Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

Abstract

The current and future status of the internet is represented by the upcoming Internet of Things (IoT). The internet can connect the huge amount of data, which contains lot of processing operations and efforts to transfer the pieces of information. The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics, sensors and network connectivity. Nowadays, there are two trending technologies that take the platform i.e., Software Defined Network (SDN) and IoT (SD-IoT). The main aim of the IoT network is to connect and organize different objects with the internet, which is managed with the control panel and data panel in the SD network. The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers. It is more significant for wide area networks, because of the large packet propagation latency and the controller placement problem is more important in every network. In the proposed work, IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization (ESFO) algorithm and Pareto Optimal Controller placement tool (POCO) for the placement problem of the controller. In order to prove the efficiency of the proposed system, it is compared with other existing methods like PASIN, hybrid SD and PSO in terms of load balance, reduced number of controllers and average latency and delay. With 2 controllers, the proposed method obtains 400 miles as average latency, which is 22.2% smaller than PSO, 76.9% lesser than hybrid SD and 91.89% lesser than PASIN.

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

“…[1] 6.62 ms 78.8 ± 0.1 ms [6] 0.000017 to 0.0000275 ms [10] 23 to 60 µs [13] 0.2 to 0.8 ms [15] 2.8×10 4 ms [23] 10 to 0.4 ms [24] 0.049 to 0.024 ms [26] 0.05 to 5.85 s…”

“…It has been found that the performance results of the POX controller are good, better and more stable than other controllers. Also, in [15][16][17], they have configured and shaped SDN and the Internet of Things (SD-IoT) for the connection and organization of different objects to internet. In addition, the used three fuzzy controller's placement controllers are used to find the average delay and used Pareto Optimal Controller placement (POCO) and the enhanced sunflower optimization (ESFO) algorithm for solving (CPP).…”

Software defined wireless sensor load balancing routing for internet of things applications: Review of approaches

“…To address this concern, the study in Ref. [ 101 ] introduced adaptive CPP based on sunflower optimization. The proposed solution outperforms other natures inspired algorithms with better latency.…”

“… The algorithm assumes that the network is static and the nodes are homogeneous. [ 97 ] Energy consumption Markov Model (MM) and the Artificial Bee Colony (ABC) Wireless sensor in IoT Improved number of alive nodes and number of delivered Packets Overlooked balancing the load on SDN resources and update operation [ 98 ] Control Server energy consumption and placement Harris Hawks Optimization SDWSN Enhances the lifetime of the SDWSN with better load balancing The solution may lead to extra processing overhead in the system [ 99 ] Optimized routing based on network resource Genetic Mutation Based PSO (GMPSO) IoT enabled SDWSN Improve Control node selection and Path Optimization It may lead to higher controller processing overhead especially in large network [ 100 ] Controller load balance Spider Monkey Optimization Algorithm SDN and IoT improved throughput and average response time The swapping process of moving switches from utilize to under may introduce additional overhead [ 101 ] Controller load balance due to an increase in the delay Enhanced sunflower optimization (ESFO) algorithm SDIoT Improved latency between controllers It may exhibit slower convergence speed in large-scale network …”