臺灣博碩士論文加值系統

結合網絡功能虛擬化（NFV）和多接取邊緣運算（MEC）環境，網絡服務提供商可以在邊緣伺服器供給商（ESP）所擁有的邊緣伺服器上部署其基於NFV 的網絡服務以提供使用者使用。對於網路服務提供商來說，決定要向哪個ESP 租用資源並支付多少來部署其網路服務成為網絡服務提供商的重要問題。而對於ESP 來說，確定要服務哪個網絡服務以及從其提供商收取多少費用也是一個重要問題。我們為此匹配市場提出了一種雙層機制的匹配賽局。上層是網絡服務和ESP 之間的討價還價過程，我們使用的是匹配拍賣的模型; 下層是ESP 內對VNF 的資源分配，我們使用的是一對多匹配模型。我們提出的機制可以提供一個弱穩定的結果，也就是說，沒有一個網路服務可以讓所有的ESP 所匹配到的對象產生更好的利潤。但是，可能存在一個網絡服務，如果它改變目前配對到的對象可以獲得更多的利潤。我們模擬了我們的方法，並且顯示出我們的方法平均可以讓較多的網路服務被部署，同時ESP 和網絡服務提供商都可以獲得更高的利潤。

With both Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) environment, network service providers can deploy their NFV-based network services on the edge servers maintained by edge service providers (ESPs) to serve end users. Nevertheless, determining which ESP to lease resource and how much to pay for the deployment will become an important issue for the network service providers. For the ESPs, determining which network serive to serve and how much to charge from its provider is also an important issue. We proposed a two-layer mechanism of matching game for the deployment of network services to the ESPs. Specifically, the upper layer is the bargaining process between network services and ESPs, we use matching auction; the lower layer is resource allocation for VNFs within an ESP, we use one-to-many matching model. The proposed mechanism can provide a weakly stable result, that is, there will be no network service that is more favorable than all ESPs' current matching results. However, there may exist a network service which can have more profit if it change its current partner. We simulated the proposed mechanisms and showed that we can have a higher average number of served network services, and both ESPs and network service providers can have higher profits.

1 Introduction 1

2 Background 4
2.1 System Model 4
2.2 Problem Formulation 6
2.3 Other Concerns 8

3 Proposed Mechanisms 10
3.1 Basic Concept 10
3.2 Two-layer Matching Based Mechanism 13
3.2.1 Bargaining Process 13
3.2.2 Intra-ESP resource allocation 17
3.3 Discussion 20
3.3.1 An Example of BP-DNSES 20
3.3.2 Stability in BP-DESNS 23

4 Simulation Results 24
4.1 Environment Settings 24
4.2 Result Analysis 27
4.2.1 The Effect of The Number of Network Services 28
4.2.2 The Effect of The Delta 32

5 Conclusion 34

References 35

[1] M. Chiosi, D. Clarke, P. Willis, A. Reid, J. Feger, M. Bugenhagen, W. Khan, M. Fargano, C. Cui, H. Deng, et al., “Network functions virtualisation introductory white paper,” in SDN and OpenFlow World Congress, 2012.
[2] A. Leivadeas, M. Falkner, I. Lambadaris, and G. Kesidis, “Optimal virtualized network function allocation for an sdn enabled cloud,” Computer Standards & Interfaces, vol. 54, pp. 266–278, 2017.
[3] D. Dietrich, C. Papagianni, P. Papadimitriou, and J. S. Baras, “Network function placement on virtualized cellular cores,” in 2017 9th International Conference on Communication Systems and Networks (COMSNETS), IEEE, 2017, pp. 259–266.
[4] Q. Sun, P. Lu, W. Lu, and Z. Zhu, “Forecast-assisted nfv service chain deployment based on affiliation-aware vnf placement,” in 2016 IEEE Global Communications Conference (GLOBECOM), IEEE, 2016, pp. 1–6.
[5] T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, and D. Sabella, “On multi-access edge computing: A survey of the emerging 5g network edge cloud architecture and orchestration,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1657–1681, 2017.
[6] Y. C. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young, “Mobile edge computing—a key technology towards 5g,” ETSI white paper, vol. 11, no. 11, pp. 1–16, 2015.
[7] F. Z. Yousaf, M. Bredel, S. Schaller, and F. Schneider, “Nfv and sdn—key technology enablers for 5g networks,” IEEE journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2468–2478, 2017.
[8] V. Sciancalepore, F. Giust, K. Samdanis, and Z. Yousaf, “A double-tier mec-nfv architecture: Design and optimisation,” in 2016 IEEE Conference on standards for communications and networking (CSCN), IEEE, 2016, pp. 1–6.
[9] A. Corradi, M. Fanelli, and L. Foschini, “Vm consolidation: A real case based on openstack cloud,” Future Generation Computer Systems, vol. 32, pp. 118–127, 2014.
[10] Amazon ec2 spot instances, https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-requests.html, Accessed: 2019-06-01.
[11] Amazon ec2 grows 62 in 2 years, https://huanliu.wordpress.com/2014/02/26/amazon-ec2-grows-62-in-2-years/, Accessed: 2014-02-26.
[12] D. Gale and L. S. Shapley, “College admissions and the stability of marriage,” The American Mathematical Monthly, vol. 69, no. 1, pp. 9–15, 1962.
[13] S. Bayat, R. H. Louie, B. Vucetic, and Y. Li, “Dynamic decentralised algorithms for cognitive radio relay networks with multiple primary and secondary users utilising matching theory,” Transactions on Emerging Telecommunications Technologies, vol. 24, no. 5, pp. 486–502, 2013.
[14] H. Xu and B. Li, “Anchor: A versatile and efficient framework for resource management in the cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1066–1076, 2012.
[15] L. S. Shapley and M. Shubik, “The assignment game i: The core,” International Journal of game theory, vol. 1, no. 1, pp. 111–130, 1971.
[16] D. Fershtman and A. Pavan, “Matching auctions,” The Economist, 2015.
[17] L. Mashayekhy, M. M. Nejad, and D. Grosu, “A ptas mechanism for provisioning and allocation of heterogeneous cloud resources,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 9, pp. 2386–2399, 2015, issn: 1045-9219. doi: 10.1109/TPDS.2014.2355228.
[18] L. A. Adamic and B. A. Huberman, “Zipf’s law and the internet.,” Glottometrics, vol. 3, no. 1, pp. 143–150, 2002.
[19] D. F. Manlove and C. T. Sng, “Popular matchings in the capacitated house allocation problem,” in European Symposium on Algorithms, Springer, 2006, pp. 492–503.
[20] A. Abdulkadiroğlu and T. Sönmez, “School choice: A mechanism design approach,” American economic review, vol. 93, no. 3, pp. 729–747, 2003.