Статті в журналах: "Load balancing in SDN"

1

Sufiev, Hadar, Yoram Haddad, Leonid Barenboim, and José Soler. "Dynamic SDN Controller Load Balancing." Future Internet 11, no. 3 (March 21, 2019): 75. http://dx.doi.org/10.3390/fi11030075.

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The software defined networking (SDN) paradigm separates the control plane from the data plane, where an SDN controller receives requests from its connected switches and manages the operation of the switches under its control. Reassignments between switches and their controllers are performed dynamically, in order to balance the load over SDN controllers. In order to perform load balancing, most dynamic assignment solutions use a central element to gather information requests for reassignment of switches. Increasing the number of controllers causes a scalability problem, when one super controller is used for all controllers and gathers information from all switches. In a large network, the distances between the controllers is sometimes a constraint for assigning them switches. In this paper, a new approach is presented to solve the well-known load balancing problem in the SDN control plane. This approach implies less load on the central element and meeting the maximum distance constraint allowed between controllers. An architecture with two levels of load balancing is defined. At the top level, the main component called Super Controller, arranges the controllers in clusters, so that there is a balance between the loads of the clusters. At the bottom level, in each cluster there is a dedicated controller called Master Controller, which performs a reassignment of the switches in order to balance the loads between the controllers. We provide a two-phase algorithm, called Dynamic Controllers Clustering algorithm, for the top level of load balancing operation. The load balancing operation takes place at regular intervals. The length of the cycle in which the operation is performed can be shorter, since the top-level operation can run independently of the bottom level operation. Shortening cycle time allows for more accurate results of load balancing. Theoretical analysis demonstrates that our algorithm provides a near-optimal solution. Simulation results show that our dynamic clustering improves fixed clustering by a multiplicative factor of 5.

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2

Perepelkin, D. A., and V. T. Nguyen. "RESEARCH OF LOAD BALANCING PROCESSES IN SOFTWARE-DEFINED NETWORKS BASED ON GENETIC ALGORITHM." Vestnik of Ryazan State Radio Engineering University 77 (2021): 43–57. http://dx.doi.org/10.21667/1995-4565-2021-77-43-57.

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As a new network paradigm, software-defined networks (SDN) are able to cope with the limitations of traditional networks. SDNs use a management controller with a global view of the network and switching devices that act as packet forwarding equipment, known as «OpenFlow switches». Network resources limitations and ensuring quality of service requirements lead to an important need for load balancing between SDN switches. The purpose of work  research and analysis of load balancing processes in SDN based on genetic algorithm. To confirm the effectiveness and correctness of the genetic algorithm in SDN, the software for modeling the load balancing processes has been developed. The simulation results confirmed the efficiency of the genetic algorithm in SDN for balancing and redistributing network traffic in order to ensure the required quality of service and reduce network congestion.

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3

Babbar, Himanshi, and Shalli Rani. "Emerging Prospects and Trends in Software Defined Networking." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4236–41. http://dx.doi.org/10.1166/jctn.2019.8506.

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In today’s era Software Defined Networking (SDN) has accumulated popularity in both the Industry and Academia. SDN is massively facilitated by different sectors, there is an abundant amount of work going on in the studies where SDN is required. Improving the load balancing in SDN plays an important role in solving the problem in the specific domain. Since 2009, publications in this field are getting doubled approximately, it takes a short time for bibliometric analysis. This paper facilitates the comprehensive survey on “SDN Load Balancing” for the fixed frame of timeline. 530 publications related to the Load Balancing in SDN were scrutinized based on the database of Scopus. This paper investigated the publications of research on multiple parameters: 1. Publishing patterns e.g., Authors and affiliations 2. Keywords to examine the specific domain 3. Analysis of keywords 4. Citation patterns 5. Several publications. Lastly, it examines the survey of literature based on the quantifiable structures of SDN load balancing on several perspectives. The suggested analytical study will act as an influential instrument for substantial discussion of impending research schemes.

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4

Wahanani, Henni Endah, Mohammad Idhom, and Eka Prakarsa Mandyartha. "LOAD BALANCING TOPOLOGI BIPARTITE PADA JARINGAN SDN." Prosiding Seminar Nasional Informatika Bela Negara 2 (November 25, 2021): 7–10. http://dx.doi.org/10.33005/santika.v2i0.77.

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Layanan teknologi telah berkembang dengan keandalan yang tinggi, oleh karena itu diperlukan sebuah konsep sistem kendali terpusat untuk mengatur perangkat jaringan pada sebuah infrastruktur jaringan yang disebut SDN (Software Defined Network), dengan memisahkan antara sistem kontrol (control plane) dan sistem forwarding (data plane). Pengontrol dapat memberikan kontrol terpusat dengan menginstal aturan penerusan dalam bidang data, dan switch melakukan operasi yang berbeda pada paket sesuai dengan aturan ini. Cara komunikasi antara perangkat dan controller menggunakan sebuah protokol yang disebut dengan Openflow. Untuk mendukung SDN diperlukannya sebuah metode untuk mendistribusikan trafik jaringan komputer secara seimbang agar trafik jaringan komputer berjalan secara maksimal, metode itu adalah load balancing. Dalam penelitian ini melakukan ujicoba load balancing topologi bipartite di ujicoba pada 3 paket yaitu UDP Flow, DNS, dan Telnet dengan parameter yang diuji adalah delay dan packet rate dengan mengirimkan 1000 paket dengan ukuran setiap paket 100Kb selama 60s dengan background trafik setiap link 100 Mbit/s. Hasil dari pengujian delay yang terkecil terdapat pada paket DNS topologi 1 dengan 11,382 ms, dan packet rate terbesar pada paket telnet dengan 92,02 pkt/s.

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5

Shrivastava, Gourav, Praveen Kaushik, and R. K.Pateriya. "Load balancing strategies in software defined networks." International Journal of Engineering & Technology 7, no. 3 (August 22, 2018): 1854. http://dx.doi.org/10.14419/ijet.v7i3.14017.

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In the past few years, network requirements have been changing frequently as the amount of data traffic increasing exponentially so it is difficult to utilize the full capacity of network resources. Software Defined Networking (SDN) is emerging as a new networking technology which decouples the control plane from the data plane in the network devices. Separation of control and data plane allows a network administrator a better control over network management and also enables new development through network programmability. Presently Open-Flow is the most popular SDN protocol which provides communication between network devices and controller. In this paper, the Round Robin algorithm is compared with the Dynamic load balancing algorithm using the OpenFlow protocol in SDN under varying load conditions of TCP and UDP traffic. Experimental analysis shows that the dynamic load balancing strategy works better than the Round Robin load balancing.

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6

Kaliuzhnyi, Oleksandr. "METHOD FOR ORGANIZING MULTIPATH ROUTING IN SDN NETWORKS." Scientific review, no. 7(60)2019 (January 10, 2019): 18–28. http://dx.doi.org/10.26886/scientificreview.2311-4517.7(60)2019.2.

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In this work we propose a method for organizing multipath routing for SDN networks. It have two main parts. The fisrt one is a routing method based on modified wave algorithm for finding paths, and second one is load balancing method based on ECMP algorithm. Combination of these methods can optimize using of network resources and provide a more optimal load balancing of network. The basis of routing algorithm is the search and use partially-overlapping routes. The basis of load balancing algorithm is equal distribution network load between all found routes. A comparative analysis with an existing algorithms for routing and load balancing was conducted and the advantages of this development are presented.

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7

Semong, Thabo, Thabiso Maupong, Stephen Anokye, Kefalotse Kehulakae, Setso Dimakatso, Gabanthone Boipelo, and Seth Sarefo. "Intelligent Load Balancing Techniques in Software Defined Networks: A Survey." Electronics 9, no. 7 (July 3, 2020): 1091. http://dx.doi.org/10.3390/electronics9071091.

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In the current technology driven era, the use of devices that connect to the internet has increased significantly. Consequently, there has been a significant increase in internet traffic. Some of the challenges that arise from the increased traffic include, but are not limited to, multiple clients on a single server (which can result in denial of service (DoS)), difficulty in network scalability, and poor service availability. One of the solutions proposed in literature, to mitigate these, is the use of multiple servers with a load balancer. Despite their common use, load balancers, have shown to have some disadvantages, like being vendor specific and non-programmable. To address these disadvantages and improve internet traffic, there has been a paradigm shift which resulted in the introduction of software defined networking (SDN). SDN allows for load balancers that are programmable and provides the flexibility for one to design and implement own load balancing strategies. In this survey, we highlight the key elements of SDN and OpenFlow technology and their effect on load balancing. We provide an overview of the various load balancing schemes in SDN. The overview is based on research challenges, existing solutions, and we give possible future research directions. A summary of emulators/mathematical tools commonly used in the design of intelligent load balancing SDN algorithms is provided. Finally, we outline the performance metrics used to evaluate the algorithms.

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8

Данешманд, Б., and Л. А. Ту. "STUDY AND REVIEW OF SDN-BASED LOAD BALANCING MECHANISMS IN 5G / IMT-2020." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 1 (March 14, 2022): 102–11. http://dx.doi.org/10.36622/vstu.2022.18.1.012.

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Ожидается, что рост числа мобильных устройств и потребность в пользовательских данных к 2030 году окажут беспрецедентное давление на текущую мобильную сеть. У будущих мобильных сетей должно быть несколько требований в отношении объема данных, задержки, качества обслуживания и опыта, мобильности, спектра и энергоэффективности. Поэтому в последнее время начались усилия по созданию более эффективных решений для мобильных сетей. С этой целью балансировка нагрузки привлекла большое внимание как многообещающее решение для более эффективного использования ресурсов, повышения производительности системы и снижения эксплуатационных расходов. Это эффективный способ сбалансировать трафик и уменьшить перегрузку в гетерогенных сетях в будущих сетях 5G / IMT-2020. Балансировка нагрузки - одна из наиболее важных задач, необходимых для максимального повышения производительности, масштабируемости и надежности сети. В настоящее время с появлением программно-конфигурируемых сетей (SDN) балансировка нагрузки для SDN стала важной проблемой в будущей сети 5G / IMT-2020. SDN позволяет использовать программируемые балансировщики нагрузки и обеспечивает гибкость для разработки и реализации стратегий балансировки нагрузки. В этом обзоре мы выделяем методы балансировки нагрузки на основе сетей SDN и предполагаемые требования к балансировке нагрузки в сетях 5G The growing number of mobile devices and the demand for user data by 2030 are expected to put pressure on the current mobile network in an unprecedented way. Future mobile networks must have several requirements regarding data amount, latency, quality of service and experience, mobility, spectrum, and energy efficiency. Therefore, efforts have recently begun for more efficient mobile network solutions. To this end, load balancing has attracted much attention as a promising solution for greater resource utilization, improved system performance, and reduced operating costs. This is an effective way to balance traffic and reduce congestion in heterogeneous networks in future 5G/IMT-2020 networks. Load Balancing is one of the most critical tasks required to maximize network performance, scalability, and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), Load Balancing for SDN has become a significant issue in future network 5G/IMT-2020. SDN allows for programmable load balancers and provides the flexibility to design and implement load balancing strategies. In this survey, we highlight the methods of load balancing based on SDN networks and prospective load balancing requirements on 5G networks

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Chen, Junyan, Yong Wang, Xuefeng Huang, Xiaolan Xie, Hongmei Zhang, and Xiaoye Lu. "ALBLP: Adaptive Load-Balancing Architecture Based on Link-State Prediction in Software-Defined Networking." Wireless Communications and Mobile Computing 2022 (February 12, 2022): 1–16. http://dx.doi.org/10.1155/2022/8354150.

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Load-balancing optimization in software-defined networking (SDN) has been researched for a long time. Researchers have proposed many solutions to the load-balancing problem but have rarely considered the impact of transmission delay between controllers and switches under high-load network conditions. In this paper, we propose an adaptive load-balancing architecture based on link-state prediction (ALBLP) in SDN that can solve the influence of transmission delay between controllers and switches on network load balancing. ALBLP constructs the prediction model of the network link status, adopts the long-term and short-term memory neural network (LSTM) algorithm to predict the network link-state value, and then uses the predicted value as the Dijkstra weight to calculate the optimal path between network hosts. The proposed architecture can adaptively optimize network load balancing and avoid the empty window period, in which the switch flow table does not exist by actively issuing the flow table. Under the network architecture, we collect the data set of the network link-state by simulating the GÉANT network, and we verify the effectiveness of the proposed algorithm. The experiment results show that the ALBLP proposed in this paper can optimize load balancing in SDN and solve the problem of transmission delay between controllers and switches. It has a maximum load-balancing improvement of 23.7% and 11.7% in comparison with the traditional Open Shortest Path First (OSPF) algorithm and the reinforcement learning method based on Q-Learning.

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10

Joshi, Prof Swati, Arnab Dutta, Neeraj Chavan, Gaurav Dhus, and Pratik Bharsakle. "Enhancing Traffic Management and Load Balancing in SDN." IJARCCE 6, no. 4 (April 30, 2014): 794–99. http://dx.doi.org/10.17148/ijarcce.2017.64148.

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11

Shi, Xiaojun, Yangyang Li, Haiyong Xie, Tengfei Yang, Linchao Zhang, Panyu Liu, Heng Zhang, and Zhiyao Liang. "An OpenFlow-Based Load Balancing Strategy in SDN." Computers, Materials & Continua 62, no. 1 (2020): 385–98. http://dx.doi.org/10.32604/cmc.2020.06418.

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12

Ghosh, Anish, and Mrs T. Manoranjitham. "A study on load balancing techniques in SDN." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 174. http://dx.doi.org/10.14419/ijet.v7i2.4.13033.

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Software defined networking(SDN) is a technique in networking which provides the administrators of the network with access to initialize, control, manage, and dynamically change how the network behaves through open interfaces and by the lower-level functioning abstraction. SDN simply addresses the basic knowledge that the architecture being static in traditional networks never provides assistance for the dynamic or scalable computing along along with the storage requirements of most of the modern computing. This is possible by the methods of decoupling or disassociation of the system that helps in making decisions about where the traffic is being delivered from the systems which then forwards this traffic to the required destination. Load balancing is the method in a computer network that is used to divide the amount of work between a collaboration of two or more computers in such a way that work can be completed in the same time limit. Hardware, software, or a combination of both can be used to implement load balancing. Moreover, computer server clustering is caused due to load balancing.This paper discusses the various kinds of load balancing algorithms which can help in better utilisation of resources and linear service delivery across multiple clients in an SDN environment.

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13

Abdelltif, Ahmed Abdelaziz, Ejaz Ahmed, Ang Tang Fong, Abdullah Gani, and Muhammad Imran. "SDN-Based Load Balancing Service for Cloud Servers." IEEE Communications Magazine 56, no. 8 (August 2018): 106–11. http://dx.doi.org/10.1109/mcom.2018.1701016.

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14

Llorens-Carrodeguas, Alejandro, Irian Leyva-Pupo, Cristina Cervelló-Pastor, Luis Piñeiro, and Shuaib Siddiqui. "An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters." Sensors 21, no. 24 (December 11, 2021): 8283. http://dx.doi.org/10.3390/s21248283.

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This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.

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15

Cui, Yunhe, Lianshan Yan, Qing Qian, Huanlai Xing, and Saifei Li. "JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center." Applied Sciences 8, no. 9 (August 28, 2018): 1478. http://dx.doi.org/10.3390/app8091478.

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Server load balancing technology makes services highly functional by distributing the incoming user requests to different servers. Thus, it plays a key role in data centers. However, most of the current server load balancing schemes are designed without considering the impact on the network. More specifically, when using these schemes, the server selection and routing path calculation are usually executed sequentially, which may result in inefficient use of network resources or even cause some issues in the network. As an emerging architecture, Software-Defined Networking (SDN) provides new solutions to overcome these shortcomings. Therefore, taking advantages of SDN, this paper proposes a Joint Server Selection and Traffic Routing algorithm (JSSTR) based on improving the Shuffle Frog Leaping Algorithm (SFLA) to achieve high network utilization, network load balancing and server load balancing. Evaluation results validate that the proposed algorithm can significantly improve network efficiency and balance the network load and server load.

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16

Babbar, Himanshi, Shalli Rani, Divya Gupta, Hani Moaiteq Aljahdali, Aman Singh, and Fadi Al-Turjman. "Load Balancing Algorithm on the Immense Scale of Internet of Things in SDN for Smart Cities." Sustainability 13, no. 17 (August 26, 2021): 9587. http://dx.doi.org/10.3390/su13179587.

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Since the worldwide Internet of Things (IoT) in smart cities is becoming increasingly popular among consumers and the business community, network traffic management is a crucial issue for optimizing the IoT ’s performance in smart cities. Multiple controllers on a immense scale implement in Software Defined Networks (SDN) in integration with Internet of Things (IoT) as an emerging paradigm enhances the scalability, security, privacy, and flexibility of the centralized control plane for smart city applications. The distributed multiple controller implementation model in SDN-IoT cannot conform to the dramatic developments in network traffic which results in a load disparity between controllers, leading to higher packet drop rate, high response time, and other problems with network performance deterioration. This paper lays the foundation on the multiple distributed controller load balancing (MDCLB) algorithm on an immense-scale SDN-IoT for smart cities. A smart city is a residential street that uses information and communication technology (ICT) and the Internet of Things (IoT) to improve its citizens’ quality of living. Researchers then propose the algorithm on the unbalancing of the load using the multiple controllers based on the parameter CPU Utilization in centralized control plane. The experimental results analysis is performed on the emulator named as mininet and validated the results in ryu controller over dynamic load balancing based on Nash bargaining, efficient switch migration load balancing algorithm, efficiency aware load balancing algorithm, and proposed algorithm (MDCLB) algorithm are executed and analyzed based on the parameter CPU Utilization which ensures that the Utilization of CPU with load balancing is 20% better than the Utilization of CPU without load balancing.

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eligar, vijaya, Nalini Iyer, Nihal N.D, Nikhil S.Hugar, YashwantKumar P, and Manjunath M.N. "Load balancing using openday light SDN controller: Case study." International Research Journal on Advanced Science Hub 2, no. 9 (October 29, 2020): 59–64. http://dx.doi.org/10.47392/irjash.2020.149.

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18

Li, Guoyan, Xinqiang Wang, and Zhigang Zhang. "SDN-Based Load Balancing Scheme for Multi-Controller Deployment." IEEE Access 7 (2019): 39612–22. http://dx.doi.org/10.1109/access.2019.2906683.

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19

Chen, Yu-Jia, Li-Chun Wang, Meng-Chieh Chen, Pin-Man Huang, and Pei-Jung Chung. "SDN-Enabled Traffic-Aware Load Balancing for M2M Networks." IEEE Internet of Things Journal 5, no. 3 (June 2018): 1797–806. http://dx.doi.org/10.1109/jiot.2018.2812718.

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20

Aly, Wael Hosny Fouad. "Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks." Journal of Computer Networks and Communications 2019 (December 1, 2019): 1–9. http://dx.doi.org/10.1155/2019/6808693.

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Fault tolerance is an important aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in different environments. The beginning of software-defined networking (SDN) has both presented new challenges and opened a new era to develop new strategies, standards, and architectures to support fault tolerance. In this paper, a study of fault tolerance is performed for two architectures: (1) a single master with multiple slave controllers and (2) multiple slave controllers. The proposed model is called a Generic Controller Adaptive Load Balancing (GCALB) model for SDNs. GCALB adapts the load among slave controllers based on a GCALB algorithm. Mininet simulation tool is utilized for the experimentation phase. Controllers are implemented using floodlights. Experiment results were conducted using GCALB when master controller is taking the responsibility of distributing switches among four and five slave controllers as a case study. Throughput and response time metrics are used to measure performance. GCALB is compared with two reference algorithms: (1) HyperFlow (Kreutz et al., 2012), and (2) Enhanced Controller Fault Tolerant (ECFT) (Aly and Al-anazi, 2018). Results are promising as the performance of GCALB increased by 15% and 12% when compared to HyperFlow and by 13% and 10% when compared to ECFT in terms of throughput and response time.

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21

Sounni, Hind, Najib El kamoun, and Fatima Lakrami. "Load balancing algorithm based-SDN for the IoT applications." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 1209. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1209-1217.

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Nowadays, the emergence of IoT devices has wholly revolutionized the customer's communication habits. The information can be collected at anytime and anywhere. However, the mobility of communication devices in a dense network results in an unbalanced network load and an increase in bandwidth demands. To address these issues, this study proposed a load balancing algorithm based on SDN for enhancing the performance of mobile IoT devices communication over a Wi-Fi network. The use of the SDN makes possible the automatic configuration of the network through a centralized controller, it provides programmability, a global view of the network, it also optimizes resource allocation based on real-time network information that helps implement our algorithm. The proposed algorithm is evaluated through simulation using mininet. The results indicate that our proposed method provides an efficient network load balancing and improves the throughput of associated devices.

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22

Zhong, Hong, Jinpeng Fan, Jie Cui, Yan Xu, and Lu Liu. "Assessing Profit of Prediction for SDN controllers load balancing." Computer Networks 191 (May 2021): 107991. http://dx.doi.org/10.1016/j.comnet.2021.107991.

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23

Mokhtar, Hamza, Xiaoqiang Di, Ying Zhou, Alzubair Hassan, Ziyi Ma, and Shafiu Musa. "Multiple-level threshold load balancing in distributed SDN controllers." Computer Networks 198 (October 2021): 108369. http://dx.doi.org/10.1016/j.comnet.2021.108369.

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24

Zhong, Hong, Qunfeng Lin, Jie Cui, Runhua Shi, and Lu Liu. "An Efficient SDN Load Balancing Scheme Based on Variance Analysis for Massive Mobile Users." Mobile Information Systems 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/241732.

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In a traditional network, server load balancing is used to satisfy the demand for high data volumes. The technique requires large capital investment while offering poor scalability and flexibility, which difficultly supports highly dynamic workload demands from massive mobile users. To solve these problems, this paper analyses the principle of software-defined networking (SDN) and presents a new probabilistic method of load balancing based on variance analysis. The method can be used to dynamically manage traffic flows for supporting massive mobile users in SDN networks. The paper proposes a solution using the OpenFlow virtual switching technology instead of the traditional hardware switching technology. A SDN controller monitors data traffic of each port by means of variance analysis and provides a probability-based selection algorithm to redirect traffic dynamically with the OpenFlow technology. Compared with the existing load balancing methods which were designed to support traditional networks, this solution has lower cost, higher reliability, and greater scalability which satisfy the needs of mobile users.

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25

Emad Ali, Tariq, Ameer Hussein Morad, and Mohammed A. Abdala. "Load Balance in Data Center SDN Networks." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3084. http://dx.doi.org/10.11591/ijece.v8i5.pp3084-3091.

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<span>In the last two decades, networks had been changed according to the rapid changing in its requirements. The current Data Center Networks have large number of hosts (tens or thousands) with special needs of bandwidth as the cloud network and the multimedia content computing is increased. The conventional Data Center Networks (DCNs) are highlighted by the increased number of users and bandwidth requirements which in turn have many implementation limitations. The current networking devices with its control and forwarding planes coupling result in network architectures are not suitable for dynamic computing and storage needs. Software Defined networking (SDN) is introduced to change this notion of traditional networks by decoupling control and forwarding planes. So, due to the rapid increase in the number of applications, websites, storage space, and some of the network resources are being underutilized due to static routing mechanisms. To overcome these limitations, a Software Defined Network based Openflow Data Center network architecture is used to obtain better performance parameters and implementing traffic load balancing function. The load balancing distributes the traffic requests over the connected servers, to diminish network congestions, and reduce underutilization problem of servers. As a result, SDN is developed to afford more effective configuration, enhanced performance, and more flexibility to deal with huge network designs</span>

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Kaur, Prabhjot, Jasmeen Kaur Chahal, and Abhinav Bhandari. "Load Balancing in Software Defined Networking: A Review." Asian Journal of Computer Science and Technology 7, no. 2 (August 5, 2018): 1–5. http://dx.doi.org/10.51983/ajcst-2018.7.2.1859.

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Software Defined Networking is an adaptable way of networking, which disconnects data forwarding plane and control-plane of system equipment’s and also solves issues in existing network infrastructure. More specifically, the control-plane of software defined network decides the advancing way of network flow with Centralized Control Manner (CCM). SDN (Software Defined Networking) is a strategy for making, planning and overseeing systems which intend to change this present unfortunate circumstance. It has been used in dissimilar areas, like a campus networks and data center systems. In this survey paper, we’ve reviewed the concept of (SDNs) Software Defined Networks, its architecture and applications. In the survey, it has been found that SDN load balancing has become more smart and efficient and reduces the statistic collection overhead and maintain better QoS (Quality of Service) data rates. In addition, we reviewed the direct routing based algorithms of Load Balancer and compare with Round Robin Strategy. Furthermore, we’ve reviewed and compared the existing work to get better idea about the concept of Load balancing.

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Yahya, Widhi, Achmad Basuki, and Jehn Ruey Jiang. "The Extended Dijkstra’s-based Load Balancing for OpenFlow Network." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (April 1, 2015): 289. http://dx.doi.org/10.11591/ijece.v5i2.pp289-296.

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<span lang="EN-US">This paper proposes load-balancing algorithm on the basis of the Extended Dijkstra’s shortest path algorithm for Software Defined Networking (SDN). The Extended Dijkstra’s algorithm considers not only the edge weights, but also the node weights to find the nearest server for a requesting client. The proposed algorithm also considers the link load in order to avoid congestion. We use Pyretic to implement the proposed algorithm and compare it with related ones under the Abilene network topology with the Mininet emulation tool. As shown by the comparisons, the proposed algorithm outperforms the others in term of the network end-to-end latency, throughput and response time at the expense of a little heavier computation load and more memory usage on the SDN controller.</span>

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Chakravarthy, V. Deeban, and B. Amutha. "Path based load balancing for data center networks using SDN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 3279. http://dx.doi.org/10.11591/ijece.v9i4.pp3279-3285.

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Due to the increase in the number of users on the internet and the number of applications that is available in the cloud makes Data Center Networking (DCN) has the backbone for computing. These data centre requires high operational cost and also experience the link failures and congestions often. Hence the solution is to use Software Defined Networking (SDN) based load balancer which improves the efficiency of the network by distributing the traffic across multiple paths to optimize the efficiency of the network. Traditional load balancers are very expensive and inflexible. These SDN load balancers do not require costly hardware and can be programmed, which it makes it easier to implement user-defined algorithms and load balancing strategies. In this paper, we have proposed an efficient load balancing technique by considering different parameters to maintain the load efficiently using Open FlowSwitches connected to ONOS controller.

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Fancy, C., and M. Pushpalatha. "Traffic-aware adaptive server load balancing for software defined networks." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2211. http://dx.doi.org/10.11591/ijece.v11i3.pp2211-2218.

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Servers in data center networks handle heterogenous bulk loads. Load balancing, therefore, plays an important role in optimizing network bandwidth and minimizing response time. A complete knowledge of the current network status is needed to provide a stable load in the network. The process of network status catalog in a traditional network needs additional processing which increases complexity, whereas, in software defined networking, the control plane monitors the overall working of the network continuously. Hence it is decided to propose an efficient load balancing algorithm that adapts SDN. This paper proposes an efficient algorithm TA-ASLB-traffic-aware adaptive server load balancing to balance the flows to the servers in a data center network. It works based on two parameters, residual bandwidth, and server capacity. It detects the elephant flows and forwards them towards the optimal server where it can be processed quickly. It has been tested with the Mininet simulator and gave considerably better results compared to the existing server load balancing algorithms in the floodlight controller. After experimentation and analysis, it is understood that the method provides comparatively better results than the existing load balancing algorithms.

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30

Chen, Junyan, Yong Wang, Jiangtao Ou, Chengyuan Fan, Xiaoye Lu, Cenhuishan Liao, Xuefeng Huang, and Hongmei Zhang. "ALBRL: Automatic Load-Balancing Architecture Based on Reinforcement Learning in Software-Defined Networking." Wireless Communications and Mobile Computing 2022 (May 2, 2022): 1–17. http://dx.doi.org/10.1155/2022/3866143.

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Due to the rapid development of network communication technology and the significant increase in network terminal equipment, the application of new network architecture software-defined networking (SDN) combined with reinforcement learning in network traffic scheduling has become an important focus of research. Because of network traffic transmission variability and complexity, the traditional reinforcement-learning algorithms in SDN face problems such as slow convergence rates and unbalanced loads. The problems seriously affect network performance, resulting in network link congestion and the low efficiency of inter-stream bandwidth allocation. This paper proposes an automatic load-balancing architecture based on reinforcement learning (ALBRL) in SDN. In this architecture, we design a load-balancing optimization model in high-load traffic scenarios and adapt the improved Deep Deterministic Policy Gradient (DDPG) algorithm to find a near-optimal path between network hosts. The proposed ALBRL uses the sampling method of updating the experience pool with the SumTree structure to improve the random extraction strategy of the empirical-playback mechanism in DDPG. It extracts a more meaningful experience for network updating with greater probability, which can effectively improve the convergence rate. The experiment results show that the proposed ALBRL has a faster training speed than existing reinforcement-learning algorithms and significantly improves network throughput.

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31

Xue, Hai, Kyung Kim, and Hee Youn. "Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization." Sensors 19, no. 2 (January 14, 2019): 311. http://dx.doi.org/10.3390/s19020311.

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Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.

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Zhou, Yang, Kangfeng Zheng, Wei Ni, and Ren Ping Liu. "Elastic Switch Migration for Control Plane Load Balancing in SDN." IEEE Access 6 (2018): 3909–19. http://dx.doi.org/10.1109/access.2018.2795576.

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Kang, Byungseok, and Hyunseung Choo. "An SDN-enhanced load-balancing technique in the cloud system." Journal of Supercomputing 74, no. 11 (December 7, 2016): 5706–29. http://dx.doi.org/10.1007/s11227-016-1936-z.

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Adekoya, Oladipupo, Adel Aneiba, and Mohammad Patwary. "An Improved Switch Migration Decision Algorithm for SDN Load Balancing." IEEE Open Journal of the Communications Society 1 (2020): 1602–13. http://dx.doi.org/10.1109/ojcoms.2020.3028971.

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Cheng, Cheng. "Research on Data Center Load Balancing Technology Based on SDN." Journal of Physics: Conference Series 1861, no. 1 (March 1, 2021): 012008. http://dx.doi.org/10.1088/1742-6596/1861/1/012008.

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Yeo, Sangho, Ye Naing, Taeha Kim, and Sangyoon Oh. "Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers." Electronics 10, no. 2 (January 13, 2021): 162. http://dx.doi.org/10.3390/electronics10020162.

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Distributed controllers in software-defined networking (SDN) become a promising approach because of their scalable and reliable deployments in current SDN environments. Since the network traffic varies with time and space, a static mapping between switches and controllers causes uneven load distribution among controllers. Dynamic migration of switches methods can provide a balanced load distribution between SDN controllers. Recently, existing reinforcement learning (RL) methods for dynamic switch migration such as MARVEL are modeling the load balancing of each controller as linear optimization. Even if it is widely used for network flow modeling, this type of linear optimization is not well fitted to the real-world workload of SDN controllers because correlations between resource types are unexpectedly and continuously changed. Consequently, using the linear model for resource utilization makes it difficult to distinguish which resource types are currently overloaded. In addition, this yields a high time cost. In this paper, we propose a reinforcement learning-based switch and controller selection scheme for switch migration, switch-aware reinforcement learning load balancing (SAR-LB). SAR-LB uses the utilization ratio of various resource types in both controllers and switches as the inputs of the neural network. It also considers switches as RL agents to reduce the action space of learning, while it considers all cases of migrations. Our experimental results show that SAR-LB achieved better (close to the even) load distribution among SDN controllers because of the accurate decision-making of switch migration. The proposed scheme achieves better normalized standard deviation among distributed SDN controllers than existing schemes by up to 34%.

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37

Liang, Siyuan, Wenli Jiang, Fangli Zhao, and Feng Zhao. "Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning." Journal of Systems Science and Information 8, no. 6 (December 1, 2020): 578–88. http://dx.doi.org/10.21078/jssi-2020-578-11.

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Abstract With the rapid development of cloud computing and other related services, higher requirements are put forward for network transmission and delay. Due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to be applied and deployed in network control. The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management. A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed. The Bayesian network is used to predict the degree of load congestion, combining reinforcement learning algorithm to make optimal action decision, self-adjusting parameter weight to adjust the controller load congestion, to achieve load balance, improve network security and stability.

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38

Yao, Haipeng, Chao Qiu, Chenglin Zhao, and Lei Shi. "A Multicontroller Load Balancing Approach in Software-Defined Wireless Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/454159.

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Software-defined networking (SDN) is currently seen as one of the most promising future network technologies, which can realize the separation between control and data planes. Furthermore, the increasing complexity in future wireless networks (i.e., 5G, wireless sensor networks) renders the control and coordination of networks a challenging task. Future wireless networks need good separation of control and data planes and call for SDN method to handle the explosive increase of mobile data traffic. Relying on a single controller in future wireless networks imposes a potential scalability problem. To tackle this problem, the thought of using multiple controllers to manage the large wide-area wireless network has been proposed, where the load balance problem of multicontroller needs to be resolved. In this paper, we propose a multicontroller load balancing approach called HybridFlow in software-defined wireless networks, which adopts the method of distribution and centralization and designs a double threshold approach to evenly allocate the load. Simulation results reveal that the proposed approach can significantly relieve the working load on the super controller and reduces the load jitter of multicontroller load in a single cluster compared with the BalanceFlow method.

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39

A. Al-Hasnawi, Sally, and Mahmood K. Ibrahem. "EMULATION OF THE POX CONTROLLER AS A LOAD BALANCER." Iraqi Journal of Information & Communications Technology 4, no. 2 (August 13, 2021): 9–22. http://dx.doi.org/10.31987/ijict.4.2.138.

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Abstract- The servers’ nature of having bottleneck situations makes the load balancing one of the essential services in the networks, particularly with the users’ increasing load. This current paper investigates the technique of load-balancing related to Software-Defined Network (SDN). Due to the nature of the paper, there shall be processes of analysis and simulation to the software-defined network of the two servers. The following parts constitute two servers, four clients, a pox controller, and an open virtual switch. To have the proposed topology connected with the pox controller, there shall be the utilization of the OpenFlow protocol. The results demonstrate that the use of the technique of load balancing assists in equally distributing the requests among the servers.

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40

Kaliuzhnyi, Oleksandr. "Modified load balancing algorithm for partially-overlapping data transmission routes." Scientific review, no. 6(69)2020 (September 7, 2020): 20–31. http://dx.doi.org/10.26886/scientificreview.2311-4517.6(69)2020.2.

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In this paper we propose a modified load balancing algorithm, which can be used to optimize network load and reduce data transmission delay. This algorithm is aimed at load balancing between partially-overlappung data transmission routes. It is based on a modified ECMP algorithm with correction of the load on the network and taking into account the length of possible paths. This algorithm is optimal for centralized network management, and therefore its use in SDN networks is proposed. Testing of this development is carried out for comparison with the existing solution and the advantages of this modification are presented.

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41

Lakhani, Gaurang, and Amit Kothari. "Fault Administration by Load Balancing in Distributed SDN Controller: A Review." Wireless Personal Communications 114, no. 4 (June 10, 2020): 3507–39. http://dx.doi.org/10.1007/s11277-020-07545-2.

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42

Lin, Ying-Dar, Chih Chiang Wang, Yi-Jen Lu, Yuan-Cheng Lai, and Hsi-Chang Yang. "Two-tier dynamic load balancing in SDN-enabled Wi-Fi networks." Wireless Networks 24, no. 8 (April 9, 2017): 2811–23. http://dx.doi.org/10.1007/s11276-017-1504-3.

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Alotaibi, Modhawi, and Amiya Nayak. "Linking handover delay to load balancing in SDN-based heterogeneous networks." Computer Communications 173 (May 2021): 170–82. http://dx.doi.org/10.1016/j.comcom.2021.04.001.

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44

Zhong, Hong, Jinshan Xu, Jie Cui, Xiuwen Sun, Chengjie Gu, and Lu Liu. "Prediction-based dual-weight switch migration scheme for SDN load balancing." Computer Networks 205 (March 2022): 108749. http://dx.doi.org/10.1016/j.comnet.2021.108749.

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Hamed, M. I., B. M. ElHalawany, M. M. Fouda, and A. S. Tag Eldien. "Performance Analysis of Applying Load Balancing Strategies on Different SDN Environments." Benha Journal of Applied Sciences 2, no. 1 (March 1, 2017): 91–97. http://dx.doi.org/10.21608/bjas.2017.163983.

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Malavika, R., and M. L. Valarmathi. "Adaptive Server Load Balancing in SDN Using PID Neural Network Controller." Computer Systems Science and Engineering 42, no. 1 (2022): 229–43. http://dx.doi.org/10.32604/csse.2022.020947.

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Li, Guoyan, Kaixin Li, Yi Liu, and Yuheng Pan. "An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN." Future Internet 11, no. 12 (December 5, 2019): 252. http://dx.doi.org/10.3390/fi11120252.

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Static multi-controller deployment architecture cannot adapt to the drastic changes of network traffic, which will lead to a load imbalance between controllers, resulting in a high packet loss rate, high latency, and other network performance degradation problems. In this paper, an efficient dynamic load balancing scheme based on Nash bargaining is proposed for a distributed software-defined network. Firstly, considering the connectivity of network nodes, the switch migration problem is transformed into a network mapping relationship reconstruction problem. Then, we establish the Nash bargaining game model to fairly optimize the two contradictory goals of migration cost and load balance. Finally, the model is solved by an improved firefly algorithm, and the optimal network mapping state is obtained. The experimental results show that this scheme can optimize the migration cost and load balance at the same time. Compared with the existing research schemes, the migration process of the switch is optimized, and, while effectively balancing the load of the control plane, the migration cost is reduced by 14.5%.

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Lemeshko, Oleksandr, and Oleksandra Yeremenko. "Enhanced method of fast re-routing with load balancing in software-defined networks." Journal of Electrical Engineering 68, no. 6 (November 1, 2017): 444–54. http://dx.doi.org/10.1515/jee-2017-0079.

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AbstractA two-level method of fast re-routing with load balancing in a software-defined network (SDN) is proposed. The novelty of the method consists, firstly, in the introduction of a two-level hierarchy of calculating the routing variables responsible for the formation of the primary and backup paths, and secondly, in ensuring a balanced load of the communication links of the network, which meets the requirements of the traffic engineering concept. The method provides implementation of link, node, path, and bandwidth protection schemes for fast re-routing in SDN. The separation in accordance with the interaction prediction principle along two hierarchical levels of the calculation functions of the primary (lower level) and backup (upper level) routes allowed to abandon the initial sufficiently large and nonlinear optimization problem by transiting to the iterative solution of linear optimization problems of half the dimension. The analysis of the proposed method confirmed its efficiency and effectiveness in terms of obtaining optimal solutions for ensuring balanced load of communication links and implementing the required network element protection schemes for fast re-routing in SDN.

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Cui, Xin, Xiaohong Huang, Yan Ma, and Qingke Meng. "A Load Balancing Routing Mechanism Based on SDWSN in Smart City." Electronics 8, no. 3 (March 1, 2019): 273. http://dx.doi.org/10.3390/electronics8030273.

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In the wireless sensor network infrastructure of smart cities, whether the network traffic is balanced will directly affect the service quality of the network. Because of the traditional WSN (wireless sensor network) architecture, load balancing technology is difficult to meet the requirements of adaptability and high flexibility. This paper proposes a load balancing mechanism based on SDWSN (software defined wireless sensor network). This mechanism utilizes the advantages of a centralized control SDN (software defined network) and flexible traffic scheduling. The OpenFlow protocol is used to monitor the running status and link load information of the network in real time. According to the bandwidth requirement of the data flow, the improved load balanced routing is obtained by an Elman neural network. The simulation results show that the improved SDSNLB (software-defined sensor network load balancing) routing algorithm has better performance than LEACH (Low Energy Adaptive Clustering Hierarchy) protocol in balancing node traffic and improving throughput.

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Mayilsamy, Jayaprakash, and Devi Priya Rangasamy. "Enhanced Routing Schedule - Imbalanced Classification Algorithm for IOT based Software Defined Networks." International Academic Journal of Science and Engineering 8, no. 1 (February 1, 2021): 01–09. http://dx.doi.org/10.9756/iajse/v8i1/iajse0801.

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Route scheduling optimization is important in SDN network. The SDN network needs the best solution for route optimization. Limited networking of software is the most interesting development in this field as it is important to provide a fast and reliable routing path based on its need. The IoT supports software defined applications interface in the overall networks. The SDN is recommended by enhancing the SDN architecture's benefits in improving research network quality. SDN network information exchange is one of the most important factor. It is important to plan the information accordingly and adjust a load of information to the SDN. A Maximum throughput scheduling process is proposed, which is upgraded using the Imbalanced Classification Algorithm. SDN has shown the advantage in many ways compared to the traditional network. But the problem of load inconstancy still occurs in SDN. The imbalanced classification method supports the maximum throughput schedule function and integrates load balancing strategies to improve SDN networks' Performance. Classification is to be proposed based on machine command in QoS. An imbalanced classification learning method is used for improving the QoS requirements and shows that the simulated results of the identified traffic load balance and maximum throughputs in the proposed solutions. Functionality has been improved much better than previous functions in the same area.

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