Prediction-based dual-weight switch migration scheme for SDN load balancing

2022 ◽  
pp. 108749
Author(s):  
Hong Zhong ◽  
Jinshan Xu ◽  
Jie Cui ◽  
Xiuwen Sun ◽  
Chengjie Gu ◽  
...  
Keyword(s):  
Load Balancing ◽  
Switch Migration

CAMD: a switch migration based load balancing framework for software defined networks

IET Networks ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 264-271 ◽  
Author(s):  
Kshira Sagar Sahoo ◽  
Bibhudatta Sahoo
Keyword(s):  
Load Balancing ◽  
Software Defined Networks ◽  
Switch Migration

scholarly journals An Improved Switch Migration Decision Algorithm for SDN Load Balancing

2020 ◽  
Vol 1 ◽  
pp. 1602-1613
Author(s):  
Oladipupo Adekoya ◽  
Adel Aneiba ◽  
Mohammad Patwary
Keyword(s):  
Load Balancing ◽  
Migration Decision ◽  
Decision Algorithm ◽  
Switch Migration

A distributed decision mechanism for controller load balancing based on switch migration in SDN

2018 ◽  
Vol 15 (10) ◽  
pp. 129-142 ◽  
Author(s):  
Tao Hu ◽  
Peng Yi ◽  
Jianhui Zhang ◽  
Julong Lan
Keyword(s):  
Load Balancing ◽  
Decision Mechanism ◽  
Switch Migration ◽  
Distributed Decision

ESMLB: Efficient Switch Migration-Based Load Balancing for Multicontroller SDN in IoT

2020 ◽  
Vol 7 (7) ◽  
pp. 5852-5860 ◽  
Author(s):  
Kshira Sagar Sahoo ◽  
Deepak Puthal ◽  
Mayank Tiwary ◽  
Muhammad Usman ◽  
Bibhudatta Sahoo ◽  
...  
Keyword(s):  
Load Balancing ◽  
Switch Migration

scholarly journals Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 162
Author(s):  
Sangho Yeo ◽  
Ye Naing ◽  
Taeha Kim ◽  
Sangyoon Oh
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Network Flow ◽  
Load Distribution ◽  
Linear Optimization ◽  
Flow Modeling ◽  
Selection Scheme ◽  
The Neural Network ◽  
Distributed Controllers ◽  
Switch Migration

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%.

scholarly journals On Load Balancing via Switch Migration in Software-Defined Networking

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 95998-96010 ◽  
Author(s):  
F. Al-Tam ◽  
N. Correia
Keyword(s):  
Load Balancing ◽  
Software Defined Networking ◽  
Switch Migration
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