Controller placement problem in the optimization of 5G based SDN and NFV architecture

The fast rise in data traffic and the vast range of services and applications accessible in 5G networks must be addressed effectively. Integrating Software Defined Networking (SDN) with Network Function Virtualization (NFV) is a low-cost way to build a reconfigurable network, reduce operating costs, and optimize network performance. The separation of control functionality from forwarding devices brings orchestration and management to enable 5G network programmability. Although centralized control facilitated orchestration and administration of 5G services and applications, it could not handle massive and varied data volumes. 5G networks can avoid performance degradation, enable diverse network traffic management, and create a flexible and scalable design by adopting and deploying multi-controllers in the network control layer. However, for optimum 5G core design and cost-effectiveness, a group of controllers must be appropriately mapped. A distributed 5G-SDN-NFV-based network architecture uses the controller placement problem (CPP) to manage controller placement and number. A heuristic called dynamic mapping and multi-stage CPP algorithm (DMMCPP) was developed to solve CPP as resource allocation in a distributed 5G-SDN-NFV-based network. This thesis divides CPP solutions into three groups based on three objectives: (i) scalability and load balancing, (ii) reliability and resilience, and (iii) efficient routing for energy-aware design. First, a dynamic allocation and mapping CPP (DAMCP) is developed to solve network dynamic resource location problems. It demonstrates a trade-off between locating a minimum number of controllers and network traffic to maximize resources and achieve load balancing at minimum costs. Second, the increasing demand for controllers exposes the network to control planes and connection failures, which are the most frequent problems in SDN networks. If the control plane fails to improve system resilience quality, A reliable RAMCP is formulated as an optimal solution for fault tolerance. Furthermore, the approach is extended with a Particle Swarm Algorithm (PSO) and presented as a hybrid RASCP to validate the optimal location and number of controllers. Third, the considered traffic paths across backup nodes and redundancy lengthen, increasing latency and power consumption in a network. The proposed energyaware routing algorithm (EARMCP) implements efficient flow routing mechanisms for network traffic to minimize the number of active links and 5G-DC devices. Extensive computations utilizing MATLAB 2018a on the Intel Core i7/Gen 10 processor and 16 GB of RAM are used to evaluate the algorithm efficacy. According to the blueprint of our heuristic method, the allocation and the optimum number of controllers under an effective decentralized policy could achieve higher efficiency. The selected control number is picked with a higher efficiency before the rescheduling is approximately 80 % for optimized controllers up to 90 % of resource management than other comparable algorithms in such a densified network. In addition, energy savings of up to 70% are achieved compared to the proposed Dijkstra-based energy-aware algorithms.

Text Abeer Ibrahim B5 - IR.pdf Download (1MB)

Actions (login required)

View Item