On Load Balancing via Switch Migration in Software-Defined Networking

Abstract

Switch-controller assignment is an essential task in multi-controller software-defined networking. Static assignments are not practical because network dynamics are complex and difficult to predetermine. Since network load varies both in space and time, the mapping of switches to controllers should be adaptive to sudden changes in the network. To that end, switch migration plays an important role in maintaining dynamic switch-controller mapping. Migrating switches from overloaded to underloaded controllers brings flexibility and adaptability to the network but, at the same time, deciding which switches should be migrated to which controllers, while maintaining a balanced load in the network, is a challenging task. This work presents a heuristic approach with solution shaking to solve the switch migration problem. Shift and swap moves are incorporated within a search scheme. Every move is evaluated by how much benefit it will give to both the immigration and outmigration controllers. The experimental results show that the proposed approach is able to outweigh the state-of-art approaches, and improve the load balancing results up to ≈ 14% in some scenarios when compared to the most recent approach. In addition, the results show that the proposed work is more robust to controller failure than the state-of-art methods.

Heuristic Resource Allocation Algorithm for Controller Placement in Multi-Control 5G Based on SDN/NFV Architecture

“…However, in the selected path, the capacity of usable bandwidth is shared by multiple switches and is assigned by the controller. Overloading the controller leads to increase response delay of network events and failure of the controller [51]. However, the total delay from the source to the destination depends on the path diversity.…”

“…In the simulation, we considered the random traffic and location scenarios for different networks consisting of several SDN nodes randomly deployed in a 2000 km×2000 km square area as in [51]. The location of the nodes followed a uniform random distribution in the simulation region.…”

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

“…Control plane can be implemented using a central controller managing the local controllers, distributed controllers that work and take action on their own, and fragmentation-based distributed controllers. Switch migration is one of the solutions in the multi-controller environment for load balancing [16][17]. In a distributed environment, switch migration is not adequate and it leads to time complexity.…”

Learn to Schedule (LEASCH): A Deep Reinforcement Learning Approach for Radio Resource Scheduling in the 5G MAC Layer

“…The main key-enablers to handle the requirements of this new era include flexible numerology, bandwidth parts (BWPs), service multiplexing and mini-slotting, optimized frame structure, massive MIMO, inter-networking between high and low bands, and ultra lean transmission [1], [6], [7]. In addition, emergent technologies like software-defined networking (SDN) [5], network function virtualization (NFV), and network slicing [4] will also be key technologies in paving the way for enhancements in 5G and beyond.…”