Increase in demand for multimedia and quality services requires 5G networks to resolve issues such as slicing, allocation, forwarding, and control using techniques such as software-defined networking (SDN) and network function virtualization. In this study, the optimum number of SDN multi-controllers are implemented based on a multi-criterion advanced genetic algorithm that takes into consideration three key parameters: Switch controller latency, hopcount, and link utilization. Preprocessing is the first step, in which delay, delay paths, hopcount, and hoppaths are computed as an information matrix (Infomat). Randomization is the second step, and consists of initially placing controllers randomly, followed by an analytical hierarchical process evaluation that considers switch controller latency as the primary objective in the assignment process. In the third and last step, crossover and mutation genetic functions are implemented for a local search process until the best placement is found (when the primary objective threshold is reached). A novel chaos theory is applied to the salp swarm algorithm (SSA) for enhancing the optimizer’s performance in assigning the switch controllers. The SSA is a dynamic optimal algorithm for switch controller connections. To enhance convergence rate and precision, chaotic maps extract random parameters based on Gaussian distribution for better control in reducing local optima. A logistic chaotic map is selected after analyzing its performance based on unimodal and multimodal optimization. Evaluations are carried out on this proposed algorithm for 12 sets of topologies with two categories: Fewer and more switches. Simulation results show the highest efficiency in a computation time of 25 ms with varying thresholds and 12 ms with varying upper boundary utilization when compared with the particle swarm optimization (PSO) meta-heuristic algorithm. The effects of varying latency thresholds and upper boundary utilization on two set of topologies inferred more requests serviced with fewer controllers deployed.

Software-defined networking (SDN) provides cost-effective dynamic communications between the control plane and the data plane. The data plane is responsible for all traffic forwarding operations while the control plane handles all traffic decisions in the network. This separation provides flexibility and control over changing network conditions.

As shown in

In this study, the two proposed dynamic algorithms are

Advanced genetic algorithm.

Novel chaos theory on meta-heuristic algorithm.

The proposed advanced genetic algorithm is multi-criterion algorithm based on an analytic hierarchy process for optimum selection and placement of SDN controllers, in order to increase flexibility of network management decision-making. The novel chaotic salp swarm algorithm (NCSSA) is proposed for switch controller allocation in large-scale distributed SDN controllers. This algorithm applies novel chaotic maps to the salp swarm optimization algorithm (SSA) enhancing its performance in latency reduction between switches and controllers. To find the fitness function for the SSA, modeling of distributed multi-controllers is employed with C controllers (i = 1, 2, 3 … C) and S switches (j = 1, 2, 3 … S). Controllers receive service requests from switches and respond according to switching-forwarding rules. Each controller has a maximum limit of service up to N switches, as its resource usage is limited [

The average response time (RT_{i}) of controller (i = 1,2,3…. C) is calculated based on arrival rate (λ_{i}) and service rate (µ_{i})

where P_{s} is usage probability of system servers. The controller arrival rate (λ_{i}) is defined as summation of the arrival rate of all switches connected with their respective controllers.

The average controller load (Load_{i}) can be calculated based on the number of requests received and serviced.

where, u represents server utilization and s represents switches set.

The rest of study is organized as follows. Section 2 consists of a previous literature review. Section 3 describes the different criteria considered for analysis. Section 4 discusses the proposed controller placement algorithm. Sections 5 and 6 describe how chaotic maps are applied to the SSA for optimum switch controller allocation. Performance evaluation results are presented in Section 8. In Section 9, the authors offer their conclusions.

In this section, the important literature on software-defined mobile network allocation is listed. A chaotic gray wolf optimizer controller framework is designed and evaluations are conducted on hierarchical switching topologies examining different quality of service (QoS) [

The analytic hierarchy process is implemented in the advanced genetic algorithm for finding the best position of the controller.

The SDN multiple controller architecture is understood. How the data plane and control plane can be integrated in a single domain and the security challenges in a 5G heterogeneous system are discussed.

How controllers are deployed and assigned to switches based on propagation delay. This propagation delay is a metric considered for the proposed algorithm. Also considered are how to minimize latency due to propagation and processing, energy consumption, cost elements such as CAPEX and OPEX, and maximizing resilience and reliability.

Optimization of controllers based on a non-zero sum algorithm and integer linear programming is explained.

The design of the framework for optimizing the placement of a fixed number of controllers using the particle swarm optimization (PSO) algorithm.

Unlike previous articles, this article considers three different metrics for the optimum deployment of controllers and the optimal establishment of switch controller connections, based on the NCSSA.

In this section, different criterion to be analyzed for controller placement and switch allocation to controller are discussed in detail [

Switch controller latency is most important parameter for controller placement. Increase in delay for switch servicing by controller will affect performance to the network activity.

In

Hop count is defined as no of nodes present as intermediate between source and destination. For every hop it incurs latency which is important criterion to be considered for allocating switches to controllers. It is not possible to allocate only based on hopcount, since other parameters like delay, utilization should also be considered. For example node 6 can be allocated to either C 1 or C 2 since both are having same hop count. On considering latency through nodes 10 and 11 the efficiency decreases when connected to C 2 rather when connected to C 1.

Link utilization criterion is defined as no of users currently using particular link indicates bandwidth utilization by traffic flow. Let us consider node 6 is connected to C 1 through the path 3 → 2 having transversal value (T) = 2 on particular link. In other case, if node 6 is connected to C 2 through the path 10 → 11 having T = 5 on particular link. Having more utilization bandwidth will definitely affect efficiency of the system and considering only one of above discussed parameters will not produce good result. Hence correct decision should be taken by administrators based on different preferences.

In order to find vicinity of controllers in network, advanced genetic algorithm is proposed to solve controller placement problem based on multicriterion analysis. Advanced genetic algorithm decides controller location and number of controllers based on network requirements [

where delay (s,c_{s}) denotes propagation delay between switch s and controller c_{s} on placement p.

Advanced genetic algorithm is explained below in detail,

Inputs are I = (S,E), C, Iteration max (IT max), permutation counter (PC), delay matrix (DM), hopcount matrix(HCM), predefined value for p is initialized based on solutions till now and will be available as output [

1. | Input sI = (S,E), C, IT max, PC, DM, HCM |

2. | n → |s| |

3. | Calculate delay between nodes I and j |

4. | Hopcount adjacency matrix, link utilization matrix |

5. | p → controller is placed randomly |

6. | use randomization process for creating initial population of placements based on multicriterion analysis |

7. | ∈ → consider switch controller latency as main objective |

8. | Best P → P, IT_{max} → 1, PC → 0 |

9. | While IT ≤ IT _{max} do |

10. | Random placement generation |

11. | Evaluations continues base on multicriterion and assignment of all switches to this set of controllers |

12. | P’ → crossover (P,RP) |

13. | P” → mutation (P’) |

14. | P”’ → localsearch (P”) |

15. | If function (P”’) ≤ function (P) then |

16. | P’” → P |

17. | If |

18. | P → Best P |

19. | Endif |

20. | Else |

21. | PC → PC + 1 |

22. | Endif |

23. | If PC = PC _{max} then |

24. | P → mutation (Best P) |

25. | PC → 0 |

26. | Endif |

27. | IT → IT + 1 |

28. | Evaluation continues based on multicriterion and assignment of all switches to this set of controllers |

29. | End while |

30. | Outcome:- Best Controller Placement |

The SSA is a meta-heuristic algorithm that mathematically models the salp chain. The front salp of the chain is the leader and the rest of the salps behind the leader are followers. The leader leads the swarm for finding the food source, followed by the followers. Each salp position is an s-dimensional search space, where s is the number of variables. The salp position is stored in a 2D matrix called m, with food source F in the search space as the salp’s destination [

The update of the leader salp occurs based on the following equation.

where ^{th} dimension, and F_{k} is the food source position in k^{th} dimension. u_{k},l_{k} are the upper and lower boundaries of k-th dimension. c_{1}, c_{2}, c_{3} are random numbers used as model coefficients. The coefficient c_{1} is given by

i = current iteration, I = maximum no of iteration. c_{2} and c_{3} are random numbers uniformly distributed in interval [0,1].

Follower’s position is updated based on newton’s law of motion which indicates of position of i_{th} follower salp in k_{th} dimension (

Meta-heuristic algorithms [_{1} and c_{2} coefficients. c_{3} represents movement toward +ve or –ve infinity. c_{2} can be adjusted with chaotic map value at current iteration as follows [

Replace

Logistic chaotic map optimizer is used for updating coefficient c_{2}

In this algorithm, the salp at the food source is the required optimized solution. The optimum number of controllers is assigned based on the multiple criteria advanced genetic algorithm. Now the optimum allocation of switches to controllers, based on NCSSA, is initialized based on the following algorithm [

1. | Initialize u_{k}, l_{k}, I, s, n |

2. | Initialize salps position X_{k}; k = 1, 2, 3… n |

3. | While (i ≤ I) |

4. | Calculate salp position fitness function using FF = ∈_{A}FF_{i}/|a| |

5. | Calculate best salp position |

6. | c1 value is updated using chaotic map value x(t) is determined |

7. | For (k = 1; k ≤ n) ;do |

8. | if (k == 1) |

9. | leading salp position updated using |

10. | else |

11. | follower salp position updated using |

12. | endif |

13. | endfor |

14. | salps position adjusted based on u_{k} and l_{k} |

15. | i + 1 → i |

16. | Outcome:- Updated best salp position |

The performance of the NCSSA was evaluated through a simulation environment running on MATLAB. We selected 12 topologies from the Internet Topology Zoo and divided them into two categories, one with fewer switches and one with more switches.

Topology name | Geolocation | No. of switches |
---|---|---|

ARPHNET 1972-08 | United states | 4 |

MREN | Montenegro | 6 |

Getnet | USA | 7 |

Sprint | USA | 11 |

NSFCNET | China | 13 |

Topology name | Geolocation | No. of switches |
---|---|---|

Garanet | Europe | 15 |

IBM | USA | 18 |

Oxford | USA | 20 |

FUNET | Portugal | 23 |

ERNET | India | 24 |

ANS | USA | 25 |

AGIS | USA | 25 |

Switch request rate (λ) = [1500,2500] |

Controller service rate (µ) = 20,000 req/sec |

Latency Threshold (τ) = around 2 ms |

Upperbound utilization (u_{k}) = 0.9 |

Maximum iteration = 50 |

Time weighting factor = 20 |

The proposed algorithm was implemented for maximum iterations for each selected topology. Two different scenarios were considered based on the different values of latency threshold and upper boundary utilization.

Cases | Scenario-1: U_{k} = 0.9 [constant] (ms) |
Scenario-2: τ = 2 millisecond [constant] |
---|---|---|

1 | τ_{1} = 2 |
U_{k} = 0.80 |

2 | τ_{1} = 3 |
U_{k} = 0.85 |

3 | τ_{1} = 4 |
U_{k} = 0.90 |

4 | τ_{1} = 5 |
U_{k} = 0.95 |

_{k} = 0.9 indicates 90% utilization) allows the controller to service more requests, so that fewer controllers need to be deployed.

In this article, two algorithms are considered in a simulation environment: A multi-criterion advanced genetic algorithm for solving the problem of controller placement, based on criteria such as switch controller latency, hopcount, and link utilization, and a novel meta-heuristic chaotic salp swarm algorithm for switch controller allocation. The performance of the proposed algorithms are evaluated for 12 topologies, based on different metrics. The results confirmed that the algorithms optimized controller allocation and had faster computation times when compared with other algorithms.