مکانیزم کنترل ازدحام مبتنی بر مهاجرت برای شبکه های نرم افزارمحور های چند دامنه ای

نوع مقاله : علمی-پژوهشی

نویسندگان

1 گروه مهندسی کامپیوتر، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران

2 گروه مهندسی کامپیوتر، واحد تفت، دانشگاه آزاد اسلامی، تفت، ایران

3 گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران

چکیده

با توسعه مستمر شبکه های نرم افزاری تعریف شده (SDN)، نیاز به استفاده از معماری توزیع شده در صفحه کنترل این شبکه ها افزایش می یابد. یکی از مهم ترین چالش ها در این شبکه ها، بار متغیر روی کنترلرها است و بار زیاد باعث ازدحام می شود. افزایش ازدحام می تواند کارایی شبکه را به شدت کاهش دهد. اگرچه مطالعات مختلف برای حل مسئله ازدحام تلاش کرده اند، اما نتوانسته اند تبادل بار بین صفحه کنترل و صفحه داده را به طور موثر مدیریت کنند. در این مقاله، یک مکانیسم کنترل تراکم مبتنی بر مهاجرت برای SDN های چند دامنه ای پیشنهاد شده است. در این مکانیزم، زمانی که یک کنترلر تحت بار زیاد است و ازدحام رخ می دهد، سوئیچ های انتخاب شده از یک کنترلر با بار زیاد به یک کنترلر با بار کمتر منتقل می شوند. در صورتی که مهاجرت باعث ازدحام در کنترلر جدید شود، مکانیسم سوئیچ ها را بین کنترلرها با کمترین میزان مهاجرت تعویض می کند تا از تراکم در سایر کنترلرها با الهام از الگوریتم Kadane جلوگیری کند. مکانیسم پیشنهادی با ابزارهای D-ITG و IPerf و کنترل‌کننده RYU مورد تجزیه و تحلیل قرار گرفت و نشان داد که عملکرد سیستم بهبود می‌یابد. علاوه بر این مقایسه میان روش پیشنهادی و روش OptiGSM نشان می دهد که روش پیشنهادی گذردهی بهتر و تاخیر پایین تر دارد هر چند روش OptiGSM لرزش کمتری دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Switch Migration-based Congestion Control Mechanism for Multi-domain SDNs

نویسندگان [English]

  • Mohammad Reza Jenabzadeh 1
  • Vahid Ayatollahitafti 2
  • Mohammad Reza MollaKhalili Meybodi 3
  • Mohammad Reza Mollahoseini Ardakani 3
1 Department of Computer Engineering, Ya. C., Islamic Azad University, Yazd Iran.
2 Department of Computer Engineering, Taft. C., Islamic Azad University, Taft, Iran.
3 3Department of Computer Engineering, May. C., Islamic Azad University, Maybod Iran.
چکیده [English]

With the continuous advancement of Software-Defined Networks (SDNs), the adoption of a distributed control plane architecture has become increasingly necessary. One of the primary challenges in these networks is the variable load on the controllers where high loads can lead to congestion. Such congestion can significantly degrade network efficiency. Although previous studies have attempted to address this issue, they have largely failed to effectively manage load exchange between the control plane and the data plane. This paper proposes a migration-based congestion control mechanism for multi-domain SDNs. In this approach, when a controller experiences high load and congestion, selected switches are migrated from the overloaded controller to one with a lower load. If the migration risks congesting the new controller, the mechanism swaps switches between controllers with minimal migrations, drawing inspiration from the Kadane algorithm to prevent congestion elsewhere. The proposed mechanism was evaluated using the D-ITG and IPerf tools with the RYU controller, demonstrating improved system performance. Simulation results show that the mechanism outperforms the baseline approach, increasing average network throughput by approximately 10%, while reducing average delay and jitter by about 30% and 25%, respectively. Furthermore, a comparison between the proposed method and the OptiGSM method reveals that the proposed method offers superior throughput and lower delay, although the OptiGSM method exhibits less jitter than the proposed method

کلیدواژه‌ها [English]

  • Software-defined networks
  • switch migration
  • Congestion control
  • RYU controller

مراجع

[1] Parsaei, M. Reza, R. Mohammadi, and R. Javidan. “A new adaptive traffic engineering method for telesurgery using ACO algorithm over software defined networks.”European Research in Telemedicine/La Recherche Europeenne en Telemedecine, vol. 6, no. 3-4, pp. 173-180, 2017.
[2] S. Rowshanrad, V. Abdi, M. Keshtgari, “Performance evaluation of sdn controllers: Floodlight and opendaylight.”  IIUM Engineering Journal , vol. 17, no. 2, pp. 47–57, 2016.          
[3]  C.Y. Chu, K. Xi, M. Luo, H.J. Chao, “Congestion-aware single link failure recovery in hybrid SDN networks.” In 2015 IEEE Conference on Computer Communications (INFOCOM) (IEEE, 2015), pp. 1086–1094.
[4]  S. Song, J. Lee, K. Son, H. Jung, J. Lee, “A congestion avoidance algorithm in SDN environment.” In 2016 International Conference on Information Networking (ICOIN) (IEEE, 2016), pp. 420–423.
[5] T. Zhu, D. Feng, F. Wang, Y. Hua, Q. Shi, Y. Xie, Y. Wan, “A congestion-aware and robust multicast protocol in sdn-based data center networks.”  Journal of Network and Computer Applications 95, 105–117 (2017).
[6]  Hu, Y., Peng, T., & Zhang, L. (2017). “Software‐Defined Congestion Control Algorithm for IP Networks.” Scientific Programming, 2017(1), 3579540.
[7]  M. Rahman, N. Yaakob, A. Amir, R. Ahmad, S. Yoon, A. Abd Halim, “Performance analysis of congestion control mechanism in software defined network (SDN),” In MATEC Web of Conferences, vol. 140 (EDP Sciences, 2017), p. 01033.
[8]  S.Y. Wang, L.M. Chen, S.K. Lin, L.C. Tseng,” Using SDN congestion controls to ensure zero packet loss in storage area networks,” In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (IEEE, 2017), pp. 490–496.
[9]  D. Shen, W. Yan, Y. Peng, Y. Fu, Q. Deng, “Congestion control and traffic scheduling for collaborative crowdsourcing in sdn enabled mobile wireless networks.”  Wireless Communications and Mobile Computing 2018, 1–11 (2018).
[10]  M.M. Tajiki, B. Akbari, M. Shojafar, S.H. Ghasemi, M.L. Barazandeh, N. Mokari, L. Chiaraviglio, M. Zink, “Cect: Computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers.”  Cluster Computing 21, 1881–1897 (2018).
[11]  J. Zhao, M. Tong, H. Qu, J. Zhao, “An Intelligent Congestion Control Method in Software Defined Networks,” In 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN) (IEEE, 2019), pp. 51–56.
[12]  K. Lei, Y. Liang, W. Li, “Congestion control in sdn-based networks via multi-task deep reinforcement learning.”  IEEE Network 34(4), 28–34 (2020).
[13]  Y.J. Chen, L.C. Wang, M.C. Chen, P.M. Huang, P.J. Chung, “Sdn-enabled traffic-aware load balancing for m2m networks.”  IEEE Internet of Things Journal 5(3), 1797–1806 (2018).
[14]  M.L. Chiang, H.S. Cheng, H.Y. Liu, C.Y. Chiang, “Sdn-based server clusters with dynamic load balancing and performance improvement.” Cluster Computing 24, 537–558 (2021).
[15]  J. Zhang, M. Ye, Z. Guo, C.Y. Yen, H.J. Chao, “CFR-RL: Traffic engineering with reinforcement learning in sdn.”  IEEE Journal on Selected Areas in Communications 38(10), 2249–2259 (2020).
[16]  Y.F. Yankam, V.K. Tchendji, J.F. Myoupo, “Wos-coms: Work stealing-based congestion management scheme for sdn programmable networks.”  Journal of Network and Systems Management 32(1), 23 (2024).
[17]  G. Diel, C.C. Miers, M.A. Pillon, G.P. Koslovski, “Rscat: Towards zero touch congestion control based on actor–critic reinforcement learning and software-defined networking.”  Journal of Network and Computer Applications 215, 103639 (2023).
[18]  U. Prajapati, B.C. Chatterjee, A. Banerjee, “ Optigsm: Greedy-based load balancing with minimum switch migrations in software-defined networks.” IEEE Transactions on Network and Service Management 21, 2200-2210 (2023).
[19] Y. Darmani, M. Sangelaji. "QDFSN: QoS-enabled Dynamic and Programmable Framework for SDN." Tabriz Journal of Electrical Engineering 51, no. 1 , 1-10 (2021)
[20] A.Ghorbannia Delavar, K. Beigi. "ESV-DBRA: An enhanced method for proportional distribution of the multitenant SDN traffic load." Tabriz Journal of Electrical Engineering  52, no. 4, 269-280 (2022).
[21]  B. Xiong, X. Peng, J. Zhao, “A concise queuing model for controller performance in software-defined networks”.  J. Comput. 11(3), 232–237 (2016).
[22]  U. Srisamarn, L. Pradittasnee, N. Kitsuwan, “Resolving load imbalance state for sdn by minimizing maximum load of controllers.”  Journal of Network and Systems Management 29(4), 46 (2021).
[23]  O. Adekoya, A. Aneiba, M. Patwary, “An improved switch migration decision algorithm for sdn load balancing.” IEEE Open Journal of the Communications Society 1, 1602–1613 (2020).
[24] Y. Zhou, K. Zheng, W. Ni, R.P. Liu, “Elastic switch migration for control plane load balancing in sdn.”  IEEE Access 6, 3909–3919 (2018).
[25]  M.T. Islam, N. Islam, M.A. Refat, “Node to node performance evaluation through ryu sdn controller.”  Wireless Personal Communications 112, 555–570 (2020).
[26]  S. Bhardwaj, S.N. Panda, “ Performance evaluation using ryu sdn controller in software-defined networking environment.” Wireless Personal Communications 122(1), 701–723 (2022).

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