Dynamic request distribution for enhanced Quality of Service
DOI:
https://doi.org/10.26577/JMMCS-2018-4-570Keywords:
Quality of Service, QoS, dynamic balancing, resource allocation, load balancing, queuingAbstract
The emergence of Web-services as an open components supporting flexible and inexpensive set
of distributed applications as well as exploiting them as a promising solution for integration with
other application and providers of software-hardware resources is very much in demand. Using
Web-services simplifies and enhances system functionality because of the availability of programs
interaction with each other through Internet using open protocols. Thereby it is necessary to provide
QoS provision issues such as distribution of request streams, enhanced efficiency of operations
at peak loads. One of the ways to tackle these issues is to apply dynamic request distribution which
ensures continuity of data transfer and processing ignoring crashes and equipment failures, redistribution
of data flow to the available nodes in case of their loss. Abovementioned can be achieved
using certain heuristics based on methods of Mathematical statistics and Probability modeling.
Moreover, we provide analysis of methods and models developed for QoS provision.
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[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.
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[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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How to Cite
Joldasbayev, S., Balakayeva, G. T., Aidarov, K. A., & Phillips, C. (2019). Dynamic request distribution for enhanced Quality of Service. Journal of Mathematics, Mechanics and Computer Science, 100(4), 18–27. https://doi.org/10.26577/JMMCS-2018-4-570
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Computer Science
