SDN中基于过程优化的动态负载均衡策略

doi:10.19678/j.issn.1000-3428.0065173

摘要/Abstract

摘要：

在软件定义网络中，多控制器静态部署策略无法适应网络中流量动态变化，存在控制平面负载不均衡的问题。提出基于过程优化的动态负载均衡策略。通过优化原始的交换机迁移方案，以均衡各控制器负载作为目标，设计理想迁出流量和迁移交换机选取概率模型，选取待迁移交换机并确定迁入控制器。综合考虑时延与迁移代价对网络性能的影响，针对蚁群算法中收敛速度较慢且容易陷入局部最优的问题，通过对启发函数和信息素更新公式进行改进，并引入非线性递减挥发系数，为每个迁移交换机选取最佳的迁入控制器。为避免不同待迁移交换机选取同一个迁入控制器造成迁入控制器过载，通过设计交换机顺序迁移算法，实现交换机的协调迁移。仿真实验结果表明，与MUCS、NM、CDAA等策略相比，该策略在面对复杂的流量情景时，迁移交换机和迁入控制器的选取更加合理，在保证各控制器负载均衡的同时，迁移代价和流建立时间平均降低10.3%和36.1%，交换机与控制器之间时延平均降低13.6%。

关键词: 软件定义网络, 多控制器, 交换机迁移, 负载均衡, 过程优化

Abstract:

To address the issue of control plane load imbalance in Software Defined Network(SDN), where static deployment strategies for multiple controllers cannot adapt to dynamic changes in network traffic, a dynamic load balancing strategy based on process optimization is proposed.The original switch migration scheme is optimized as follows.To balance the load of each controller, ideal migration traffic is designed and a statistical model is selected for the migration switch.The switch to be migrated and the migration controller are thus determined.Considering the impact of delay and migration cost on network performance, improvements are made to the heuristic function and pheromone update formula in Ant Colony Optimization(ACO) algorithms.These improvements addressed the slow rate of convergence and tendency to fall into local optimization.Additionally, a nonlinear decreasing volatility coefficient is introduced to select the best migration controller for each migration switch. To avoid overloading the migration controller by selecting the same one for different switches, a switch sequential migration algorithm is designed to achieve coordinated migration of switches.The simulation results show that compared to MUCS, NM, CDAA dynamic load balancing strategies, the proposed strategy provides a more reasonable approach to selecting migration switches and migration controllers in complex traffic scenarios.It achieves a balanced load for each controller, reduces migration cost by an average of 10.3%, decreases flow setup time by an average of 36.1%, and reduces latency between switches and controllers by an average of 13.6%.

Key words: Software Defined Network(SDN), multiple controllers, switch migration, load balancing, process optimization

刘向举, 赵犇, 方贤进, 徐杨洋. SDN中基于过程优化的动态负载均衡策略[J]. 计算机工程, 2023, 49(8): 137-145.

Xiangju LIU, Ben ZHAO, Xianjin FANG, Yangyang XU. Dynamic Load Balancing Strategy Based on Process Optimization in SDN[J]. Computer Engineering, 2023, 49(8): 137-145.

https://www.ecice06.com/CN/Y2023/V49/I8/137

图/表 8

图1 SDN交换机与多控制器连接示意图

Fig.1 Schematic diagram of connection between SDN switch and multiple controllers

图2 基于过程优化的动态负载均衡策略架构

Fig.2 Architecture of dynamic load balancing based on process optimization strategy

图3 基于过程优化的动态负载均衡策略流程

Fig.3 Procedure of dynamic load balancing based on process optimization strategy

图4 迁移前后各控制器的负载比

Fig.4 Load ratio of each controllers before and after migration

图5 控制器使用不同策略的负载均衡度对比

Fig.5 Load balance comparison of controller using different strategies

图6 不同策略的迁移代价对比

Fig.6 Migration cost comparison among different strategies

图7 不同策略的流建立时间对比

Fig.7 Flow setup time comparison among different strategies

图8 交换机与控制器之间的传播时延

Fig.8 Propagation delay between switch and controller

参考文献 25

1	ALHARTHI M, TAHA A E M, HASSANEIN H S. Dynamic controller placement in software defined drone networks[C]//Proceedings of Global Communications Conference. Washington D. C., USA: IEEE Press, 2019: 1-6.
2	ALSAEEDI M, MOHAMAD M M, AL-ROUBAIEY A A. Toward adaptive and scalable OpenFlow-SDN flow control: a survey. IEEE Access, 2019, 7, 107346- 107379. doi: 10.1109/ACCESS.2019.2932422
3	贾吾财, 吕光宏, 王桂芝, 等. SDN多控制器放置问题研究综述. 计算机科学, 2020, 47 (7): 206- 212. URL
	JIA W C, LÜ G H, WANG G Z, et al. Review on placement of multiple controllers in SDN. Computer Science, 2020, 47 (7): 206- 212. URL
4	NEGHABI A A, JAFARI NAVIMIPOUR N, HOSSEINZADEH M, et al. Load balancing mechanisms in the software defined networks: a systematic and comprehensive review of the literature. IEEE Access, 2018, 6, 14159- 14178. doi: 10.1109/ACCESS.2018.2805842
5	刘佳美, 徐巧枝. 基于机器学习的SDN网络流量预测与部署策略. 计算机工程, 2020, 46 (10): 223- 230. URL
	LIU J M, XU Q Z. Network traffic prediction and deployment strategy based on machine learning for SDN. Computer Engineering, 2020, 46 (10): 223- 230. URL
6	赵季红, 孙天骜, 曲桦, 等. 一种改进社区检测算法的SDN控制器部署策略. 计算机工程, 2020, 46 (11): 207- 213. URL
	ZHAO J H, SUN T A, QU Y, et al. An SDN controller deployment strategy for improved community detection algorithm. Computer Engineering, 2020, 46 (11): 207- 213. URL
7	胡涛, 张建辉, 邬江, 等. SDN中基于分布式决策的控制器负载均衡机制. 电子学报, 2018, 46 (10): 2316- 2324. URL
	HU T, ZHANG J H, WU J, et al. Controller load balancing mechanism based on distributed policy in SDN. Acta Electronica Sinica, 2018, 46 (10): 2316- 2324. URL
8	AL-TAM F, CORREIA N. On load balancing via switch migration in software-defined networking. IEEE Access, 2019, 7, 95998- 96010. doi: 10.1109/ACCESS.2019.2929651
9	李婉, 沈苏彬, 吴振宇. 一种基于多SDN控制器的交换机迁移机制. 计算机技术与发展, 2018, 28 (1): 89-94, 99 URL
	LI W, SHEN S B, WU Z Y. A switch migration mechanism based on multiple SDN-controllers. Computer Technology and Development, 2018, 28 (1): 89-94, 99 URL
10	LI J M, HU X L, ZHANG M Q. Research on dynamic switch migration strategy based on FMOPSO[C]//Proceedings of the 3rd Advanced Information Technology, Electronic and Automation Control Conference. Washington D. C., USA: IEEE Press, 2018: 913-917.
11	LAI W K, WANG Y C, CHEN Y C, et al. TSSM: time-sharing switch migration to balance loads of distributed SDN controllers. IEEE Transactions on Network and Service Management, 2022, 19 (2): 1585- 1597.
12	LAN W J, LI F M, LIU X H, et al. A dynamic load balancing mechanism for distributed controllers in software-defined networking[C]//Proceedings of the 10th International Conference on Measuring Technology and Mechatronics Automation. Washington D. C., USA: IEEE Press, 2018: 259-262.
13	陆杰, 张震, 胡涛. 基于可靠性与负载优化的多控制器弹性部署算法. 计算机工程, 2018, 44 (8): 135- 141. URL
	LU J, ZHANG Z, HU T. Multi-controllers elastic placement algorithm based on reliability and load optimization. Computer Engineering, 2018, 44 (8): 135- 141. URL
14	ALY W H F. Controller adaptive load balancing for SDN networks[C]//Proceedings of the 17th International Conference on Ubiquitous and Future Networks. Washington D. C., USA: IEEE Press, 2019: 514-519.
15	左攀, 束永安. DCN中基于前馈神经网络的动态多路径负载均衡方法. 计算机工程, 2021, 47 (9): 113- 119. URL
	ZUO P, SHU Y A. Dynamic multi-path load balancing method based on feedforward neural network in DCN. Computer Engineering, 2021, 47 (9): 113- 119. URL
16	DORIGO M, BIRATTARI M, STUTZLE T. Ant colony optimization. IEEE Computational Intelligence Magazine, 2006, 1 (4): 28- 39.
17	MCKEOWN N, ANDERSON T, BALAKRISHNAN H, et al. OpenFlow. ACM SIGCOMM Computer Communication Review, 2008, 38 (2): 69- 74.
18	CHENG G Z, CHEN H C. Game model for switch migrations in software-defined network. Electronics Letters, 2014, 50 (23): 1699- 1700.
19	ADEKOYA O, ANEIBA A, PATWARY M. An improved switch migration decision algorithm for SDN load balancing. IEEE Open Journal of the Communications Society, 2020, 1, 1602- 1613.
20	KNIGHT S, NGUYEN H X, FALKNER N, et al. The Internet topology zoo. IEEE Journal on Selected Areas in Communications, 2011, 29 (9): 1765- 1775.
21	HU T, YI P, GUO Z H, et al. Bidirectional matching strategy for multi-controller deployment in distributed software defined networking. IEEE Access, 2018, 6, 14946- 14953.
22	CHENG G Z, CHEN H C, WANG Z M, et al. DHA: distributed decisions on the switch migration toward a scalable SDN control plane[C]//Proceedings of IFIP Networking Conference. Washington D. C., USA: IEEE Press, 2015: 1-9.
23	DIXIT A A, HAO F, MUKHERJEE S, et al. ElastiCon: an elastic distributed SDN controller[C]//Proceedings of the 10th Symposium on Architectures for Networking and Communications Systems. New York, USA: ACM Press, 2014: 17-28.
24	LI G Y, WANG X Q, ZHANG Z G. SDN-based load balancing scheme for multi-controller deployment. IEEE Access, 2019, 7, 39612- 39622.
25	IBRAHIM A A Z, HASHIM F, SALI A, et al. A modified genetic algorithm for controller placement problem in SDN distributed network[C]//Proceedings of the 26th Asia-Pacific Conference on Communications. Washington D. C., USA: IEEE Press, 2021: 83-88.

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