MAROQ: um modelo de alocação de recursos orientado a qualidade de experiência / MAROQ: a quality of experience oriented resource allocation model

André Luiz Tinassi D'Amato


A quantidade de recursos fornecidos pelas nuvens computacionais na Internet gerou desafios complexos para resolver problemas relacionados à alocação desses recursos. A qualidade de experiência surge como um paradigma diferenciado como um fator potencialmente importante na solução desses desafios. A qualidade de experiência leva em consideração parametrosˆ de contexto. Sendo assim, é proposto neste trabalho o modelo de alocação de recursos MAROQ, que é um modelo orientado à qualidade de experiência, que utiliza informações de contexto para alocação de recursos em nuvens e grids computacionais. Resultados experimentais mostram que utilizar informações de contexto melhora o desempenho na submissão de tarefas.


sistemas distribuídos, qoe, qualidade de experiência, alocação de recursos, satisfação do usuário.


Alkhatib, A. and Krunz, M. Application of chaos theory to the modeling of compressed video. In Communications, 2000. ICC 2000. 2000 IEEE International Conference on, volume 2, pages 836–840 vol.2. (2000).

Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., and Riboni, D. A survey of context modelling and reasoning techniques. Pervasive Mob. Comput., 6(2):161–180. (2010).

Chase, J. S., Irwin, D. E., Grit, L. E., Moore, J. D., and Sprenkle, S. E. Dynamic virtual clusters in a grid site manager. In Proceedings of the 12th IEEE Internatio-nal Symposium on High Performance Distributed Computing, HPDC ’03, pages 90–, Washington, DC, USA. IEEE Computer Society. (2003).

De Moor, K., Ketyko, I., Joseph, W., Deryckere, T., De Marez, L., Martens, L., and Ver-leye, G. Proposed framework for evaluating quality of experience in a mobile, testbed-oriented living lab setting. Mobile Networks and Applications, 15(3):378–391. (2010).

Dey, A. K. Providing Architectural Support for Building Context-aware Applica-tions. PhD thesis, Atlanta, GA, USA. AAI9994400. (2000).

Frey, J., Tannenbaum, T., Livny, M., Foster, I., and Tuecke, S. Condor-g: a computation management agent for multi-institutional grids. In High Performance Distributed Computing, 2001. Proceedings. 10th IEEE International Symposium on, pages 55–63. (2001).

Ghodsi, A., Zaharia, M., Hindman, B., Konwinski, A., Shenker, S., and Stoica, I. . Dominant resource fairness: Fair allocation of multiple resource types. In NSDI, vo-lume 11, pages 24–24. (2011)

Hermenier, F., Lebre, A., and Menaud, J.-M. Cluster-wide context switch of vir-tualized jobs. In Proceedings of the 19th ACM International Symposium on High Per-formance Distributed Computing, HPDC ’10, pages 658–666, New York, NY, USA. ACM. (2010).

Joseph, V., de Veciana, G., and Arapostathis, A. Resource allocation: Realizing mean-variability-fairness tradeoffs. In Allerton Conference, pages 831–838. IEEE. (2012).

Kalin, M. Java Web Services: Up and Running. O’Reilly Media, Inc. (2013).

Koning, T. C. M., Veldhoven, P., Knoche, H., and Kooij, R. E. Of MOS and men: bridging the gap between objective and subjective quality measurements in mobile tv. In SPIE. (2007).

Micillo, R., Venticinque, S., Aversa, R., and Di Martino, B. A grid service for resource-to-agent allocation. In Internet and Web Applications and Services, 2009. ICIW ’09. Fourth International Conference on, pages 443–448. (2009).

Nazario, D. C. and Dantas, M. A. R. Taxonomia das publicações sobre qualidade de contexto. In International Journal of Sustainable Business, number 20, pages 1–28. (2012).

Ryman, A. Simple object access protocol SOAP and web services. In Proceedings of the 23rd International Conference on Software Engineering, ICSE ’01, pages 689–, Washington, DC, USA. IEEE Computer Society. (2001).

Scheidt, R. F., Schmidt, K., Pessoa, G. M., Viera, M. A., and Dantas, M. A. R. A software product line approach to enhance a meta-scheduler middleware. In Journal of Physics: Conference Series, volume 341, pages 1–7. (2012).

Schilit, B., Adams, N., and Want, R. Context-aware computing applications. In Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applicati-ons, WMCSA ’94, pages 85–90, Washington, DC, USA. IEEE Computer Society. (1994).

Shaikh, J., Fiedler, M., and Collange, D. Quality of experience from user and network perspectives. annals of telecommunications, 65(1-2):47–57. (2010).

Sotomayor, B., Keahey, K., and Foster, I. Combining batch execution and leasing using virtual machines. In Proceedings of the 17th International Symposium on High Performance Distributed Computing, HPDC ’08, pages 87–96, New York, NY, USA. ACM. (2008).

Stolyar, A. L. Maximizing queueing network utility subject to stability: Greedy primal-dual algorithm. Queueing Syst. Theory Appl., 50(4):401–457. (2005).

Sullivan, M., Pratt, J., and Kortum, P. Practical issues in subjective video quality evaluation: Human factors vs. psychophysical image quality evaluation. In Procee-dings of the 1st International Conference on Designing Interactive User Experiences for TV and Video, UXTV ’08, pages 1–4, New York, NY, USA. ACM. (2008).

Zhou, L., Chen, M., Qian, Y., and Chen, H.-H. Fairness resource allocation in blind wireless multimedia communications. Multimedia, IEEE Transactions on, 15(4):946–956. (2013).