A Survey of Resource Allocation and Controller Placement Problem in SDN-SDWSN

Kgogo, Tebogo; Isong, Bassey; Lugayizi, Francis; Abu‐Mahfouz, Adnan M.

doi:10.1109/imitec52926.2021.9714659

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…The last row of Table 1 indicates the factors considered for energy saving for the solution proposed in this work. Note that several studies have addressed the CP problem [31][32][33][34][35][36][37]. However, only some of them consider energy savings as a location criterion [28,38,39].…”

Section: Related Workmentioning

confidence: 99%

Energy-and-Blocking-Aware Routing and Device Assignment in Software-Defined Networking—A MILP and Genetic Algorithm Approach

Riveros-Rojas,

Cespedes-Sanchez,

Pinto-Roa

et al. 2024

MCA

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Internet energy consumption has increased rapidly, and energy conservation has become a significant issue that requires focused research efforts. The most promising solution is to identify the minimum power subsets within the network and shut down unnecessary network devices and links to satisfy traffic loads. Due to their distributed network control, implementing a centralized and coordinated strategy in traditional networks is challenging. Software-Defined Networking (SDN) is an emerging technology with dynamic, manageable, cost-effective, and adaptable solutions. SDN decouples network control and forwarding functions, allowing network control to be directly programmable, centralizing control with a global network view to manage power states. Nevertheless, it is crucial to develop efficient algorithms that leverage the centralized control of SDN to achieve maximum energy savings and consider peak traffic times. Traffic demand usually cannot be satisfied, even when all network devices are active. This work jointly addresses the routing of traffic flows and the assignment of SDN devices to these flows, called the Routing and Device Assignment (RDA) problem. It simultaneously seeks to minimize the network’s energy consumption and blocked traffic flows. For this approach, we develop an exact solution based on Mixed-Integer Linear Programming (MILP) as well as a metaheuristic based on a Genetic Algorithm (GA) that seeks to optimize both criteria by routing flows efficiently and suspending devices not used by the flows. Conducted simulations on traffic environment scenarios show up to 34% savings in overall energy consumption for the MILP and 33% savings achieved by the GA. These values are better than those obtained using competitive state-of-the-art strategies.

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Section: Related Workmentioning

confidence: 99%

Energy-and-Blocking-Aware Routing and Device Assignment in Software-Defined Networking—A MILP and Genetic Algorithm Approach

Riveros-Rojas,

Cespedes-Sanchez,

Pinto-Roa

et al. 2024

MCA

View full text Add to dashboard Cite

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“…This can be a difficult decision because the number of controllers is growing, and it may be difficult to identify the right metrics. Despite these benefits, having several controllers introduces a number of challenges, one of which is the optimum controller placement problem (CPP) [6]. For example, adding more controllers or distributing them randomly around the network would not provide scalability because acceptable performance will not be achieved.…”

Section: Introductionmentioning

confidence: 99%

ISFO-CS: An Improved Sailfish Optimization Algorithm for Controller Selection in SDWSN

Rajoriya,

Gupta

2024

Preprint

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Software-defined wireless sensor networks (SDWSNs) have recently been added to networking, increasing scalability and performance. Choosing SDN controllers is a critical issue for network administrators in SDNs. The control plane in SDN is a separate procedure that operates on the control layer. In terms of applications and services, the controller provides a comprehensive view of the entire network. The three key factors examined when selecting a controller are open source, campus network, and productivity. An appropriate device for the prompt processing of all switch requests is required for SDN to function properly and the network to behave properly. To determine the optimum controller for the given parameters, decision logic that allows controller comparison must be developed. As a result, an improved Sailfish Optimization (ISFO) method is proposed in this study to ideally take the best controller node (CN) from a group of sensor nodes. The ISFO selects the best CN by considering a multi-objective fitness function incorporating distance, residual energy, node coverage, and sensor node communication cost. After selecting CN, the Fuzzy C-means (FCM) Clustering algorithm executes the subsequent data transmission process. The Matlab program is used to execute the simulation, and the performance of the proposed methodology is assessed using several performance criteria. The proposed model accomplishes a 0.95% packet delivery ratio for 500 rounds and takes 1.8s for 500 rounds of node to controller latency.

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