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Deneyim Kalitesi odaklı Akıllı İşbirlikçi Çoklu-erişimli Uç Hesaplama Çerçevesi

Year 2019, Volume: 4 Issue: 3, 8 - 18, 25.12.2019

Abstract

Günümüzün gelişmiş radyo erişim teknolojileri, yüksek-tanımlı büyük veri/video trafiğinin hızlı ve güvenilir iletimini kolaylaştırmış olsa da, kullanıcılar tarafından deneyimlenen/algılanan kalite, kaynak (örneğin, bulut sunucuları) ve uç-nokta arasındaki mesafeye dayalı yüksek gecikme nedeniyle beklenenden düşüktür. 5G ağ erişim teknolojisiyle ivmelenen çoklu-erişimli uç hesaplama (MEC) paradigmasının, bu sorunu MEC sunucularını uç-noktaların yoğunlaştığı ağ kenarlarında dağıtarak, ve hesaplama, içerik dağıtımı, depolama ve sanallaştırma işlemlerinin önemli bir kısmını bu uçlar üzerinden gerçekleştirerek çözmesi beklenmektedir. Yalnız, MEC sunucularının kendi aralarında veya kaynaklar ile uç-noktalar arasında iletişim kurmasını sağlamak için de yeni ve karmaşık protokollere ihtiyaç duyulmaktadır. Çalışma kapsamında, haberleşmenin sadece yardımcı bir bilgi alışverişi sağlayacağı varsayımından farklı olarak, araç ağlarda servis ve deneyim kalitesini ve yolcu konforunu artırmayı amaçlayan, yeni nesil kablosuz ve hücresel ağ erişim teknolojileri ile yazılım-tanımlı ağların (SDN), çoklu-erişimli uç hesaplama (MEC) ve makine öğrenmesi (ML) tekniklerinin entegrasyonunun sağlandığı, yeni ve özgün bir mimari önerilmektedir. Önerilen çalışma ile, sadece 5G/IEEE 802.11ac/p gibi yeni nesil ağ erişim teknolojileri ve özellikleri sistem optimizasyonuna dahil edilmeyecek, aynı zamanda sistem tasarımı işbirlikçi ve sistematik bir perspektiften köklü değişikliklere uğratılacaktır. Önerilen çözümün, temel olarak yüksek güvenilirlik ve düşük gecikme arasındaki ikilemi çözmesi ve sonuç olarak hızlı ve güvenilir olan uygun fiyatlı araç iletişiminin gerçekleştirilmesini ve ticarileştirilmesini hızlandırması hedeflenmektedir.

References

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Zhang et al., “Cooperative Interference Mitigation and Handover Management for Heterogeneous Cloud Small Cell Networks,” IEEE Wireless Commun., vol. 22, no. 3, June 2015, pp. 92–99. [35] H. Song, X. Fang, and L. Yan, “Handover Scheme for 5G C/U Plane Split Heterogeneous Network in High-Speed Railway,” IEEE Trans. Vehic. Tech., vol. 63, no. 9, Nov. 2014, pp. 4633–46. [36] Polese, Michele, et al. "Machine learning at the edge: A data-driven architecture with applications to 5G cellular networks." arXiv preprint arXiv:1808.07647 (2018). [37] Zhang, Yaomin, et al. "Software-defined and fog-computing-based next generation vehicular networks." IEEE Communications Magazine 56.9 (2018): 34-41. [38] R. Trestian, K. Katrinis, and G. M. Muntean, ‘OFLoad: An OpenFlow-based Dynamic Load Balancing Strategy for Datacenter Networks’, IEEE Transactions on Network and Service Management, vol. PP, no. 99, pp. 1–1, 2017. [39] A. Al-Jawad, P. Shah, O. Gemikonakli, and R. Trestian, ‘Policy-based QoS Management Framework for Software-Defined Networks’, in 2018 International Symposium on Networks, Computers and Communications (ISNCC), 2018, pp. 1–6. [40] Tuysuz MF, Ankarali ZK, Gozüpek D., (2017) “A Survey on Energy Efficiency in Software Defined Networks”, Computer Networks, Vol. 113, Pages 188-204. [41] I. Comşa et al., ‘Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management’, IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1661–1675, Dec. 2018. [42] O. A. Ezenwigbo, V. V. Paranthaman, G. Mapp, and R. Trestian, ‘Exploring Intelligent Service Migration in Vehicular Networks’, in Testbeds and Research Infrastructures for the Development of Networks and Communities, 2019, pp. 41–61.
Year 2019, Volume: 4 Issue: 3, 8 - 18, 25.12.2019

Abstract

References

  • [1] Information and communications technology, Wikipedia, https://en.wikipedia.org/wiki/Information_and_ communications_technology. [2] J. Wan, et al., “VCMIA: A Novel Architecture for Integrating Vehicular Cyber-Physical Systems and Mobile Cloud Computing,” Mobile Networks and Applications, vol. 19, no. 2, 2014, pp. 153–60. [3] J. Jiang et al., “An Efficient Distributed Trust Model for Wireless Sensor Networks,” IEEE Trans. Parallel and Distrib. Sys., vol. 26, no. 5, 2015, pp. 1228–37. [4] J. Liu et al., “A Survey on Position-Based Routing for Vehicular Ad Hoc Networks,” Telecommun. Sys., vol. 62, no. 1, 2016, pp. 15–30. [5] Gartner’s report, http://www.gartner.com/newsroom/id/3165317. [6] Technology and requirements for self-driving cars, https://www .intel.com/content/www/us/en/ automotive/driving-safetyadvanced- driver-assistance-systems-self-driving-technologypaper. html [7] S. Tachi, M. Inami, and Y. Uema, “Augmented reality helps drivers see around blind spots,” IEEE Spectrum, vol. 31, 2014. [8] J. Wan et al., “Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles,” Sensors, vol. 16, no. 1, 2016, pp. 88. [9] G. Han et al., “Green Routing Protocols for Wireless Multimedia Sensor Networks,” IEEE Wireless Commun., vol. 23, no. 6, Dec. 2016, pp. 140–46; DOI 10.1109/ MWC.2016.1400052WC. [10] J. Wu et al., “Augmented Reality Multi-View Video Scheduling Under Vehicle-Pedestrian Situations,” Proc. ICCVE, 2015, pp. 163–68. [11] M. Nekovee, “Radio Technologies for Spectrum above 6 GHz — A Key Component of 5G,” Proc. 5G Radio Tech. Seminar: Exploring Technical Challenges in the Emerging 5G Ecosystem, IET, 2015, pp. 1–46. [12] J. Lee et al., “LTE-Advanced in 3GPP Rel-13/14: An Evolution Toward 5G,” IEEE Commun. Mag., vol. 54, no. 3, Mar. 2016, pp. 36–42. [13] J. Wan et al., “Software-Defined Industrial Internet of Things in the Context of Industry 4.0,” IEEE Sensors J., vol. 16, no. 20, 2016, pp. 7373–80. [14] S. Sezer et al., “Are We Ready for SDN? Implementation Challenges for Software-Defined Networks,” IEEE Commun. Mag., vol. 51, no. 7, July 2013, pp. 36–43. [15] N. McKeown et al., “OpenFlow: Enabling Innovation in Campus Networks,” ACM SIGCOMM Comp. Commun. Rev., vol. 38, no. 2, 2008, pp. 69–74. [16] T. Luo et al., “Sensor OpenFlow: Enabling Software-Defined Wireless Sensor Networks,” IEEE Commun. Lett., vol. 16, no. 11, 2012, pp. 1896–99. [17] J. Liu et al., “High-Efficiency Urban Traffic Management in Context-Aware Computing and 5G Communication,” IEEE Commun. Mag., vol. 55, no. 1, Jan. 2017, pp. 34–40. [18] M. Vouk, “Cloud computing issues, research and implementations,” Journal of Computing and Information Technology, vol. 16, pp. 235–246, 2008. [19] D. Rountree and I. Castrillo, e basics of cloud computing: Understanding the fundamentals of cloud computing in theory and practice, 2013. [20] S. Olariu, I. Khalil, and M. Abuelela, “Taking VANET to the clouds,” International Journal of Pervasive Computing and Communications, vol. 7, no. 1, pp. 7–21, 2011. [21] M. Shojafar, N. Cordeschi, and E. Baccarelli, “Energy-efficient adaptive resource management for real-time vehicular cloud services,” IEEE Transactions on Cloud Computing, pp. 1-1, 2016. [22] M. Whaiduzzaman, M. Sookhak, A. Gani, and R. Buyya, “A survey on vehicular cloud computing,” Journal of Network and Computer Applications, vol. 40, no. 1, pp. 325–344, 2014. [23] V. G. Menon and P. Joe Prathap, “Moving from vehicular cloud computing to vehicular fog computing: Issues and challenges,” International Journal of Computer Science and Engineering, vol. 9, no. 2, 2017. [24] M. Chiang and T. Zhang, “Fog and IoT: an overview of research opportunities,” IEEE Internet of ings Journal, vol. 3, no. 6, pp. 854–864, 2016. [25] A. V. Dastjerdi and R. Buyya, “Fog Computing: Helping the Internet of Things Realize Its Potential,” e Computer Journal, vol. 49, no. 8, Article ID 7543455, pp. 112–116, 2016. [26] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” in Proceedings of the ACM conference on Mobile cloud computing first edition of the MCC workshop, pp. 13–16, 2012. [27] J. Feng, Z. Liu, C. Wu, and Y. Ji, “AVE: Autonomous vehicular edge computing framework with ACO-based scheduling,” IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 10660– 10675, 2017. [28] X. Hou, Y. Li,M. Chen, D. Wu,D. Jin, and S. Chen, “Vehicular fog computing: a viewpoint of vehicles as the infrastructures,” IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 3860–3873, 2016. [29] D. J. Deng et al., “Latency Control in Software-Defined Mobile-Edge Vehicular Networking,” IEEE Commun. Mag., vol. 55, no. 8, Aug. 2017, pp. 87–93. [30] X. Ge, Z. Li, and S. Li, “5G Software Defined Vehicular Networks,” IEEE Commun. Mag., vol. 55, no. 7, July 2017, pp. 87–93. [31] X. Duan, Y. Liu, and X. Wang, “SDN Enabled 5G-VANET: Adaptive Vehicle Clustering and Beamformed Transmission for Aggregated Traffic,” IEEE Commun. Mag., vol. 55, no. 7, July 2017, pp. 120–27. [32] R. Yu et al., “Optimal Resource Sharing in 5G-Enabled Vehicular Networks: A Matrix Game Approach,” IEEE Trans. Vehic. Tech., vol. 65, no. 10, Oct. 2016, pp. 7844–56. [33] J. Huang et al., “Resource Allocation for Multi-Cell Device-to-Device Communications Underlaying 5G Networks: A Game-Theoretic Mechanism with Incomplete Information,” IEEE Trans. Vehic. Tech., vol. 67, no. 3, Mar. 2018, pp. 2557–70. [34] H. Zhang et al., “Cooperative Interference Mitigation and Handover Management for Heterogeneous Cloud Small Cell Networks,” IEEE Wireless Commun., vol. 22, no. 3, June 2015, pp. 92–99. [35] H. Song, X. Fang, and L. Yan, “Handover Scheme for 5G C/U Plane Split Heterogeneous Network in High-Speed Railway,” IEEE Trans. Vehic. Tech., vol. 63, no. 9, Nov. 2014, pp. 4633–46. [36] Polese, Michele, et al. "Machine learning at the edge: A data-driven architecture with applications to 5G cellular networks." arXiv preprint arXiv:1808.07647 (2018). [37] Zhang, Yaomin, et al. "Software-defined and fog-computing-based next generation vehicular networks." IEEE Communications Magazine 56.9 (2018): 34-41. [38] R. Trestian, K. Katrinis, and G. M. Muntean, ‘OFLoad: An OpenFlow-based Dynamic Load Balancing Strategy for Datacenter Networks’, IEEE Transactions on Network and Service Management, vol. PP, no. 99, pp. 1–1, 2017. [39] A. Al-Jawad, P. Shah, O. Gemikonakli, and R. Trestian, ‘Policy-based QoS Management Framework for Software-Defined Networks’, in 2018 International Symposium on Networks, Computers and Communications (ISNCC), 2018, pp. 1–6. [40] Tuysuz MF, Ankarali ZK, Gozüpek D., (2017) “A Survey on Energy Efficiency in Software Defined Networks”, Computer Networks, Vol. 113, Pages 188-204. [41] I. Comşa et al., ‘Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management’, IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1661–1675, Dec. 2018. [42] O. A. Ezenwigbo, V. V. Paranthaman, G. Mapp, and R. Trestian, ‘Exploring Intelligent Service Migration in Vehicular Networks’, in Testbeds and Research Infrastructures for the Development of Networks and Communities, 2019, pp. 41–61.
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Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

M. Fatih Tüysüz 0000-0002-8955-9710

Publication Date December 25, 2019
Submission Date December 9, 2019
Acceptance Date December 22, 2019
Published in Issue Year 2019 Volume: 4 Issue: 3

Cite

APA Tüysüz, M. F. (2019). Deneyim Kalitesi odaklı Akıllı İşbirlikçi Çoklu-erişimli Uç Hesaplama Çerçevesi. Harran Üniversitesi Mühendislik Dergisi, 4(3), 8-18.