Mapping QoE with Resource Estimation in IoTMohammad Aazam and Khaled A. Harras

Carnegie Mellon Universityaazam@ieee.org, kharras@cs.cmu.edu

Abstract—Technologies associated directly or indirectly withInternet of Things (IoT) are growing rapidly, paving way formultimedia-based IoT services such as social IoT (SIoT), health-care/telemedicine, and Tactile Internet. Multimedia services re-quire a certain level of quality of service (QoS), which can be achallenge in the case of resource-constrained devices, particularly,mobile nodes. Therefore, it becomes imperative to dynamicallyallocate resources incorporating quality of experience (QoE). Inthis paper, we provide a methodology for incorporating QoE indifferent ways such as overall QoE and the QoE provided bythe current customer. QoE is determined through net promoterscore (NPS). Our mathematical model determines the ratio withwhich the resources need to be scaled up to meet the desiredQoS. We have implemented our model using Java, and tested itsimpact using the CloudSim simulator. The results show the effectof the QoE ratio determined by our proposed model.

Index Terms—IoT; Multimedia; QoE; QoS; Resource Manage-ment

I. INTRODUCTION

Multimedia-based Internet of Things (IoT) applications isgaining importance as the technologies around multimediacommunication are becoming cheaper and and more applica-tions rely on more heterogeneous IoT devices. With the avail-ability of 4G long-term evolution (LTE) communication, andsoon to be available 5G, more high-quality video communica-tion will be enabled. As mobile communication also advances,along with agile virtualization techniques (e.g. containers),coupled with futuristic paradigms such as Tactile Internet (TI),Industrial Internet of Things (IIoT) [1], cyber-physical systems(CPS) [2], and intelligent transportation systems (ITS), videocommunication will be an inevitable part of smart services.More than entertainment and standard communication, videoor multimedia-based communication will be playing a vitalrole in the healthcare sector (e.g. remote surgery, remotepatient monitoring) [3].

Several services will have strict quality of service (QoS)requirements, and quality of experience (QoE) will play avital role in determining the required QoS. Examples in-clude: remote surgery via Tactile Internet, remote patientmonitoring, drone applications (e.g. delivery, surveillance,or emergency management), IIoT, and CPS. If a patient ismonitored/operated remotely or a drone is distantly maneu-vered, user experience will determine the amount of resourcesrequired to be allocated in order to reach user satisfaction.Hence, more adaptive and dynamic multimedia delivery meth-ods are needed. Currently, only 17 percent of Internet videois supported by adaptive or dynamic streaming technologies

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[4]. Dynamic methodologies will not only achieve the desiredlevel of QoE but will also help improve resource utilization.

Multimedia content often requires high processing power,storage, bandwidth, and better resource scheduling. Hence-forth, it becomes important to manage these resources effec-tively to perform efficient resource management at the serviceprovider (e.g., cloud, fog, or edge server) [5]. Especially withIoT nodes that do not typically experience reliable/consistentconnectivity quality, the cost considerably increases when itcomes to resource allocation and a huge amount of resourcesgo underutilized because of the unexpected and unreliablebehavior of the user [5].

In this paper, we build on our previous work [5] wherewe proposed a basic mathematical model for IoT resourceestimation (RE), with the focus on customer historical records.Here, our focus is on QoE-based dynamic resource estimation,referred to as QoE Ratio (QoER). QoE has a direct impacton the cloud and IoT business process since the reputationof the service provider, provider’s net profit, and mitigationof resource underutilization depend on it. There are differentways to acquire QoE and one of those is through net promoterscore (NPS) [6], which we incorporate in our work.

Customers provide NPS-based QoE feedback on a scale of0-10. Where customers giving a score between 0 to 6 areknown as detractors, 7-8 are passives, and 9-10 are promoters.Final NPS is determined by subtracting detractors from thepromoters. In the default case when there is no feedbackavailable, there are no promoters and detractors. The defaultNPS in that case is the mean of passive, which is 7.5.

We determine NPS ratio (NPSr) using two parameters. Oneis the overall NPS (NPSo) which is given by all the userswho have previously used the service S. The other parameteris the NPS of the particular customer C (NPSc), currentlyrequesting a service S. We analyze the effect of NPSr onthe basis of which dynamic RE can be performed. Such asystem can play a vital role in dynamically improving theQoS according to the changing conditions of the service.The service quality can be close-to-desired, and resourceunderutilization can be mitigated, resulting in profit for theservice provider. We implement our model in Java and provideempirical results using the CloudSim simulator.

In rest of the paper, section II is on prior related works.Section III presents the proposed QoE Ratio model. In sectionIV, we present the outcomes of the proposed model. SectionV concludes the paper with some insights on the future work.

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Figure 1: QoE influence factors.

II. RELATED WORK

Reiter et al. [7] discuss influence factors (IFs) in QoE. IFscan be defined by characteristics of a user, service, or context,that may influence a user’s QoE. There are three categoriesof IFs: human IFs (HIFs), system IFs (SIFs), and contextIFs (CIFs), as shown in Fig 1. IFs may influence each other.For instance, HIFs and CIFs might determine to what extentSIFs affect QoE; a video watched on a mobile phone, oron a TV, might have different QoE. HIFs are defined by auser’s emotional, physical, and/or mental state, demographicand socio-economic back-ground. SIFs are the attributes thatdefine the technically produced QoS. CIFs correspond to asituational property describing a user’s environment.

Hossfeld and Keimel [8] emphasize that traditional QoEevaluation has limitations, one of which is the unavailabilityof the number of simultaneous participants. Moreover, demog-raphy of the subjects is often not illustrative of the diversity inthe general population. The evaluation setup also does not trulyrepresent most real-life environments. One viable solution tothis problem is to crowdsource QoE evaluations over the Inter-net via user feedback. This way, more realistic QoE evaluationcan be performed. Yitong et al. [4] examin the aspects thatinfluence the QoE of adaptive streaming, and assess it whenrunning an end-to-end service. It has been endorsed that withrespect to QoE, adaptive streaming greatly improves end-user’ssubjective perception, as compared to the fixed-rate streaming.Li et al. [9] presented a QoE evaluation for IoT on the basisof linear regressions analysis. They analyze the type of IoTapplications, determine the applicable QoS parameters of QoE,and collect the sample data accordingly. However, dynamicallyadaptive methodologies are not discussed in this work.

Floris and Atzori [10] presented mean opinion score (MOS)-based QoE evaluation for multimedia IoT. A layered IoTarchitecture is analyzed in their work to get relevant QoEIFs required according to relevant application scenarios. Ningand Wang discussed the potential of IoT and the amount ofdata it is going to generate [11]. The authors also emphasizeefficient management of resources for the future Internet in

which heterogeneous IoTs would be an essential part.

III. PROPOSED QOER MODEL

In this section, we present our QoER model for resourceestimation. This model will be implemented where the re-sources are allocated for a service, such as cloud, fog, edgeserver. Customers make a request for a service S. The ser-vice provider in a cloud-IoT scenario makes sure that theQoS requirements are met. Customer’s feedback (QoE) isconsidered while estimating the resources, so that the servicequality can meet the expectations of the user. An NPS ratiois generated using the overall NPS of a service and theNPS provided by the current customer. Later, resources areestimated according to the determined NPS ratio. In this way,resources are always allocated according to the expectationsand changing requirements of each customer. This not onlyimproves the service quality and customer satisfaction, butalso works for the marketing of the service and net profit gainfor the provider. Additionally, it helps to mitigate resourceunderutilization [12].

During the resource estimation phase, resources are in-creased whenever QoS requirements are not met in the pre-vious case of service consumption, so that better service isprovided and customer’s loyalty is gained, as the resourceconsumption goes on. NPS ratio is calculated as follows:

NPSr =

n∑i=0

NPSd

xNPSoi, if n = 0

n∑i=0

xNPSoi

NPSd, if xNPSoi > NPSd

n∑i=0

n∑k=0

xNPSoi

xNPSck, if xNPSoi ≥ xNPSck

n∑i=0

n∑k=0

xNPSck

xNPSoi, if xNPSck ≥ xNPSoi

(1)NPSr is the NPS ratio determined on the basis of the

overall NPS (NPSo) of the service instance i that is currentlyrequested by a customer with a possible certain historical NPS(NPSc). There are four cases: Case 1 represents the situationwhen the customer is new, without any previous NPS record.In this case, the default NPS (NPSd) is applied which is 7.5.However, it is possible that the requested service may havea higher overall NPS (NPSoi) than the default value 7.5. Itshows that majority of the users have a better opinion aboutthe service quality, hence, higher priority should be given totheir opinion. This corresponds to case 2 of our model. Whena customer is returning (previous NPS records exist), eitherof case 3 or 4 is applied. Case 3 corresponds to the situationwhen a customer c with k number of previous instances ofservice utilization (NPSck) has a higher value than the overallNPS (NPSoi). In the otherwise situation, case 4 is applied.The ratio increases as the gap between NPSck and NPSoi

increases, so that the resources are scaled up according tothe particular opinion that would eventually enhance servicequality. In the case when (NPSck) and NPSoi are the

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same, ratio remains 1 because it shows that everyone has thesame experience, hence, a particular customer does not needexclusively greater resources for better QoS, rather the servicein general needs to be scaled up. Thenceforth, in such a case,regular, linear increase in resources will be applied.

All types of NPS are calculated through Eq. 2, in whichNPSpr represents the promoters and NPSdt represent thedetractors.

NPS = NPSpr −NPSdt (2)

IV. IMPLICATIONS AND OUTCOMES

In this section, we provide details on implementation of ourproposed model, it evaluation, and the results of the evaluation,along with their discussions.

A. Evaluation Setup and Implementation

We implement and evaluate our proposed model using Javaon the CloudSim simulator. The evaluation setup is based onreal IBM datacenters (IBM server x3550 (2 x [Xeon X56753067 MHz, 6 cores], 16 GB) defined in the CloudSim toolkit.We generate 1000 different requests for service instances withdifferent QoS requirements (delay, jitter, packet loss and soon). We use a Gaussian distribution (as it is more generic andcontinuous probability distribution) to generate different re-quest types which represent different instances of the services.We generate the QoE feedback randomly in the form of NPSthrough the toss−of−a−die methodology. We accommodatedifferent service instances requested by the customers withdifferent historical statuses, such as new customer with norecord available, existing customer with high QoE feedbackrecord, and existing customer with low QoE feedback record.All these factors are incorporated while evaluating our model.

B. Performance Metrics

The metrics used in the evaluation are as following:Overall NPS: Average NPS of a particular service provided

by all the customers that have used the service so far.Default NPS: This is the default, initial NPS, which is the

average of the passive range (7.5).Customer’s Historical NPS: This is the average NPS of

the current customer that has requested the service.

C. Results

We now present our evaluation results on QoE ratio as partof the overall resource estimation methodology.

1) Default NPS: Fig. 2 shows the NPS ratio (NPSr)which is based on the current customer’s average NPS(NPSc) and the overall (NPSo). However, as it is thedefault case, there is no previous NPS record for customer c.Thenceforth, the default NPS (NPSd) which is 7.5 accordingto Eq. 2 is applied. The horizontal axis in Fig. 2 showsdifferent NPSo for each service, and accordingly, differentNPSr. The larger the gap between NPSo and NPSc, thehigher the NPSr, which implies that additional resources

Figure 2: NPS ratio for default case when the customer is new.

with that ratio are required to improve the QoS. For example,for service 1, when NPSo is 3, the NPSr becomes 2.5.Hence, additional resources with the ratio of 2.5 will berequired to improve the QoS, or otherwise the service willcontinue to decline in its QoE. In the case of service 5where NPSo = 9 (higher than the default case of 7.5), theNPSr becomes 0.84. The reasons why this RE is less than1 is because the customer is new, without any previous NPShistory. Which means that the service provider does not knowthe customer’s loyalty. If the customer quits the service, theresources will be underutilized. Hence, resources have to beallocated cautiously in the beginning. It does not mean thatthe customer will experience a comparatively bad service,but resources would be increased as soon as the servicecontinues to ensure there are enough resources for otherreliable existing customers.

2) When NPSo > NPSc: In this case the customer isreturning, and his/her historical record (NPSc) exists, whichis essentially case 3 of Eq. 1. To evaluate this case, let usassume that the average overall NPS (xNPSo) is 8. In thefirst instance in Fig. 3, the overall and customer specificNPSes are the same. This means that the quality most ofthe people have experienced for this service is the same asthat of the current customer. Hence, no change in the ratio.In the second instance when the customer’s NPS is 4, theNPSr becomes 2. Similarly, NPSr becomes 2.67 as thegap between NPSc and NPSo increases. The ratio shrinksas the gap between NPSc and NPSo decreases in instance4. In the same way, the resources are scaled up based on theratio determined, which results in enhanced service qualityevery time a customer requests for it. This eventually plays itsrole in determining customer’s loyalty, resulting in improvingresource utilization.

3) When NPSc > NPSo: When the current customer’sNPS is greater than the overall NPS (case 4 of Eq. 1),NPSr will be affected accordingly. Opposite to the previouscase, on this occasion, since NPSc is already higher thanNPSo, apparently, there is no need to enhance resources.Nevertheless, the reason why NPSr still matters here is

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Figure 3: NPS ratio when NPSo > NPSc.

Figure 4: NPS ratio when NPSc > NPSo.

because if the current customer gets a better service, it wouldresult in even better QoE, which would eventually enhance theoverall QoE. This is imperative for marketing purposes. Fig. 4shows case 4 of Eq. 1. As an example, NPSo is 7 for services and NPSc varies according to different utilization instances.In the first instance NPSc is 8 which is slightly higher thanthe overall NPS. The NPSr becomes 1.15. This means thatthe difference between the general opinion by others is notmuch from the current customer. Therefore, there is only aslight increase in resources. But if the difference increases,it would mean that the general opinion needs to be betteredas well, in order to enhance the overall QoE. However, thereis an exception to it in the case when NPSc is 10 (whichis perfect QoE). This exceptional case is represented throughinstance 6. As the current customer has no complaint withthe QoE, there is no point in increasing the resources withoutany reason. As a result, the increase ratio (NPSr) is zero.Here, it should be noted that zero means no increase at all, 1means a linear or default increase (according to the policy ofa service provider), and rest of the ratio determined throughour model tell the exact factor with which the resources haveto be increased.

V. CONCLUSION AND FUTURE WORK

The rising importance of IoT-based services has resultedin adoption of more sophisticated ways to provide improvedservices that meet rising user QoE expectations. QoS is a

major concern when it comes to multimedia services, thatincludes services related to telemedicine, remote healthcare,and various Tactile Internet applications. Resource allocationin currently deployed systems does not take into account QoEwhile estimating resources. If QoE is considered, better QoScan be maintained. As such, a fair business process is upheld,reliable customer behavior is gained, resource underutilizationis minimized, and profit is increased. Therefore, it is a win-win situation for all the involved parties. This is what we havefocused on in this paper. We provided a methodology thattakes into account the overall QoE and a specific customer’sQoE, on the basis of which a ratio is calculated. Resourcesare dynamically estimated on the basis of this ratio. Ourwork provides some insight on how dynamically QoE-basedresource estimation can be done to achieve the desired servicequality results. An extended part of this work is in press [13],in which we focus on Tactile Internet applications. We plan tofurther extend this work on one or more use-cases of Tactileapplications.

ACKNOWLEDGMENT

This publication was made possible by NPRP grant # 8-1645-1-289 from the Qatar National Research Fund, a memberof Qatar Foundation. The statements made herein are solelythe responsibility of the authors.

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