Динамическое распределение запросов для повышения качества обслуживания
DOI:
https://doi.org/10.26577/JMMCS-2018-4-570Ключевые слова:
Качество обслуживания, QoS, динамическая балансировка, распределение ресурсов, балансировка нагрузки, очередностьАннотация
Появление Web-сервисов как открытых компонентов, поддерживающих гибкий и недорогой
набор распределенных приложений, а также их использование в качестве перспективного
решения для интеграции с другими приложениями и поставщиками программно-
аппаратных ресурсов, очень востребовано. Использование Web-сервисов упрощает и
улучшает функциональность системы из-за доступности взаимодействия программ друг с
другом через Интернет с использованием открытых протоколов. Таким образом, необходимо
решать вопросы обеспечения QoS, такие как распределение потоков запросов, повышение
эффективности операций при пиковых нагрузках. Одним из способов решения этих
проблем является применение динамического распределения запросов, которое обеспечивает
непрерывность передачи и обработки данных, игнорируя сбои и выхода из строя
оборудования, перераспределение потока данных к доступным узлам в случае их потери.
Вышеупомянутые могут быть достигнуты с использованием определенных эвристик на
основе методов математической статистики и моделирования вероятностей. Кроме того, мы
предлагаем анализ методов и моделей, разработанных для обеспечения QoS.
Библиографические ссылки
[1] Hyunyoung Kil, Reeseo Cha, Wonhong Nam. Transaction history-based web service composition for uncertain QoS. International journal of web and grid services, vol. 12 (2016): 42.
[2] Balakayeva G.T., Aidarov K.A. Research of algorithms and methods of load balancing and construction of models for queuing networks. Proceedings of the International Conference on Computational and Applied Mathematics "VPM’17"in the framework of the Marchuk Scientific Readings, Novosibirsk, June 25 – July 14 [Electron. resource], (2017): 17–21.
[3] Goldstein B.S., Marshak M.A., Mishin E.D., Sokolov N.A., Tum A.V. "Indicators of the functioning of the multiservice communication network of public use". Journal of Communication Engineering, no. 3–4 (2009): 17.
[4] Balakayeva G., Aidarov K., Simulation of load balancing algorithms based on queuing networks. Abstracts of VI Congress of the Turkic World Mathematical Society (TWMS 2017), Astana, (2017): 313.
[5] EL-Sanosi I. and Ezhilchelvan, P. Improving zookeeper atomic broadcast performance by coin tossing. In European Workshop on Performance Engineering, Springer, (2017): 249–265.
[6] Flannagen E. Michael. Syngress (2001) ”Administering Cisco QoS in IP-Networks”, Syngress Media, ISBN 1928994210, 9781928994213, (2001): 519.
[7] Goldstein B.S., Marshak M.A., Mishin E.D., Sokolov N.A., Tum A.V. Kontrol pokazatelei kachestva obsluzhivanya s uchotom perekhoda k seti svyazi sleduyushego pokolenya [Control of quality of service indicators, taking into account the transition to a next-generation communication network]. Tekhnika Svyazi, no 1 (2009).
[8] Andrzejak A., Arlitt M., Roila J. Bounding the Resource Savings of Utility Computing Models. Technical Report HPL-2002, Internet Systems and Storage Laboratory, HP Laboratories, (December 2002): 339.
[9] Kharchenko V, Illiashenko O, Boyarchuk A, Sklyar V, Phillips C Emerging curriculum for industry and human applications in Internet of Things. In: 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Bucharest, Romania: Institute of Electrical and Electronics Engineers Inc., (2017): 918-922.
[10] Chase, J. S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., Doyle, R.P. Managing energy and server resources in hosting centers. ACM SIGOPS Operating Systems Review., no 35(5), (2001): 103.
[11] Lee, Y.C., Zomaya, A.Y. Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2), (2010): 268–280.
[12] Enokido, T., Aikebaier, A., Takizawa, M. A Model for Reducing Power Consumption in Peer-to-Peer Systems. IEEE Systems Journal, 4(2),(2010): 221–229.
[13] Liu, S., Ren, S., Quan, G., Zhao, M., Ren, S. Profit Aware Load Balancing for Distributed Cloud Data Centers. 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, (2013): 611–622.
[14] Vakilinia, S., Heidarpour, B., Cheriet, M. Energy Efficient Resource Allocation in Cloud Computing Environments. IEEE Access, 4, (2016), 8544–8557.
[15] Zhang, W., Zhang, Z., Chao, H.-C. Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management. IEEE Communications Magazine, 55(12),(2017): 60–67.
[16] Nagpure, M. B., Dahiwale, P., Marbate, P. An efficient dynamic resource allocation strategy for VM environment in cloud. 2015 International Conference on Pervasive Computing (ICPC) (2015).
[17] Mukherjee, M., Shu, L., Wang, D. Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges. IEEE Communications Surveys and Tutorials, (2018): 1–1.
[18] Bodenstein, C., Schryen, G., Neumann, D. Energy-aware workload management models for operation cost reduction in data centers. European Journal of Operational Research, 222(1), (2012): 157–167.
[19] Mohammad Ali, H. M., El-Gorashi, T. E. H., Lawey, A. Q., Elmirghani, J. M. H. Future Energy Efficient Data Centers With Disaggregated Servers. Journal of Lightwave Technology, 35(24), (2017): 5361–5380.
[20] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P. P., Kolodziej, J., Balaji, P., . . . Zomaya, A. A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7), (2014): 751–774.
[21] Ge, Y., Zhang, Y., Qiu, Q., Lu, Y.-H. A game theoretic resource allocation for overall energy minimization in mobile cloud computing system. Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design - ISLPED ’12., (2012): 279-284.
[22] Kliazovich, D., Arzo, S. T., Granelli, F., Bouvry, P., Khan, S. U. e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing. 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, (2013):7-13.
[23] Aikebaier, A., Yang, Y., Enokido, T., Takizawa, M. Energy-Efficient Computation Models for Distributed Systems. 2009 International Conference on Network-Based Information Systems (2009): 424-431.
[24] Sharma, B., Wood, T., Das, C. R. HybridMR: A Hierarchical MapReduce Scheduler for Hybrid Data Centers. 2013 IEEE 33rd International Conference on Distributed Computing Systems (2013): 102-111.
[25] Gao, Y., Guan, H., Qi, Z., Song, T., Huan, F., Liu, L. Service level agreement based energy-efficient resource management in cloud data centers. Computers and Electrical Engineering, 40(5), (2014): 1621–1633.
[2] Balakayeva G.T., Aidarov K.A. Research of algorithms and methods of load balancing and construction of models for queuing networks. Proceedings of the International Conference on Computational and Applied Mathematics "VPM’17"in the framework of the Marchuk Scientific Readings, Novosibirsk, June 25 – July 14 [Electron. resource], (2017): 17–21.
[3] Goldstein B.S., Marshak M.A., Mishin E.D., Sokolov N.A., Tum A.V. "Indicators of the functioning of the multiservice communication network of public use". Journal of Communication Engineering, no. 3–4 (2009): 17.
[4] Balakayeva G., Aidarov K., Simulation of load balancing algorithms based on queuing networks. Abstracts of VI Congress of the Turkic World Mathematical Society (TWMS 2017), Astana, (2017): 313.
[5] EL-Sanosi I. and Ezhilchelvan, P. Improving zookeeper atomic broadcast performance by coin tossing. In European Workshop on Performance Engineering, Springer, (2017): 249–265.
[6] Flannagen E. Michael. Syngress (2001) ”Administering Cisco QoS in IP-Networks”, Syngress Media, ISBN 1928994210, 9781928994213, (2001): 519.
[7] Goldstein B.S., Marshak M.A., Mishin E.D., Sokolov N.A., Tum A.V. Kontrol pokazatelei kachestva obsluzhivanya s uchotom perekhoda k seti svyazi sleduyushego pokolenya [Control of quality of service indicators, taking into account the transition to a next-generation communication network]. Tekhnika Svyazi, no 1 (2009).
[8] Andrzejak A., Arlitt M., Roila J. Bounding the Resource Savings of Utility Computing Models. Technical Report HPL-2002, Internet Systems and Storage Laboratory, HP Laboratories, (December 2002): 339.
[9] Kharchenko V, Illiashenko O, Boyarchuk A, Sklyar V, Phillips C Emerging curriculum for industry and human applications in Internet of Things. In: 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Bucharest, Romania: Institute of Electrical and Electronics Engineers Inc., (2017): 918-922.
[10] Chase, J. S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., Doyle, R.P. Managing energy and server resources in hosting centers. ACM SIGOPS Operating Systems Review., no 35(5), (2001): 103.
[11] Lee, Y.C., Zomaya, A.Y. Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2), (2010): 268–280.
[12] Enokido, T., Aikebaier, A., Takizawa, M. A Model for Reducing Power Consumption in Peer-to-Peer Systems. IEEE Systems Journal, 4(2),(2010): 221–229.
[13] Liu, S., Ren, S., Quan, G., Zhao, M., Ren, S. Profit Aware Load Balancing for Distributed Cloud Data Centers. 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, (2013): 611–622.
[14] Vakilinia, S., Heidarpour, B., Cheriet, M. Energy Efficient Resource Allocation in Cloud Computing Environments. IEEE Access, 4, (2016), 8544–8557.
[15] Zhang, W., Zhang, Z., Chao, H.-C. Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management. IEEE Communications Magazine, 55(12),(2017): 60–67.
[16] Nagpure, M. B., Dahiwale, P., Marbate, P. An efficient dynamic resource allocation strategy for VM environment in cloud. 2015 International Conference on Pervasive Computing (ICPC) (2015).
[17] Mukherjee, M., Shu, L., Wang, D. Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges. IEEE Communications Surveys and Tutorials, (2018): 1–1.
[18] Bodenstein, C., Schryen, G., Neumann, D. Energy-aware workload management models for operation cost reduction in data centers. European Journal of Operational Research, 222(1), (2012): 157–167.
[19] Mohammad Ali, H. M., El-Gorashi, T. E. H., Lawey, A. Q., Elmirghani, J. M. H. Future Energy Efficient Data Centers With Disaggregated Servers. Journal of Lightwave Technology, 35(24), (2017): 5361–5380.
[20] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P. P., Kolodziej, J., Balaji, P., . . . Zomaya, A. A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7), (2014): 751–774.
[21] Ge, Y., Zhang, Y., Qiu, Q., Lu, Y.-H. A game theoretic resource allocation for overall energy minimization in mobile cloud computing system. Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design - ISLPED ’12., (2012): 279-284.
[22] Kliazovich, D., Arzo, S. T., Granelli, F., Bouvry, P., Khan, S. U. e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing. 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, (2013):7-13.
[23] Aikebaier, A., Yang, Y., Enokido, T., Takizawa, M. Energy-Efficient Computation Models for Distributed Systems. 2009 International Conference on Network-Based Information Systems (2009): 424-431.
[24] Sharma, B., Wood, T., Das, C. R. HybridMR: A Hierarchical MapReduce Scheduler for Hybrid Data Centers. 2013 IEEE 33rd International Conference on Distributed Computing Systems (2013): 102-111.
[25] Gao, Y., Guan, H., Qi, Z., Song, T., Huan, F., Liu, L. Service level agreement based energy-efficient resource management in cloud data centers. Computers and Electrical Engineering, 40(5), (2014): 1621–1633.
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Как цитировать
Joldasbayev, S., Balakayeva, G. T., Aidarov, K. A., & Phillips, C. (2019). Динамическое распределение запросов для повышения качества обслуживания. Вестник КазНУ. Серия математика, механика, информатика, 100(4), 18–27. https://doi.org/10.26577/JMMCS-2018-4-570
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