Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking
The ever-increasing heterogeneous connections and the demands of the users pose many new challenges to the network service providers to sustain by providing improved quality of service (QoS). Software-defined networking (SDN) is a game changer in networking by allowing user customization to enhance...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10250788/ |
| _version_ | 1797676697976307712 |
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| author | Mohammad Riyaz Belgaum Shahrulniza Musa Fuead Ali Muhammad Mansoor Alam Zainab Alansari Safeeullah Soomro Mazliham Mohd Su'ud |
| author_facet | Mohammad Riyaz Belgaum Shahrulniza Musa Fuead Ali Muhammad Mansoor Alam Zainab Alansari Safeeullah Soomro Mazliham Mohd Su'ud |
| author_sort | Mohammad Riyaz Belgaum |
| collection | DOAJ |
| description | The ever-increasing heterogeneous connections and the demands of the users pose many new challenges to the network service providers to sustain by providing improved quality of service (QoS). Software-defined networking (SDN) is a game changer in networking by allowing user customization to enhance performance. With the advent of 5G and the increasing user requests, a massive volume of heterogeneous traffic is generated in the network, increasing load. Currently, the existing load balancing techniques lack efficiency in handling the load under unicontroller deployment. In addition, the network paths selected must also be reliable and optimal. We proposed the self-socio adaptive, reliable particle swarm optimization (SSAR-PSO) load balancing technique to address the issue of load balancing in the unicontroller deployment of SDN. In the proposed technique, the performance of the node itself, known as direct information, and the performance of the neighbouring nodes, known as indirect information, were considered to identify the reliable node to form an optimal path. Simulation results showed that the proposed technique outperforms the existing state-of-the-art techniques under TCP and UDP load in the following network performance metrics: latency, packet loss ratio, throughput, average round trip time, and bandwidth utilization ratio. |
| first_indexed | 2024-03-11T22:33:57Z |
| format | Article |
| id | doaj.art-9855a0116ba84a55bb09d07345f03aca |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T22:33:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9855a0116ba84a55bb09d07345f03aca2023-09-22T23:01:26ZengIEEEIEEE Access2169-35362023-01-011110166610167710.1109/ACCESS.2023.331479110250788Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined NetworkingMohammad Riyaz Belgaum0https://orcid.org/0000-0002-6155-1530Shahrulniza Musa1https://orcid.org/0000-0003-4867-5085Fuead Ali2https://orcid.org/0000-0002-7271-5035Muhammad Mansoor Alam3https://orcid.org/0000-0001-5773-7140Zainab Alansari4https://orcid.org/0000-0003-3443-4002Safeeullah Soomro5https://orcid.org/0000-0001-5571-1262Mazliham Mohd Su'ud6https://orcid.org/0000-0001-9975-4483Faculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaMalaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, MalaysiaMalaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaUniversity of Technology and Applied Sciences, Muscat, OmanAmerican National University, Louisville, KY, USAFaculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaThe ever-increasing heterogeneous connections and the demands of the users pose many new challenges to the network service providers to sustain by providing improved quality of service (QoS). Software-defined networking (SDN) is a game changer in networking by allowing user customization to enhance performance. With the advent of 5G and the increasing user requests, a massive volume of heterogeneous traffic is generated in the network, increasing load. Currently, the existing load balancing techniques lack efficiency in handling the load under unicontroller deployment. In addition, the network paths selected must also be reliable and optimal. We proposed the self-socio adaptive, reliable particle swarm optimization (SSAR-PSO) load balancing technique to address the issue of load balancing in the unicontroller deployment of SDN. In the proposed technique, the performance of the node itself, known as direct information, and the performance of the neighbouring nodes, known as indirect information, were considered to identify the reliable node to form an optimal path. Simulation results showed that the proposed technique outperforms the existing state-of-the-art techniques under TCP and UDP load in the following network performance metrics: latency, packet loss ratio, throughput, average round trip time, and bandwidth utilization ratio.https://ieeexplore.ieee.org/document/10250788/Load balancingparticle swarm optimizationquality of servicereliabilitysoftware-defined networking |
| spellingShingle | Mohammad Riyaz Belgaum Shahrulniza Musa Fuead Ali Muhammad Mansoor Alam Zainab Alansari Safeeullah Soomro Mazliham Mohd Su'ud Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking IEEE Access Load balancing particle swarm optimization quality of service reliability software-defined networking |
| title | Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking |
| title_full | Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking |
| title_fullStr | Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking |
| title_full_unstemmed | Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking |
| title_short | Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking |
| title_sort | self socio adaptive reliable particle swarm optimization load balancing in software defined networking |
| topic | Load balancing particle swarm optimization quality of service reliability software-defined networking |
| url | https://ieeexplore.ieee.org/document/10250788/ |
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