always under 2%), and this holds for all thevalues of . Second, direct comparison between curves showthat adding 0.01 to reduces considerably the percentage ofQoS violations. This fact is a direct consequence of the relativelylarge standard deviation values; clearly, in cases where is large,mistakes in predictions are more likely to occur and to be larger.Therefore, for scenarios where is too low, the adjustments ityields may not be sufficient to overcome an overall estimationwr,tthat was too low.

The observations abovemotivated an investigation of scenarioswhere 2r assume significantly smaller values. Figs. 3 and 4 showthe results of experiments in scenarios where 2r [0.01, 0.1] for


Fig. 2. QoS violations under various numbers of classrooms ( 2r [0.01, 0.2]);where is the minimum resource safety margin.

Fig. 3. QoS violations under various numbers of classrooms ( 2r [0.01, 0.1]);where is the minimum resource safety margin.

each r in R. Fig. 3 corroborates the discussions above by show-ing that QoS violations are significantly smaller in scenarios withlow variance (as presented in Fig. 2). Conversely, improvements onQoS typically lead tomore costs, and this is exactlywhat can be ob-served in a direct comparison between Fig. 4 (lower variances) andFig. 1 (higher variances). In resume, we observe a clear trade-offbetween QoS and resource savings, and this aspect becomes moreevident in scenarios where |R| is small (as discussed below).

Fig. 2 shows that QoS violation is very close to zero for instanceswith 10 and 20 classes if the algorithm is set with = 1.3 and = 1.1, respectively. Higher values of clearly improve QoS, butas lower values lead to significant resource savings (up to 20% insome scenarios), we decided to investigate the trade-off betweenQoS and resource savings for relatively small values of and |R|,i.e., 1 R 70 and {1.04, 1.06, 1.08, 1.1, 1.2}. The resultsare reported in Figs. 5 and 6, which show QoS levels and resourcesavings, respectively. Both figures suggest that there is a certaincriticalmass related to the quality of our algorithm; in particular,the curves reach a certain stability when |R| becomes larger than30 due to error cancellation.

To further explain the reasons of these results, we note thatscenarios with few classrooms reduce the possibilities of errorcancellation, making hence the performance of our method moreunpredictable andmore susceptible to themean and, more impor-tant, to the variance of the distributions. For instance, a sequence

Fig. 4. Resource savings under various numbers of classrooms ( 2r [0.01, 0.1]);where is the minimum resource safety margin.

Fig. 5. QoS violations between 1 and 70 classrooms ( 2R [0.01, 0.2]); where is the resource minimum safety margin.

Fig. 6. Resource savings between 1 and 70 classrooms ( 2R [0.01, 0.2]); where is the resource minimum safety margin.

of time-stepswheremost deviations are either positive or negativelead to abnormalities such as the one observed with |R| 6,where QoS violations and resource savings abruptly increased and


decreased, respectively. Therefore, scenarios with small values of|R|make our methodmore susceptible to the values of 2. Never-theless, our results show that these effects can be mitigated if weset = 1.1 because, in this case, QoS violations are negligible andresource savings are significant (above 20%). In summary, highervalues of make the MLE method less sensitive to the parame-ters of the distributions and reduce QoS violations at the expenseof additional expenses with resources.

Finally, we observe that the MLE method in Algorithm 2 hasa convergence rate that is satisfactory for digital teaching scenar-ios. Namely, after few time-steps (approximately 10), it is possibleto observe that the effects of noise start to be adequately filteredout and parameters (r , 2r ) become indeed subject to fine-grainadjustments. Typically, after 20 time-steps, significant changes on(r ,

2r ) cease to happen, suggesting hence that the algorithm is

also suitable for scenarioswhere the behaviour of the IT infrastruc-ture may change over time.

5. Related work

Cloud Computing is being used by educational institutions as aplatform for offering modern and up-to-date IT resources to stu-dents [8,9]. This is particularly important in developing countriesand for meeting the limited budgets that institutions often haveas a result of the current economic turmoil [1013]. The 2010 UN-ESCO Report points out that Cloud computing offers opportunitiesfor cost reduction due to the economies of scale, thus resultingin a shift away from locally-hosted services [3]. The same reporthighlights the benefits of cloud computing for institutions and stu-dents. Apart from the claimed benefits of cost reduction, elastic-ity [14], and concentration on core business, the report mentionsenhanced resource availability, better end-user satisfaction, andaugmented learning process and collaboration. Cloud is also an in-teresting mechanism for schools to use software licenses over theInternet [15].

Another study has focused on the opportunities of cloud com-puting to increase collaboration among multiple institutions [16].In addition, as discussed by Sultan, there are several examples ofeducational institutions that have adopted cloud computing notonly to rationalise the management of IT resources, but also tomake the education process more efficient [13].

Cost reductions and quality of service are key factors for ed-ucational institutions and are impacted by how cloud providersmanage their resources. Having appropriate tools for doing so is animportant differentiator. The following projects, for instance, haveinvestigated aspects related to Service Level Agreements (SLAs)and load predictionmethods for optimising resourcemanagement.Emeakaroha et al. investigated monitoring time intervals for de-tecting SLA violations and for informing the resource allocationsystemof such violations [17]. The solution is reactive and does notuse serviceworkload for proactively predicting resource consump-tion. Li et al. introduced an approach to optimal virtual-machineplacement for predictable and time-constrained load peaks [18].The solution, although focuses on a proactive resource allocationusing prediction techniques, does not leverage specific informa-tion about the workload domain. Similar approaches were investi-gatedbyAli-Eldin et al. [19].McGougha et al. comparedon-premiseand cloud resources [20]such a study is relevant for better under-standing the cost-benefit of moving workloads to the Cloud.

As in other domains, educational institutions can also utilise hy-brid clouds to balance their workloads between on-premise andremote infrastructure [2123]. The selection of a cloud can rangefrom a single provider to multiple providers. For the latter, rec-ommendation systems could be leveraged for selecting providers[24,25].

Bodenstein et al. have focused on resource allocation decisions,ignoring application information to predict when resource alloca-tion should be adapted [26]. Gong et al. introduced a system calledPRedictive Elastic ReSource Scaling (PRESS), which aims at avoid-ing resource waste and service level objective violations in thecontext of cloud computing [27]. Their goal is to avoid the use ofapplication profiling, model calibration, and understanding of userapplications. Our work takes another direction where cloud cus-tomers provide information about their workloads to avoid SLAviolations and reduce resource waste. Gmach et al. also investi-gated capacity planning using historical data, but without consid-ering the nature of the workload [28]. Other projects [29,30] havealso explored the use of resource consumption prediction to bet-ter allocate resources. However they have not considered IT costreductions and QoS in their studies. Adaptive resource allocationand demand prediction have also been explored in Grid and clus-ter computing environments [3134].

Our work exploits a gap in the state-of-the-art for a methodto assess the impact of using specific domain information of aworkload to assist resource allocation considering both IT costs andQoS for educational institutions.

6. Conclusion

We presented in this work a probabilistic resource allocationmethod that can be specially tailored for cloud computing environ-ments providing services to education institutions. The proposedmethod explores the fact that IT infrastructure instrumentingphysical classrooms may be heterogeneous and may prevent stu-dents from accessing the education material, which leads to underused resources. The method improves system utilisation (and, si-multaneously, reduce allocation costs) at the expense of a minorimpact on QoS.

For our computational evaluation, we generated datasets re-producing scenarios that are similar to those identified in real-world digital teaching initiatives. Using resource savings and QoSviolation as metrics, we investigated several configurations, typi-cally characterised by the number of classrooms and theminimumsafety margin being employed. Results show that error cancella-tion plays a major role and allows for considerable cost reductionsand that QoS violation is small even in situation where large devi-ations in the behaviour of the IT infrastructures are to be expected.This error cancellation is possible in digital teaching environmentsdue to two main reasons: (i) it is possible to have a fairly accurateestimate on resource usage since classes happen in pre-definedtime slots and their content are known in advance; and (ii) classeshappen in parallel, thusmaking error amortisation possible amongthe classrooms.

The results of our experiments allow us to conclude that theprobabilistic resource allocation method is very satisfactory, sinceit is able to deliver allocation plans which are considerably moreeconomic and largely compensate the marginal impacts they haveon QoS when compared with a typical worst-case-oriented ap-proach. Finally, we believe that the performance of this algorithmhas the potential to motivate several education institutions to em-ploy cloud solutions to deliver electronicmaterial to their students.

Acknowledgements

We thank the anonymous reviewers for their valuable com-ments, which have improved our manuscript substantially. Thismaterial is based upon work supported by the FINEP under Con-tract 03.11.0371.00,MCT/FINEP/FNDCT 2010, related to the projectPlatform for the Development of Accessible Vocational Train-ing. Any opinions, findings, and conclusions or recommendations


expressed in thismaterial are those of the author(s) anddonot nec-essarily reflect the views of FINEP or any other related institution.Part of the research described in this article was performed whenFernando Koch and Marcos D. Assuno were working at IBM Re-search.

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Fernando Koch is a Director R & D at Samsung ResearchInstitute Brazil. His group is investigating the next bigthing in health and education by applying SAMSUNGtechnology along with advancedmodels of computing. Hehas over 20 years of IT industry experiencewith practise ininnovation and product development. He published over50 academic papers and more than 20 patents depositedor ongoing. He holds a Ph.D. in Computer Sciences (2009)from Utrecht University, the Netherlands, with thesis onagent-based intelligent mobile services.

Marcos D. Assuno obtained a Ph.D. in Computer Scienceca and Software Engineering (2009) from The Universityof Melbourne, Australia. His research interests includevirtualization, resource allocation and task scheduling forcloud computing, and energy efficiency of distributedsystems.

Carlos Cardonha is a Research Staff Member in the Sys-tems of Engagement and Insight group at IBM ResearchBrazil since January 2012, working onmathematical mod-elling, optimisation, and machine learning to problemsrelated to Smarter Cities, retail, and industrial scheduling.He holds a B.Sc. (2005) and a M.Sc. (2006) degree in Com-puter Science from the Universidade de So Paulo and aPh.D. (2011) in Mathematics from the Technische Univer-sitt Berlin.

Marco A. S. Netto has over 14 years of experience onresource management for distributed systems. He worksmainly on Cloud Computing related topics. He is currentlythe manager of the Industrial Cloud Technologies Groupat IBM Research Brazil. He published over 40 scientificpublications, including journals, conference papers, andbook chapters, and has filed over 30 patents. He obtainedhis Ph.D. in Computer Science at the University ofMelbourne (2010), Australia.

Optimising resource costs of cloud computing for educationIntroductionMotivation and problem descriptionProbabilistic workload-aware dynamic resource allocationPerformance evaluationExperimental setup and metricsResults and analysis

Related workConclusionAcknowledgementsReferences

optimising resource costs of cloud computing for education

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