A Hybrid Intelligent Approach for Optimising

A Hybrid Intelligent Approach for Optimising Software-Defined Networks Performance Ann Sabeeh Yousif Al-Dunainawi Electronic and Computer Engineering Dept. Electronic and Computer Engineering Dept. College of Engineering, Design and Physical Sciences College of Engineering, Design and Physical Sciences Brunel University London Brunel University London Email: [email protected] Email: [email protected]

H. S. Al-Raweshidy Maysam F. Abbod Wireless Network and Communication Centre Electronic and Computer Engineering Dept. (WNCC) College of Engineering, Design and Physical Sciences College of Engineering, Design and Physical Sciences Brunel University London Brunel University London Email: [email protected] Email: [email protected]

Abstract- A new hybrid intelligent approach 1 Introduction for optimising the performance of Software- In the recent years, hybridisation or combination Defined Networks (SDN), based on heuristic of different machine learning and adaptation optimisation methods integrated with Artificial Neural Network (ANN) paradigm is techniques has been employed for a large number presented. Evolutionary Optimisation of new intelligent system designs. The main aim techniques, such as Particle Swarm of integrating these techniques is to overcome Optimisation (PSO) and Genetic Algorithms individual limitations and to achieve synergetic (GA) are employed to find the best set of effects [1]. These techniques including, Artificial inputs that give the maximum performance of Neural Networks (ANNs), the Adaptive Network an SDN. The Neural Network model is trained Fuzzy Inferences System (ANFIS) are used for and applied as an approximator of SDN mapping and modelling purposes. Whilst behaviour. An analytical investigation has evolutionary based optimisation approaches, been conducted to distinguish the optimal such as the Genetic Algorithm (GA) and Particle optimisation approach based on SDN Swarm Optimisation (PSO) have been applied performance as an objective function as well widely to produce powerful and optimised as the computational time. After getting the general model of the Neural Network through intelligent systems [2]. testing it with unseen data, this model has Recently, with the fast development of the been implemented with PSO and GA to find Internet, network topology, and the number the best performance of SDN. The PSO approach combined with SDN, represented by applications have changing gradually, which has ANN, is identified as a comparatively better led to more complex structures and functions. A configuration regarding its performance index network based on the traditional TCP/IP as well as its computational efficiency. architecture faces many challenges, especially the router, as the network core, takes on too many efforts to deal with. As a result, the validity Keywords–ANN; Evolutionary Optimisation; and efficiency of data forwarding is being SDN threatened. Hence, we need to find a new kind of network architecture to solve the existing problems. To this end, the study of future networks is proceeding all over the world, and 2 Software-Defined Networks the Software Defined Network (SDN) is one of It was previously noted that an SDN, which is the [3]. SDN is an architecture enabling rapid centrally organised around the principle of the innovation, while hiding much of the complexity compartmentalisation of the control plane and of the networking design. As such, it is a data plane, has emerged as a desirable computer promising solution for network virtualisation that networking device. As the SDN enables the decouples control from the forwarding or data control-plane to be personalised with regard to its plane [4]. In doing so, it can provide the programming so as to manipulate the remote capability of remote and centralised control of system gear, those supplying the administration the network forwarding-plane through the can engage in flexible system operation. The network control-plane. genuine specification of SDN integrates the SDN SDN, as a network platform, has been studied controller in the control plane and, following widely in the literature and many researchers this, it also integrates the SDN switch in the data have proposed soft computing methods to model plane. Figure 1 provides an indication of the way and optimise the network. Yilan and et al. [5] in which the interface particular is organised with provided a genetic algorithm for solving the regard to the two. As demonstrated by [9], the bandwidth-constrained multi-path optimisation SDN controller serves to maintain the stream problem in the SDN. Gao and et al. [6] tables so as to engage in the management of contributed a PSO algorithm to solve the control every stream that is conveyed through the placement problem that takes into consideration system. both the latency between controllers and their The OpenFlow procedure can be regarded as an capacities. Zhang and Fumin [7] explored open SDN specification that is constituted of a applications of the SDN technique in the pair of foundational elements: first, the direction of the automation of network OpenFlow switch and, second, the controller. management, the unified control of optical The former facilitates the execution of the data transmission and IP bearing, smooth switching in plane while the latter realises the control plane. It a wireless network as well as network should be noted that the OpenFlow procedure as virtualisation and QoS assurance. Risdianto and a totality is employed by the interchange that et al. [4] evaluated the performance of an SDN- takes place with regard to the controller and the based virtual network on different virtualisation switches via a safe channel. The OpenFlow environments, including operating system based switch and controller communicate via a safe virtualisation, hardware-assisted virtualisation, channel that is realised in the context of a and par virtualisation. Sgambelluri and et al. [8] distinctive practice and, according to [10], this is proposed novel mechanisms that have been also referred to as OpenFlow. The controller specifically introduced to maintain working and transmits a Flow Table towards the OpenFlow backup flows at different priorities and to switches and each stream passage in the former guarantee effective network resource utilisation element is constituted of a regulation crafted when a failed link is recovered. from an organisation of fields, thereby A hybrid intelligent system is proposed in this coordinating the approaching bundles. It is paper to optimise the performance of the SDN. notable that this is an activity that characterises The proposed system includes ANN to model the issue of how the coordinating parcels can be inputs-outputs of the network and evolutionary prepared, for example, by transmitting on a algorithms employed to find the optimal set of specific yield port. In addition, a number of inputs that maximize the SDN performance. We counters are employed in order to collect present a new method to lessen the burden of information relating to the stream. In a similar SDN switch by making the network controller way, each passage can be linked to alternative poll the switches adaptively, instead of making information, one case of this being the the SDN switch wait for an event all the time. requirement level and the hard and idle timeout of the pair of timing devices. The bundle is captured and conveyed to the controller that employs the safe channel in those instances by ANNs and, notably, this relationship can be where a switch receives a parcel that is not straightforwardly, rapidly, and cost- and time- engaging in the coordination of the passages that effectively discerned by lowering the error with have been introduced. Having captured the regard to the network output(s) and the actual bundle, the activity of the OpenFlow switch is output(s). A defining feature is that, following regulated by the controller as a result of the the appropriate preparation of the network, overhaul of its Flow Tables, thereby transmitting outputs can be estimated within a very short the coordinating parcels to the end point [8]. One space of time (namely, in a matter of seconds). implication of this is that the relative casings are Frameworks that centre on artificial neural not required to partake in the controller another networks are currently seeing effective time as a result of the potential initiation of the application in a range of areas – a few examples Flow Tables’ reformulation [3]. being including adaptive control, laser applications, nonlinear system identification, robotics, image and signal processing, medical areas, pattern recognition, error detection, process logging, and renewable and sustainable energy areas – in order to surmount obstacles faced by engineers [11]. 3.2 Evolutionary Algorithms Taking inspiration and motivation from natural processes and, in addition, basing the developments on factors relating to iterative and probabilistic processes, a range of evolutionary algorithms – including GAs, PSO, and simulated annealing – have been formulated in recent years. The primary purpose of such developments is for application in optimisation issues. Two multi-purpose and frequently employed algorithms include GA and PSO, and these are utilised in every domain [12]. 3.3 Genetic algorithm Formulated by Holland in 1975, GAs are self- modifying global optimisation probability search algorithms, the fundamental concept of which Figure 1.The software-defined network’s architecture was inspired by the genetic mechanisms that form the basis of the theory of Darwinian natural selection and biological evolution. GAs operate 3 Artificial Intelligence by simulating the biological processes that are 3.1 Neural Networks observed in the natural world as driving the phenomena of genetic and evolutionary A branch of artificial intelligence, disseminated development; according to the concept of natural at a considerable pace in recent times owing to selection, GAs provide solutions to deep its suitability with regard to the modelling and problems by employing code technique and forecasting ability it has in relation to dynamic reproduction processes [13]. GAs have been systems, is the ANN. In light of their promising extensively employed in a variety of domains potential, ANNs have emerged as a central with considerable efficacy in recent years, and branch of research into artificial intelligence. The this is primarily attributed to their almost registration of the input-output relationships of universal relevance and promising results. nonlinear and synthesis systems have been identified as one of the key advantages offered 3.4 Particle Swarm Optimisation experiments were carried out using POX First developed by Kennedy and Eberhart in controller and monitoring of the flows was 1995 [14] and built on by the researchers several required for all events. Regarding which, in order years later [15], PSO algorithms have been to build the learning system, the SDN controller applied with enormous success in optimising a gives the orders to SDN switch to monitor the broad range of applications [16]. PSO operates flows, and the switch keeps on monitoring them by locating all individuals and particles (usually to detect the events. When the flows are in the range of 10-100) in randomised positions monitored the entire event, whether they occur and, following this, intending that each particle only frequently or periodically, are stored in the engages in random motion in a determined database to be used in the ANN learning system, direction in the search space. Following this, the and consequently the data which are collected direction of each particle is incrementally from the ANN learning system is considered as modified in order to proceed according to the being an efficient input to the optimisation optimal previous positions, thereby identifying algorithms. This paper presents a new method to more favourable positions on the basis of minimize the load of the SDN switch by making specifications or an objective function (i.e. it changing adaptively by the controller, instead fitness). The original particle speed and location of wait for the event all the time. are chosen randomly and, in turn, the velocity 4.2 SDN Identification formula presented below is used to provide When the ANN training started, the dataset had updates: been pre-processed by normalising them into the range between -1 and 1. The dataset of the inputs and outputs was divided randomly into three subsets: a training set, valediction set and testing Contrastingly, the new particle is computed by set. The first subset was for establishing the summing the new velocity to that which precedes gradient as well as updating the network weights it, as presented below: and biases. The error regarding the second subset was observed during the training development. where Vc denotes the particle’s velocity; x The validation error is usually reduced in the denotes the particle’s position; R1 and R2 are initial training phase, as is the training set error. independent random variables uniformly Nevertheless, when the network overfits the data, distributed in [0, 1]; C1 and C2 are the the error in the validation set invariably starts to acceleration coefficients; and w represents the rise. In the current case, the network parameters inertia weight. The particle’s new velocity can be were saved at the minimum of the validation set calculated by employing Eq. (1), and the error [17]. information required includes the previous velocity, the distance of the particle’s present Input values need to be normalised in the range [- position from its optimal position (Pb), and the 1, 1], which corresponds to the minimum and global best position (GB). Following this, on the maximum actual values. Subsequently, testing basis of Eq. (2), the particle is conveyed to a new the ANN requires a new independent set (test location in the search space and, notably, the way sets) in order to validate the generalisation in which every particle performs is evaluated in capacity of the prediction model. A multilayer relation to a predetermined objective function feedforward network was implemented to known as the performance index. estimate the performance of the SDN. In order to obtain a maximum accuracy of prediction, the 4 Simulation and Results network was trained in different topologies. For each network architecture, the training was run 4.1 SDN Simulation ten times for various random initial weights and SDN simulation has been performed using biases using the Levenberg Marquardt algorithm the Mininet platform to collect all datasets of (LMA). After investigating the performance of inputs and outputs. In addition, simulation different architectures using the exhaustive search method, the best trained ANN with one hidden layer was found to consist of 17 neurons in this hidden layer, which gives the comparably better performance of MSE, with 2.488 10-8. Fig. 3 shows the performance of the network as a mean square error (MSE) versus the network architecture for the single hidden layer. Also, Figs 2 and 3 show the simulated and predicted SDN performance for both the training and test sets. It is noticed that the ANN model is efficiently accurate and the network is accepted as a general model to be integrated, as the next step, with GA or PSO so as to produce the proposed intelligent hybrid system. Figure 3. Comparing predicted with actual SDN performance for the testing sets

4.3 SDN Optimisation GA and PSO were integrated separately with the trained ANN model to select the optimal set of inputs that make the network work as efficient as possible. Fig. 4 shows the architecture of the system including the trained general ANN model as well as PSO and GA as an optimiser. Figure 2. Performance of a single hidden layer ANN

Figure 4. The architecture of the proposed method

The simulation experiments were carried out by MATLAB platform. PSO was employed to find the optimal structure of the network and the best operational parameters of this and the GA algorithm were chosen after extensive simulations, which were set as follows:  Size of the population or swarm: 50 Figure 2. Comparing predicted with actual SDN  Maximum iterations or generations performance for the training sets (max) :100  Cognitive acceleration (C1): 1.2  Social acceleration (C2): 0.12  Momentum or inertia (w): 0.9 A comparison of the results of the SDN performance is provided in Table 1 and Fig. 6. PSO has outperformed GA regarding the performance and computational time, the convergence is faster with fewer iterations and the obtained fitness is higher.

Table 1. Performance and computation time comparisons for GA and PSO

Optimisation SDN Performance Computation method (%) Time (min) GA 96.321 3.23 PSO 94.846 6.75

Figure 5. Convergence comparison of GA and PSO 5 Conclusions in 2015 11th International Conference on Natural Computation (ICNC), 2015, pp. In this paper, a hybrid intelligent system has been 250–254. proposed for modelling and optimising the Software-Defined Network (SDN). An artificial [6] C. Gao, H. Wang, F. Zhu, L. Zhai, and S. neural network (ANN) with a single layer in the Yi, “A Particle Swarm Optimization hidden zone has been trained to map the inputs Algorithm for Controller Placement and the performance of the network. The results Problem in Software Defined Network,” have shown that the network gives a very Springer International Publishing, 2015, pp. acceptable MSE, we hereby this has been 44–54. demonstrated as being less than 2.466×10-8. The [7] Z. F. Zhang Shunmiao, “Survey on software generality of the trained ANN model has been defined network research,” Appl. Res. checked by applying unseen data as a test set and Comput., vol. 30, pp. 2246–2251, 2013. subsequently, this general ANN model has been [8] A. Sgambelluri, A. Giorgetti, F. Cugini, F. synergised with evolutionary algorithms (EAs) to Paolucci, and P. Castoldi, “OpenFlow- produce the proposed intelligent hybrid system. Based Segment Protection in Ethernet GA and PSO have been employed as an EA Networks,” J. Opt. Commun. Netw., vol. 5, optimiser to find the optimal set of inputs to the no. 9, p. 1066, Sep. 2013. SDN based on the maximum performance as a [9] S. Baik, Y. Lim, J. Kim, and Y. Lee, fitness function. According to the obtained “Adaptive flow monitoring in SDN results, PSO outperforms GA regarding architecture,” in 2015 17th Asia-Pacific performance, convergence and computational Network Operations and Management time. Symposium (APNOMS), 2015, pp. 468–470. 6 Acknowledgment [10] D. Kreutz, F. M. V. Ramos, P. Esteves Verissimo, C. Esteve Rothenberg, S. The corresponding author is grateful to the Iraqi Azodolmolky, and S. Uhlig, “Software- Ministry of Higher Education and Scientific Defined Networking: A Comprehensive Research for supporting the current research. Survey,” Proc. IEEE, vol. 103, no. 1, pp. 14–76, Jan. 2015. 7 References [11] Y. K. Yousif, K. M. Daws, and B. I. Kazem, [1] Y. Al-Dunainawi and M. F. Abbod, “Hybrid “Prediction of Friction Stir Welding Intelligent Approach for Predicting Product Characteristic Using Neural Network,” Composition of Distillation column,” Int. J. JJMIE Jordan J. Mech. Ind. Eng., vol. 2, no. Adv. Res. Artif. Intell., vol. 5, no. 4, pp. 28– 3, 2008. 34, 2016. [12] M. Azarkaman, M. Aghaabbasloo, and M. [2] D. Ruan and P. Wang, Intelligent hybrid E. Salehi, “Evaluating GA and PSO systems: fuzzy logic, neural networks, and evolutionary algorithms for humanoid walk genetic algorithms. Springer, 1997. pattern planning,” in 2014 22nd Iranian [3] W. Xia, Y. Wen, C. H. Foh, D. Niyato, and Conference on Electrical Engineering H. Xie, “A Survey on Software-Defined (ICEE), 2014, pp. 868–873. Networking,” IEEE Commun. Surv. [13] B. Sun, L. Li, and H. Chen, “Shortest Path Tutorials, vol. 17, no. 1, pp. 27–51, 2015. Routing Optimization Algorithms Based on [4] A. C. Risdianto and E. Mulyana, Genetic Algorithms,” Comput. Eng., 2005. “Implementation and analysis of control and [14] R. Eberhart and J. Kennedy, “A new forwarding plane for SDN,” in 2012 7th optimizer using particle swarm theory,” International Conference on MHS’95. Proc. Sixth Int. Symp. Micro Telecommunication Systems, Services, and Mach. Hum. Sci., pp. 39–43, 1995. Applications (TSSA), 2012, pp. 227–231. [15] R. C. Eberhart, Y. Shi, and J. Kennedy, [5] Y. Yilan Liu, Y. Yun Pan, M. Muxi Yang, Swarm intelligence. 2001. W. Wenqing Wang, C. Chi Fang, and R. [16] R. Poli, “Analysis of the Publications on the Ruijuan Jiang, “The multi-path routing Applications of Particle Swarm problem in the Software Defined Network,” Optimisation,” J. Artif. Evol. Appl., vol. 2008, no. 2, pp. 1–10, 2008.