بهبود توازن بار پویا و زمان پاسخ در شبکه‌های نرم‌افزارمحور با بهره‌گیری از الگوریتم‌های برنامه‌ریزی آرمانی چند منظوره‌ی فازی
محورهای موضوعی : فناوری اطلاعات و ارتباطات
محمدرضا فرقانی
1
,
محمدرضا سلطان آقایی کوپائی
2
*
,
فرساد زمانی بروجنی
3
1 - دانشگاه آزاد اسلامی واحد اصفهان (خوراسگان)
2 - دانشگاه آزاد اسلامی واحد اصفهان(خوراسگان)
3 - دانشگاه آزاد اسلامی واحد اصفهان ( خوراسگان)
کلید واژه: شبکه نرم¬افزار محور, توازن بار پویا, بهینه¬سازی چند منظوره, الگوریتم رأی¬گیری فازی,
چکیده مقاله :
شبکه‌های نرم‌افزارمحور به عنوان یک رویکرد کارآمد در حوزه فناوری ارتباطات شناخته شده‌اند که هدف آن¬ها بهبود عملکرد و بهره‌وری شبکه‌های کامپیوتری است و در نتیجه کاهش هزینه‌ها را به همراه دارند. یکی از چالش‌های اساسی در شبکه‌های نرم‌افزارمحور، توازن بار بین گره‌ها است. حل این چالش باعث بهبود زمان پاسخ و عملکرد شبکه می‌شود. امروزه روش‌های متعددی برای توازن بار در شبکه‌های نرم‌افزارمحور ارائه شده است، اما هنوز به وضعیت ایده‌آل نرسیده‌اند. در این مقاله، یک روش جدید برای بهبود توازن بار و کاهش زمان پاسخ ارائه می‌شود. این روش از الگوریتم‌های برنامه‌ریزی آرمانی چند منظوره و وزن‌دهی فازی بهره می‌برد. در روش پیشنهادی، فاکتورهایی مانند پهنای باند، وضعیت ترافیک، لینک بافر و مسیریاب مد نظر قرار می‌گیرند و بهترین مسیر و مسیریاب با توازن بار مطلوب برای جریان‌های اطلاعات با کمترین زمان انتخاب می‌شوند. یکی از مزایای بارز این روش، امکان انجام توازن بار به صورت خودکار و بدون نیاز به مداخله انسان است. نتایج تجربی نشان می‌دهد که روش پیشنهادی نسبت به روش‌های دیگر، بهبود قابل توجهی در زمان پاسخ حدود 14.8 درصد را نشان می¬دهد و همچنین توازن بار شبکه‌های نرم‌افزارمحور را حفظ می‌کند. با استفاده از روش پیشنهادی، علاوه بر بهبود کیفیت سرویس و رضایت کاربران، زمان پاسخ نیز بهبود خواهد یافت. به طور خلاصه، روش پیشنهادی به عنوان یک رویکرد قابل استفاده در شبکه‌های نرم‌افزارمحور مطرح است و نسبت به روش‌های موجود برتری دارد.
Software-Defined Networking (SDN) has been recognized as an efficient approach in the field of communication technology, aiming to improve the performance and efficiency of computer networks, thus reducing costs. One of the key challenges in SDN is load balancing among nodes. Solving this challenge leads to improved response time and network performance. Nowadays, various methods have been proposed for load balancing in SDN, but they have not yet reached the ideal state. In this article, a new method is presented to enhance load balancing and reduce response time. This method utilizes multi-objective evolutionary algorithms and fuzzy weighting. In the proposed method, factors such as bandwidth, traffic status, link buffer, and desired router are taken into account, and the best path and router with desired load balancing for information flows are selected with the minimum time. One prominent advantage of this method is the possibility of performing load balancing automatically without the need for human intervention. Experimental results demonstrate that the proposed method shows a significant improvement of approximately 14.8% in response time compared to other methods, while maintaining load balancing in SDNs. By using the proposed method, in addition to improving service quality and user satisfaction, response time will also be enhanced. In summary, the proposed method is introduced as a viable approach in SDNs and exhibits superiority over existing methods.
[1] R. A. Ammal, P. Sajimon, and S. Vinodchandra, "Termite inspired algorithm for traffic engineering in hybrid software defined networks," PeerJ Computer Science, vol. 6, p. e283, 2020.
[2] L. Nkenyereye, L. Nkenyereye, B. Adhi Tama, A. G. Reddy, and J. Song, "Software-defined vehicular cloud networks: architecture, applications and virtual machine migration," Sensors, vol. 20, no. 4, p. 1092, 2020.
[3] K. Rui, H. Pan, and S. Shu, "Secure routing in the Internet of Things (IoT) with intrusion detection capability based on software-defined networking (SDN) and Machine Learning techniques," Scientific Reports, vol. 13, no. 1, p. 18003, 2023/10/21 2023, doi: 10.1038/s41598-023-44764-6.
[4] K. Luo, "A distributed SDN-based intrusion detection system for IoT using optimized forests," (in eng), PLoS One, vol. 18, no. 8, p. e0290694, 2023, doi: 10.1371/journal.pone.0290694.
[5] I. Smołka and J. Stój, "Utilization of SDN Technology for Flexible EtherCAT Networks Applications," Sensors, vol. 22, no. 5, p. 1944, 2022. [Online]. Available: https://www.mdpi.com/1424-8220/22/5/1944.
[6] J. Gong and A. Rezaeipanah, "A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks," (in eng), Multimed Tools Appl, pp. 1-30, Jan 23 2023, doi: 10.1007/s11042-023-14349-6.
[7] Z. Liu, X. Dong, L. Wang, J. Feng, C. Pan, and Y. Li, "Satellite Network Task Deployment Method Based on SDN and ICN," (in eng), Sensors (Basel), vol. 22, no. 14, Jul 21 2022, doi: 10.3390/s22145439.
[8] C. Urrea and D. Benitez, "Software-Defined Networking Solutions, Architecture and Controllers for the Industrial Internet of Things: A Review," (in eng), Sensors (Basel), vol. 21, no. 19, Oct 1 2021, doi: 10.3390/s21196585.
[9] A. Savaliya, R. H. Jhaveri, Q. Xin, S. Alqithami, S. Ramani, and T. A. Ahanger, "Securing industrial communication with software-defined networking," (in eng), Math Biosci Eng, vol. 18, no. 6, pp. 8298-8313, Sep 22 2021, doi: 10.3934/mbe.2021411.
[10] D. Wang et al., "DoSDefender: A Kernel-Mode TCP DoS Prevention in Software-Defined Networking," (in eng), Sensors (Basel), vol. 23, no. 12, Jun 8 2023, doi: 10.3390/s23125426.
[11] Z. B. Zuo, R. Y. He, X. W. Zhu, and C. W. Chang, "A novel software-defined network packet security tunnel forwarding mechanism," (in eng), Math Biosci Eng, vol. 16, no. 5, pp. 4359-4381, May 17 2019, doi: 10.3934/mbe.2019217.
[12] Y. Guo et al., "Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration," (in eng), Sensors (Basel), vol. 23, no. 16, Aug 10 2023, doi: 10.3390/s23167091.
[13] L. Li, K. Li, X. Meng, Y. Wang, and X. Wang, "Dynamic weight routing and optical-code algorithm based on SDN," (in eng), Heliyon, vol. 9, no. 1, p. e12407, Jan 2023, doi: 10.1016/j.heliyon.2022.e12407.
[14] M. Hussain, N. Shah, R. Amin, S. S. Alshamrani, A. Alotaibi, and S. M. Raza, "Software-Defined Networking: Categories, Analysis, and Future Directions," (in eng), Sensors (Basel), vol. 22, no. 15, Jul 25 2022, doi: 10.3390/s22155551.
[15] M. Hamzei, S. Khandagh, and N. Jafari Navimipour, "A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm," (in eng), Sensors (Basel), vol. 23, no. 16, Aug 17 2023, doi: 10.3390/s23167233.
[16] G. Yuan, Y. Yang, G. Tian, and A. M. Fathollahi-Fard, "Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm," (in eng), Environ Sci Pollut Res Int, Jan 31 2022, doi: 10.1007/s11356-022-18883-y.
[17] H. Li, D. Ou, and Y. Ji, "An Environmentally Sustainable Software-Defined Networking Data Dissemination Method for Mixed Traffic Flows in RSU Clouds with Energy Restriction," (in eng), Int J Environ Res Public Health, vol. 19, no. 22, Nov 16 2022, doi: 10.3390/ijerph192215112.
[18] H. Xue, K. T. Kim, and H. Y. Youn, "Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization," (in eng), Sensors (Basel), vol. 19, no. 2, Jan 14 2019, doi: 10.3390/s19020311.
[19] X. Xu, W. K. Jia, Y. Wu, and X. Wang, "On the Optimal Lawful Intercept Access Points Placement Problem in Hybrid Software-Defined Networks," (in eng), Sensors (Basel), vol. 21, no. 2, Jan 9 2021, doi: 10.3390/s21020428.
[20] Z. Kabiri, B. Barekatain, and A. Avokh, "GOP-SDN: an enhanced load balancing method based on genetic and optimized particle swarm optimization algorithm in distributed SDNs," Wireless Networks, vol. 28, no. 6, pp. 2533-2552, 2022.
[21] R. Sharma, I. Sharma, and A. Sharma, "Load Balancing and Resource Utilization Approach in Cloud Computing Using Honey Bee-Inspired Algorithm," in International Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020, 2021: Springer, pp. 811-820.
[22] S. Ejaz, Z. Iqbal, P. A. Shah, B. H. Bukhari, A. Ali, and F. Aadil, "Traffic load balancing using software defined networking (SDN) controller as virtualized network function," IEEE Access, vol. 7, pp. 46646-46658, 2019.
[23] O. Adekoya, A. Aneiba, and M. Patwary, "An improved switch migration decision algorithm for SDN load balancing," IEEE Open Journal of the Communications Society, vol. 1, pp. 1602-1613, 2020.
[24] Y. Zhao, X. Wang, Q. He, C. Zhang, and M. Huang, "PLOFR: An online flow route framework for power saving and load balance in SDN," IEEE Systems Journal, vol. 15, no. 1, pp. 526-537, 2020.
[25] X. Shi et al., "An openflow-based load balancing strategy in SDN," Comput. Mater. Contin, vol. 62, no. 1, pp. 385-398, 2020.
[26] H. Babbar, S. Rani, D. Gupta, H. M. Aljahdali, A. Singh, and F. Al-Turjman, "Load balancing algorithm on the immense scale of internet of things in SDN for smart cities," Sustainability, vol. 13, no. 17, p. 9587, 2021.
[27] A. El Kamel and H. Youssef, "Improving switch-to-controller assignment with load balancing in multi-controller software defined WAN (SD-WAN)," Journal of Network and Systems Management, vol. 28, pp. 553-575, 2020.
[28] K. S. Sahoo, M. Tiwary, B. Sahoo, B. K. Mishra, S. RamaSubbaReddy, and A. K. Luhach, "RTSM: Response time optimisation during switch migration in software‐defined wide area network," IET wireless sensor systems, vol. 10, no. 3, pp. 105-111, 2020.
[29] G. S. Begam, M. Sangeetha, and N. Shanker, "Load balancing in dcn servers through sdn machine learning algorithm," Arabian Journal for Science and Engineering, pp. 1-12, 2022.
[30] K. A. Jadhav, M. M. Mulla, and D. Narayan, "An efficient load balancing mechanism in software defined networks," in 2020 12th international conference on computational intelligence and communication networks (CICN), 2020: IEEE, pp. 116-122.
[31] G. Li, X. Wang, and Z. J. I. A. Zhang, "SDN-based load balancing scheme for multi-controller deployment," vol. 7, pp. 39612-39622, 2019.
[32] Z. Li and E. Peng, "Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking," (in eng), Sensors (Basel), vol. 21, no. 3, Feb 1 2021, doi: 10.3390/s21030955.