A Comparative Study On Load Balancing Techniques In Software Defined Networks

Sayed, Moinul Islam; Saha, S. K.; Sayem, Ibrahim Mohammed; Majumder, Sarna

doi:10.35444/ijana.2022.13401

Cited by 4 publications

(1 citation statement)

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“…The selection of optimal controllers and their placement are considered controller placement problems (CPPs) and are one of the important problems in the SDWSN system 12 . The CPP undergoes various constraints like network overloading and high latency between the sensor nodes 13 . In CPP, the controllers are implemented in a single hardware or the OpenFlow switches.…”

Section: Introductionmentioning

confidence: 99%

SO‐CPP: Sailfish optimization‐based controller placement in IoT‐enabled software‐defined wireless sensor networks

Kumari Rajoriya,

Gupta

2024

Int J Communication

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SummaryOne of the expanding network topologies that is frequently utilized to improve network development by successfully separating the control plane and data plane is software‐defined networking (SDN). In order to function inside complex sensor networks, the SDWSN system frequently relies on centralized controller logic that pulls global network information. In wireless sensor networks (WSNs), using several SDN controllers is known as a promising strategy due to reliability and performance considerations. However, using numerous controllers increases the synchronization overhead between the controllers. Consequently, it is a difficult research challenge to discover the best placement of SDN controllers to enhance the performance of a WSN, subject to the maximum number of controllers calculated based on the synchronization overhead. This research introduces a novel technique to overcome the controller placement problem (CPP) by optimizing multi‐constraints within the sensor networks. For selecting the optimal controllers and placing them in an optimal location, a novel sailfish optimization (SO) strategy is introduced that can enhance the search space and maintain optimal global values throughout the iteration. Then, node clustering is performed using the fuzzy‐C‐means (FCM) clustering technique, which can reduce energy consumption and path delay within the network. The overall latency obtained by the proposed method is about 0.51 and 0.56 ms, and a total run time of 4 ms for both single sink and multi‐sink, respectively. The proposed method is implemented in the MATLAB platform, and different performance metrics are analyzed and compared with existing techniques.

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Section: Introductionmentioning

confidence: 99%