Towards Improved Model for User Satisfaction Assessment of Multimedia Cloud Services

Authors

  • Aleksandar Karadimce University of Information Science and Technology St. Paul the Apostle, Ohrid
  • Danco P. Davcev University Ss Cyril and Methodius, Skopje

DOI:

https://doi.org/10.13052/1550-4646.1422

Keywords:

Cloud Computing, User Satisfaction, Bayesian Networks, Perceived Quality

Abstract

Service providers are in constant search for appropriate tools that will help them to determine and measure the satisfaction of end-users. In terms of efficient utilization of network resources, they are constantly striving to guaranty the quality, availability, and responsibilities of the directly measurable parameters with the service-level agreements (SLA). The introduction of cloud computing technology aims to provide stable, reliable and encapsulated environment for users who use different types of mobile and desktop devices to simultaneously access shared resources that are available anywhere and at any time. However, what is lacking in this direction is an assessment of how much the end-users themselves are satisfied with the cloud-based services offered. This research has considered different cloud computing service categories, some of them have been extensively used and others are still under development. These services use a variety of organizational and infrastructure access models, making them easily accessible and practical for work. The very nature of cloud computing-based services allows dynamic allocation of resources based on end-user needs, which makes the process of evaluating the offered services complex. This research will deliver the most appropriate model for assessing the impacts of objective and subjective factors by using the Bayesian networks. The dynamic nature of these networks allows flexibility and adaptation to measure the impact of various influencing factors. The main contribution of this research is the introduction of a metrics for assessing the user satisfaction of the particular type of offered service. In that direction, this research has provided improved Quality of Experience (QoE) model for measuring and assessing the perception of the multimedia cloud-based services quality among end-users.

 

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References

Karadimce,A., and Davcev, D. (2017). Bayesian Network Model for Estimating

User Satisfaction of Multimedia Cloud Services. In Proceedings

of the 15th International Conference on Advances in Mobile Computing

& Multimedia 3–12. Available at: https://doi.org/10.1145/3151848.315

Abunaser, A., and Alshattnawi, S. (2012). Mobile cloud computing and

other mobile technologies: Survey. Journal of Mobile Multimedia, 8(4),

–252.

Wang, M., and Ng, J.W. (2012). Intelligent mobile cloud education: smart

anytime-anywhere learning for the next generation campus environment.

In 8th International Conference on Intelligent Environments (IE),

–156. Available at: https://doi.org/10.1109/IE.2012.8

Noor, R. M., and Khorsandroo, S. (2011). Quality of experience key

metrics framework for network mobility user. International Journal of

the Physical Sciences, 6(28), 6521–6528. DOI: 10.5897/IJPS11.318

Parry G., Newnes L., and Huang X., ( 2011). Goods, products and

services, in Service Design and Delivery, Service Science: Research and

Innovations in the Service Economy, M. Macintyre et al., Eds. NewYork,

NY, USA: Springer, 19–29. DOI: 10.1007/978-1-4419-8321-3 2

Safdari, F., and Chang, V. (2014). “Review and Analysis of Cloud Computing

Quality of Experience”, SCITEPRESS – Science and Technology

Publications, 2014, 83–88. DOI: 10.5220/0004982800830088

El-Khatib, K., Bochmann, G. v., and El-Saddik, A. (2017).AQoS-based

Service Composition for Content Adaptation, 331–338. Available at:

https://doi.org/10.1109/ICDEW.2007.4401013

ITU-T G.1011 Reference guide to quality of experience assessment

methodologies, 2013.

Wu, W., Arefin, A., Rivas, R., Nahrstedt, K., Sheppard, R., and Yang,

Z. (2009). Quality of experience in distributed interactive multimedia

environments: toward a theoretical framework. In Proceedings of the

th ACM International Conference on Multimedia, 481–490. DOI:

1145/1631272.1631338

Shin, Y. R., and Huh, E. N. (2016). mCSQAM: Service Quality Assessment

Model in Mobile Cloud Services Environment. Mobile Information

Systems, 1–9. Available at: https://doi.org/10.1155/2016/2517052

Zepernick, H.-J., and Engelke, U. (2014). Quality of Mobile Multimedia

Experience: Past, Present and Future. In IEEE International Conference

on Communications and Electronics, .

Nam, H., Kim, K.-H., Calin, D., and Schulzrinne, H. (2014).YouSlow: a

performance analysis tool for adaptive bitrate video streaming.ACMSIGCOMMComputer

Communication Review, 44(4), 111–112.Available at:

https://doi.org/10.1145/2740070.2631433

Mushtaq, M. S., Augustin, B., and Mellouk, A. ( 2012). Empirical study

based on machine learning approach to assess the QoS/QoE correlation,

–7. Available at: https://doi.org/10.1109/NOC.2012.6249939

Suznjevic, M., Skorin-Kapov, L., and Humar, I. (2015). Statistical user

behavior detection and QoE evaluation for thin client services. Computer

Science and Information Systems, 12(2), 587–605. Available at: .

https://doi.org/10.2298/CSIS140810018S

Gholami, M. F., Daneshgar, F., Low, G., and Beydoun, G. (2016).

Cloud migration process—A survey, evaluation framework, and open

challenges. Journal of Systems and Software, 120, 31–69. Available at:

https://doi.org/10.1016/j.jss.2016.06.068

Casas, P., and Schatz, R. (2014). Quality of experience in cloud services:

survey and measurements. Computer Networks, 68, 149–165.

DOI:10.1016/j.comnet.2014.01.008

Hobfeld, T., Schatz, R.,Varela, M., and Timmerer, C. (2012). Challenges

of QoE management for cloud applications. IEEE Communications Magazine,

(4). 28–36. Available at: https://doi.org/10.1109/MCOM.2012.

Casas, P., Seufert, M., Egger, S., and Schatz, R. (2013). Quality of

experience in remote virtual desktop services. In IFIP/IEEE International

Symposium on Integrated Network Management (IM 2013), 1352–1357.

Casas, P., Sackl, A., Egger, S., and Schatz, R. (2012). YouTube & facebook

quality of experience in mobile broadband networks. In Globecom

Workshops (GC Wkshps), 1269–1274.

Mitra, K., Zaslavsky, A., and Åhlund, C. (2015). Context-aware QoE

modelling, measurement, and prediction in mobile computing systems.

IEEE Transactions on Mobile Computing, 14(5), 920–936.Available at:

https://doi.org/10.1109/TMC.2013.155

Tasaka, S. (2015). A Bayesian hierarchical model of QoE in

interactive audiovisual communications. In IEEE International

Conference on Communications (ICC), 6983–6989. Available at:

https://doi.org/10.1109/ICC.2015.7249439

Karadimce, A., and Davcev, D. (2016). Perception of quality in

cloud computing based services. In Eighth International Conference

on Quality of Multimedia Experience (QoMEX), 1–6. Available at:

https://doi.org/10.1109/QoMEX.2016.7498925

Le Callet P., Möller S. and Perkis A. (eds), (2012). Qualinet white

paper on definitions of quality of experience–output version of the

dagstuhl seminar 12181, in European network on quality of experience

in multimedia systems and services (COST Action IC 1003).

Sun,Y.-C., and Chen, C.-M. (2010). Assessing learning emotion for both

the cognitive styles of visualizer and verbalizer distributed to different

types of multimedia learning materials. In 2010 International Symposium

on Computer Communication Control and Automation (3CA), 148–151.

Available at: https://doi.org/10.1109/3CA.2010.5533628

Eerola, T., Lensu, L., Kamarainen, J. K., Leisti, T., Ritala, R.,

Nyman, G., and Kälviäinen, H. (2011). Bayesian network model

of overall print quality: construction and structural optimisation.

Pattern Recognition Letters, 32(11), 1558–1566. Available at:

https://doi.org/10.1016/j.patrec.2011.04.006

Kevin Korb, B., and Ann Nicholson, E. (2010). Bayesian Artificial

Intelligence, in Second Edition (2nd ed.). CRC Press, Inc., Boca Raton,

FL, USA,. ISBN:1439815917.

Nokelainen, P., Silander, T., Ruohotie, P., and Tirri, H., (2003). Investigating

Non-linearities with Bayesian Networks. In 11th Annual Convention

of the American Psychology Association at Toronto, Division of

Evaluation, Measurement and Statistics. Toronto, Canada.

Dielmann, A., and Renals, S. (2007). Automatic meeting segmentation

using dynamic Bayesian networks. IEEE Transactions on Multimedia,

(1), 25–36. DOI: 10.1109/TMM.2006.886337

Castillo, E., Gutiérrez, J. M., and Hadi, A. S. (1997). Sensitivity analysis

in discrete Bayesian networks. IEEE Transactions on Systems, Man, and

Cybernetics-Part A: Systems and Humans, 27(4), 412–423.Available at:

https://doi.org/10.1109/3468.594909

Kjærulff, U., and van der Gaag, L. C. (2000). Making SensitivityAnalysis

Computationally Efficient. In Proceedings of the 16th Conference on

Uncertainty in Artificial Intelligence, 317–325.

Coupe,V. M. H., van der Gaag, L. C., and Habbema, J. D. F. (2000). Sensitivity

analysis: an aid for probability elicitation. Knowledge Engineering

Review, (15), 1–18.

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Published

2018-04-26

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