Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> Monitoring transit traffic at one or more points in a network is of interest to network operators for reasons of traffic accounting, debugging or troubleshooting, forensics, and traffic engineering. Previous research in the area has focused on deriving a placement of monitors across the network toward the end of maximizing the monitoring utility of the network operator for a given traffic routing. However, both traffic characteristics and measurement objectives can dynamically change over time, rendering a previously optimal placement of monitors suboptimal. It is not feasible to dynamically redeploy/reconfigure measurement infrastructure to cater to such evolving measurement requirements. We address this problem by strategically routing traffic subpopulations over fixed monitors. We refer to this approach as MeasuRouting. The main challenge for MeasuRouting is to work within the constraints of existing intradomain traffic engineering operations that are geared for efficiently utilizing bandwidth resources, or meeting quality-of-service (QoS) constraints, or both. A fundamental feature of intradomain routing, which makes MeasuRouting feasible, is that intradomain routing is often specified for aggregate flows. MeasuRouting can therefore differentially route components of an aggregate flow while ensuring that the aggregate placement is compliant to original traffic engineering objectives. In this paper, we present a theoretical framework for MeasuRouting. Furthermore, as proofs of concept, we present synthetic and practical monitoring applications to showcase the utility enhancement achieved with MeasuRouting. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> In today's IT systems, cyber security requires fine-grained, flexible, adaptable and cost optimized monitoring mechanisms. The emergence of new networking technologies, like Network Function Virtualization (NFV) and Software Defined Networking (SDN), opens up new venues for large scale adoption of these cyber security tools. In particular, Deep Packet Inspection (DPI) engines can be virtualized and dynamically deployed as pieces of software on commodity hardware. Deploying such software DPI engines is costly in terms of license fees and power consumption. Designing cost effective DPI engine deployment strategies that meet the cybersecurity operational constraints is thus mandatory for the adoption of this approach. For this purpose, we propose a method, based on genetic algorithms, that optimizes the cost of DPI engine deployment, minimizing their number, the global network load and the number of unanalyzed flows. We conduct several experiments with different types of traffic and different cost structures. The results show that the method is able to reach a trade-off between the number of DPI engines and network load. Furthermore, the global cost can be reduced up to 58% when relaxing the constraint on the used link capacity, that is the provisioning rate. <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> Load balancing service is essential for distributing workload across server farms or data centers and mainly provided by dedicated hardware. In recent years, the concept of Software-Defined Networking (SDN) has been applied successfully in the real network environment, especially by OpenFlow designs. This paper presents an OpenFlow-based load balancing system with the genetic algorithm. This system can distribute large data from clients to different servers more efficiently according to load balancing policies. Furthermore, with the pre-configured flow table entries, each flow can be directed in advance. Once the traffic burst or server loading increased suddenly, the proposed genetic algorithm can help balance workload of server farms. The experiments demonstrate the better performance of the proposed method compared to other approaches. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the hard constraints of network measurement resources. This paper proposes a new framework, SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can leads to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications including per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring 8.8\% of per-flow path delays in Harvard network, congested paths can be detected with probability 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet. <s> BIB004 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> Software Defined Nerwork (SDN) provides centralized and opened way to manage the network by separating both control plane and data plane each other. The controller defined as a software is responsible for a control plane. If there is too much load on a specific controller, controller's suboptimal performance problem will be occurred. Because of this problem, network will provide services in an inefficient way. OpenFlow provides a way to connect switches with multiple controllers so that the controllers' load can be balanced. This paper suggests a solution to distribute and balance the load on controllers based on genetic algorithm when imbalance is detected. Then we can get an optimized way to deploy the switches to controller and network services can be provided efficiently. <s> BIB005 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> Software Defined Networking (SDN) technologies provide applications opportunities to manipulate underlying network flows and topologies via network controllers during runtime. In cloud environments, networked virtual machines can be enhanced by SDN by providing applications with controllable infrastructures to meet system-level quality requirements; however, customizing a suitable network topology with optimally placed controller(s) for given quality requirements and workload characteristics is often not an easy task. We call such problem virtual SDN network planning problem. In this paper, a Topology-Controller planner (TCPlanner) is proposed for customizing the network topology and placing the controllers. Experiments with different scales of network show that our approach can effectively plan virtual SDN networks to meet the various QoS requirements and reduce costs. <s> BIB006 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> To support emerging traffic demands, metro optical networks evolve over time with the addition of new links to relieve network bottlenecks and congestion. These new resources need to be optimally located in the network, in order to minimize the cost of the new deployment. Spectrum-fragmentation and network-wide load imbalance impede the optimal allocation of network resources. With the recent convergence of advanced flexible and programmable optical devices with emerging software-defined network paradigms, it is possible to flexibly and dynamically defragment the spectrum and balance the load on the network. This study introduces an optimization strategy for software-defined elastic optical networks for fiber-load balancing across the network, while minimizing the cost of the resulting service disruption. An entropy-based metric is proposed for measuring load imbalance and used to design utility functions for the joint optimization, taking into consideration the optical and defragmentation constraints. <s> BIB007 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> Abstract A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today’s large-scale networks, this is a challenging task due to the scarcity of network measurement resources and the hard constraints that this imposes. This paper proposes a new framework, called SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can lead to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications for per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring only 8.8% of all per-flow path delays in Harvard network [1], congested paths can be detected with probability of 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet. <s> BIB008 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithms in SDN <s> The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: 1) the elapsing of a predefined time interval, or, 2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3x compared to ECMP while packet loss is decreased up to 2x. <s> BIB009
Genetic algorithm were mainly used for routing  , virtual network planning BIB006 , optimizing the cost of deploying deep packet inspection functions in SDN BIB002 , load balancing BIB003 BIB005 BIB007 , designing optimal observation matrices BIB008 , resource reallocation for SDN-based data centres BIB004 , and congestion avoidance BIB009 . Yu and Ke  introduced, GA-SDN, a genetic algorithmbased routing algorithm for enhancing the video delivery quality over SDNs. The experimental results showed that GA-SDN outperformed Bellman-Ford routing algorithm in terms of packet drop rate, throughput, and average peak signal-to-noise ratio. Wang et al. BIB006 studied the problem of virtual network planning in SDNs based on GA approach. The virtual network refers to customise a network topology and place the controllers that should meet a given QoS requirements. Their proposed approach outperformed a greedy solution. In addition, compared to k-medoids, the proposed algorithm showed that it can reduce the number of required SDN controllers. However, k-medoids showed that it can outperform the GA-based solution in terms of the average latency. Bouet et al. BIB002 presented a cost-based method for optimal deployment of deep packet inspection (DPI) engines in NFVSDNs. The experimental results showed that the method was able to reach a trade-off between the number of DPI engines and network load. In addition, the global cost can be reduced up to 58% when relaxing the constraint on the used link capacity. Chou et al. BIB002 introduced a policy-based load balancing system, where the authors proposed a genetic-based load balancing approach. Their system outperformed load-based, round-robin, and random choice-based approaches in terms of arithmetic average for coefficient of variation. Kang and Kwon BIB005 also introduced an SDN-based load balancer where the authors introduced a mapping solution as a tree structure. The authors suggested that a master (super) controller, which should be responsible for the load balancing operation can be represented as a root node, and the other controllers can be indicated by an internal node and finally a switch will be represented by a leaf node. Mahlab et al. BIB007 proposed a fragmentation-aware load-balancing strategy for optical networks. The goal here is to optimize the fiber-load across the network. The authors employed an entropy-based metric for measuring the load imbalance and then used it for designing a joint entropy/hits utility function for the optimization, which was solved by a GA. Malboubi et al. BIB008 BIB004 presented, SNIPER, a framework for designing the optimal observation matrix that can lead to the best estimation accuracy using matrix completion techniques. The authors used GA and PSO to deal with large-scale optimal observation matrices. The experimental results showed that the proposed approach can be applied to many network monitoring applications in large-scale networks under hard resource constraints. For instance, the authors showed that by measuring only 8.8% of all per-flow path delays in Harvard network BIB001 , congested paths can be detected with probability of 0.94. Tajiki et al. BIB009 introduced, CECT, a congestion avoidance scheme for SDN-based cloud data centres. They used a routing architecture to reconfigure the network resources, which is triggered when a predefined time interval or when a congestion occurs. They proposed a meta-heuristic approach based on genetic algorithms to find the optimal solution for the optimization problem. CECT can enhance the total network throughput up to 3x while it decreases the packet loss up to 2x when compared with ECMP (Equal Cost Multiple Path).