在共享的軟體定義網路中，控制器應提供流(用戶)與流之間的隔離保證，以預測 網路性能並最大程度地減少某些惡意流的攻擊。通常，數據包通過安裝在流表中的 流規則轉發，並且流表的容量通常受高耗能和高成本的限制，因此可容納流的數量 有限。迄今為止，OpenFlow 1.4.0 引進了流規則替換，當流表已滿時，它允許用 新規則替換現有流規則。這稱為流表溢出。儘管流表溢出可能導致數據包延遲增 加，但是我們在軟體定義網路測試平台上進行的實驗表明，我們可以稍微超額預 定流表資源來接納更多流，進而提高網路效能。在本論文的第一部分中，我們解 決了有限流表容量隔離保證問題(BOLA)，其目的是在有流表容量的限制下，最 大化最小流進展同時確保每個流分配的頻寬不超過其需求。我們首先證明BOLA是 NP-hard 的問題且不存在任何近似演算法。然後，我們提出一個嘗試性的演算法。 最後，我們討論如何通過電腦模擬與使用軟體定義網路測試平台進行仿真實驗去評 估我們設計的演算法。在本文的第二部分，我們討論了流表超額預訂隔離保證問題 (FOLA)，目的在最大化最小流進展並最小化最大流表溢出。為此，我們設計了 一個保證最小進展和最大流表溢出的演算法。然後，我們討論如何在軟體定義網路 測試平台上進行仿真實驗以評估我們提出的演算法，並與目前最先進的方法比較。 仿真實驗的結果顯示了我們的方法在最小流進展與網路效能勝過目前最新的方法。

In a shared software-defined network (SDN), the controller should provide isolation guarantees across flows (users) to predict network performance and minimize dis- ruption from some malicious flows. Normally, packets are forwarded by flow rules installed in flow tables, and the capacity of flow tables is usually limited by power and cost constraints so that a limited number of flows can be accommodated. To date, OpenFlow 1.4.0 introduces the flow rule replacement, which allows replacing existing flow rules with new ones once the flow table is full. This is called flow table overflow. Although flow table overflow may lead to an increase in packet delay, our experiments on an SDN testbed show that the network performance could benefit by admitting more flows through slightly overbooking the flow table resource. In the first part of this dissertation, we address the BOunded flow table capacity isoLation guArantees problem (BOLA), which aims to maximize minimum progress of flows while ensuring the bandwidth allocated to a flow does not exceed its demand and the flow table capacity constraints are imposed. We first show BOLA is NP-hard and intractable to devise any approximation algorithm. Then, we propose an heuristic algorithm for BOLA. Finally, we evaluate the proposed algorithm through computer simulations and experiments on an SDN testbed using real-life traces. In the second part of this dissertation, we address the Flow table Overbooking isoLation guArantees problem (FOLA), which aims to maximize minimum progress of flows and minimize maximum flow table overflow. To that end, we design an algorithm with guaranteed minimum progress and bounded maximum flow table overflow. Trace-driven experiments on an SDN testbed show that our solution outperforms state-of-the-art methods for maxi- mizing minimum progress in terms of the minimum progress and network throughput.

[1] S. Orlowski, R. Wessa ̈ly, M. Pio ́ro, and A. Tomaszewski, “SNDlib 1.0-survivable network design library,” Networks: An International Journal, vol. 55, no. 3, pp. 276–286, 2010.
[2] “GENI: Global environment for network innovations.” [Online]. Available: https://www.geni.net/
[3] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rex- ford, S. Shenker, and J. Turner, “Openflow: enabling innovation in campus networks,” ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69–74, 2008.
[4] U. Hoelzle, “Opening address: 2012 open network summit,”
http://www.opennetsummit.org/archives/apr12/hoelzle-tue-openflow.pdf, Date Retrieved, vol. 8, no. 08, p. 2014, 2012.
[5] E. Rosen, A. Viswanathan, R. Callon et al., “Multiprotocol label switching ar- chitecture,” 2001.
[6] L. Huang, Q. Shen, F. Zhou, and W. Shao, “Label space reduction based on LSP multiplexing in MPLS Openflow hybrid network,” Computer Communications, 2018.
[7] A. Bashandy, C. Filsfils, S. Previdi, B. Decraene, S. Litkowski, and R. Shakir, “Segment Routing with MPLS data plane,” draft-ietf-spring-segmentrouting- mpls-14 (work in progress), IETF, 2018.
[8] D. Kreutz, F. M. Ramos, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, “Software-defined networking: A comprehensive survey,” Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76, 2014.
[9] S. Yasukawa, A. Farrel, and O. Komolafe, “An analysis of scaling issues in mpls- te core networks,” IETF RFC 5439, 2009.
[10] R. Cohen, L. Lewin-Eytan, J. S. Naor, and D. Raz, “On the effect of forwarding table size on sdn network utilization,” in IEEE INFOCOM 2014-IEEE confer- ence on computer communications. IEEE, 2014, pp. 1734–1742.
[11] X.-N. Nguyen, D. Saucez, C. Barakat, and T. Turletti, “Officer: A general op- timization framework for openflow rule allocation and endpoint policy enforce- ment,” in 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, 2015, pp. 478–486.
[12] A. Gushchin, A. Walid, and A. Tang, “Enabling service function chaining through routing optimization in software defined networks,” in 2015 53rd An- nual Allerton Conference on Communication, Control, and Computing (Aller-
ton). IEEE, 2015, pp. 573–581.
[13] J. Wang, C. Qiao, and H. Yu, “On progressive network recovery after a major disruption,” in 2011 Proceedings IEEE INFOCOM. IEEE, 2011, pp. 1925–1933.
[14] M. Chiesa, G. Kindler, and M. Schapira, “Traffic engineering with equal-cost- multipath: An algorithmic perspective,” IEEE/ACM Transactions on Network- ing, vol. 25, no. 2, pp. 779–792, 2016.
[15] S. Paris, A. Destounis, L. Maggi, G. S. Paschos, and J. Leguay, “Controlling flow reconfigurations in sdn,” in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 2016, pp. 1–9.
[16] E. Danna, S. Mandal, and A. Singh, “A practical algorithm for balancing the max-min fairness and throughput objectives in traffic engineering,” in 2012 Pro- ceedings IEEE INFOCOM. IEEE, 2012, pp. 846–854.
[17] E. Danna, A. Hassidim, H. Kaplan, A. Kumar, Y. Mansour, D. Raz, and M. Segalov, “Upward max min fairness,” in 2012 Proceedings IEEE INFOCOM. IEEE, 2012, pp. 837–845.
[18] G. R ́etva ́ri, J. J. B ́ıro ́, and T. Cinkler, “Fairness in capacitated networks: A polyhedral approach,” in IEEE INFOCOM 2007-26th IEEE International Con- ference on Computer Communications. IEEE, 2007, pp. 1604–1612.
[19] D. Nace and M. Pio ́ro, “Max-min fairness and its applications to routing and load-balancing in communication networks: a tutorial,” IEEE Communications Surveys & Tutorials, vol. 10, no. 4, pp. 5–17, 2008.
[20] M. Allalouf and Y. Shavitt, “Centralized and distributed algorithms for routing and weighted max-min fair bandwidth allocation,” IEEE/ACM Transactions on networking, vol. 16, no. 5, pp. 1015–1024, 2008.
[21] L. Wang, W. Wang, and B. Li, “Utopia: Near-optimal coflow scheduling with isolation guarantee,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 2018, pp. 891–899.
[22] L. Wang and W. Wang, “Fair coflow scheduling without prior knowledge,” in
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2018, pp. 22–32.
[23] S. Agarwal, M. Kodialam, and T. Lakshman, “Traffic engineering in software defined networks,” in 2013 Proceedings IEEE INFOCOM. IEEE, 2013, pp. 2211–2219.
[24] L. Liu, X. Cao, Y. Cheng, L. Du, W. Song, and Y. Wang, “Energy-efficient capacity optimization in wireless networks,” in IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, 2014, pp. 1384–1392.
[25] J. He, X. Zhao, and B. Zhao, “Joint request routing and video adaptation in collaborative vod systems,” in 2013 IEEE Wireless Communications and Net- working Conference (WCNC). IEEE, 2013, pp. 1920–1925.
[26] Z. Cao, M. Kodialam, and T. Lakshman, “Traffic steering in software defined networks: Planning and online routing,” in Proceedings of the 2014 ACM SIG- COMM workshop on Distributed cloud computing, 2014, pp. 65–70.
[27] N. Garg and J. Koenemann, “Faster and simpler algorithms for multicommod- ity flow and other fractional packing problems,” SIAM Journal on Computing, vol. 37, no. 2, pp. 630–652, 2007.
[28] L. K. Fleischer, “Approximating fractional multicommodity flow independent of the number of commodities,” SIAM Journal on Discrete Mathematics, vol. 13, no. 4, pp. 505–520, 2000.
[29] G. Karakostas, “Faster approximation schemes for fractional multicommodity flow problems,” ACM Transactions on Algorithms (TALG), vol. 4, no. 1, pp. 1–17, 2008.
[30] H. Farmanbar and H. Zhang, “Traffic engineering for software-defined radio ac- cess networks,” in 2014 IEEE Network Operations and Management Symposium
(NOMS). IEEE, 2014, pp. 1–7.
[31] F. Giroire, J. Moulierac, and T. K. Phan, “Optimizing rule placement in software-defined networks for energy-aware routing,” in 2014 IEEE Global Com- munications Conference. IEEE, 2014, pp. 2523–2529.
[32] H. Huang, P. Li, S. Guo, and B. Ye, “The joint optimization of rules allocation and traffic engineering in software defined network,” in 2014 IEEE 22nd Inter- national Symposium of Quality of Service (IWQoS). IEEE, 2014, pp. 141–146.
[33] X.-N. Nguyen, D. Saucez, C. Barakat, and T. Turletti, “Rules placement problem in openflow networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1273–1286, 2015.
[34] T. Cheng, K. Wang, L.-C. Wang, and C.-W. Lee, “An in-switch rule caching and replacement algorithm in software defined networks,” in 2018 IEEE International Conference on Communications (ICC). IEEE, 2018, pp. 1–6.
[35] H. Yang and G. F. Riley, “Machine learning based proactive flow entry dele- tion for openflow,” in 2018 IEEE International Conference on Communications
(ICC). IEEE, 2018, pp. 1–6.
[36] ——, “Machine learning based flow entry eviction for openflow switches,” in
2018 27th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2018, pp. 1–8.
[37] C. Zhang, H. Yang, and G. F. Riley, “Admission control in software-defined dat- acenter network in view of flow table capacity,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2018, pp. 871–876.
[38] Z. Guo, R. Liu, Y. Xu, A. Gushchin, A. Walid, and H. J. Chao, “Star: Preventing flow-table overflow in software-defined networks,” Computer Networks, vol. 125, pp. 15–25, 2017.
[39] C. Zhang and D. M. Blough, “High satisfaction and fair allocation of resources in software-defined data center networks,” in ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 2019, pp. 1–6.
[40] M. Huang, W. Liang, Z. Xu, W. Xu, S. Guo, and Y. Xu, “Dynamic routing for network throughput maximization in software-defined networks,” in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 2016, pp. 1–9.
[41] H. Xu, S. Chen, Q. Ma, and L. Huang, “Lightweight flow distribution for collab- orative traffic measurement in software defined networks,” in IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019, pp. 1108– 1116.
[42] Q. Fu, E. Sun, Q. Wang, Z. Wang, and Y. Zhang, “A joint balancing flow table and reducing delay scheme for mice-flows in data center networks,” in 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019, pp. 1–6.
[43] E. Zahavi, A. Shpiner, O. Rottenstreich, A. Kolodny, and I. Keslassy, “Links as a service (laas): Guaranteed tenant isolation in the shared cloud,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 5, pp. 1072–1084, 2019.
[44] K. Lei, J. Huang, Y. Li, F. Zhang, H. Susanto, B. Bai, G. Zhang, and J. Liu, “Hommo: A hierarchical flow management framework for multi-objective data center networks,” in 2019 IEEE Global Communications Conference (GLOBE-
COM). IEEE, 2019, pp. 1–6.
[45] M. R. Abbasi, A. Guleria, and M. S. Devi, “Traffic engineering in software defined networks: a survey,” Journal of Telecommunications and Information Technology, 2016.
[46] R. Karp, “Reducibility among combinatorial problems,” in Complexity of Com- puter Computations. Plenum Press, 1972.
[47] J. Y. Yen, “An algorithm for finding shortest routes from all source nodes to a given destination in general networks,” Quarterly of Applied Mathematics, vol. 27, no. 4, pp. 526–530, 1970.
[48] “Iperf.” [Online]. Available: https://iperf.fr
[49] M. Pizzutti and A. E. Schaeffer-Filho, “Adaptive multipath routing based on hybrid data and control plane operation,” in IEEE INFOCOM 2019-IEEE Con- ference on Computer Communications. IEEE, 2019, pp. 730–738.
[50] F. Hao, M. Kodialam, and T. Lakshman, “Optimizing restoration with segment routing,” in IEEE INFOCOM 2016-The 35th Annual IEEE International Con- ference on Computer Communications. IEEE, 2016, pp. 1–9.
[51] L. Dai, Y. Xue, B. Chang, Y. Cao, and Y. Cui, “Integrating traffic estimation and routing optimization for multi-radio multi-channel wireless mesh networks,” in IEEE INFOCOM 2008-The 27th Conference on Computer Communications. IEEE, 2008, pp. 71–75.
[52] P.-J. Wan, Z. Wang, L. Wang, Z. Wan, and S. Ji, “From least interference-cost paths to maximum (concurrent) multiflow in mc-mr wireless networks,” in IEEE INFOCOM2014-IEEEConferenceonComputerCommunications. IEEE,2014, pp. 334–342.
[53] J. He and W. Song, “Optimizing video request routing in mobile networks with built-in content caching,” IEEE Transactions on Mobile Computing, vol. 15, no. 7, pp. 1714–1727, 2015.
[54] I. Gurobi Optimization, “Gurobi optimizer reference manual,” URL http://www.gurobi.com, 2018.