LBABC: Distributed controller load balancing using artificial bee colony optimization in an SDN

Sridevi, K.; Saifulla, M. A.

doi:10.1007/s12083-023-01448-2

Cited by 17 publications

(5 citation statements)

References 27 publications

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“…𝑐𝑡 𝑖𝑗 = et ij + 𝑤𝑡 𝑖 (10) Where V m j represents the 𝑗th VM and the time of waiting to accomplish the 𝑖th task is denoted as 𝑤𝑡 𝑖 .…”

Section: Task Scheduling To Vmsmentioning

confidence: 99%

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Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

2024

IJIES

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An efficient task scheduling plays an important role in facilitating the virtual resource in a cloud computing environment by minimalizing make span and enhancing the allocation of resources. Requests for resources are treated as tasks, and appropriate resources are allocated based on user requirements. But, due to high demand and requests, the cloud has difficulty allocating resources. To overcome the issues, this research introduced an optimization-based task scheduling approach. The Multi-Objective Prairie Dog Optimization (MOPDO) algorithm is introduced which considers the makespan time and the execution time as the major objective while allocating resources in IoT. The proposed MOPDOA effectively allocates the resource to the Virtual Machines (VMs) by choosing the host with maximized resources. The search mechanism with the help of MOPDO helps to detect a suitable VM for resource allocation will be continued. After the process of allotting the resources to VMs, the load balancing process must be initiated to schedule the tasks for VMs. When the task count is assigned as 100, the makespan time of MOPDOA is 12s while Particle Swarm Gray Wolf Optimization (PSGWO) obtains a makespan time of the 20s. Similarly, for different VMs, the proposed approach is 175.45s for execution whereas the existing Improved Multi-Objective Multi-Verse Optimizer obtains 186.33s to execute for 10 VMs.

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“…𝑐𝑡 𝑖𝑗 = et ij + 𝑤𝑡 𝑖 (10) Where V m j represents the 𝑗th VM and the time of waiting to accomplish the 𝑖th task is denoted as 𝑤𝑡 𝑖 .…”

Section: Task Scheduling To Vmsmentioning

confidence: 99%

“…The optimized load balancer acts as an important component that considers all the aforementioned issues by using efficient balancing strategies [10]. In general, load balancing is classified into two types such as static load balancing and dynamic load balancing.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

2024

IJIES

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“…The experiments are conducted in the Mininet virtual environment [41] with a POX controller [42]. We used Mininet version 2.3.2 in our experiments, which supports Open Virtual Switches (OVS) [43].…”

Section: Experimental Environmentmentioning

confidence: 99%

Detecting and Mitigating Botnet Attacks in Software-Defined Networks Using Deep Learning Techniques

et al. 2023

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Software-Defined Networking (SDN) is an emerging architecture that enables flexible and easy management and communication of large-scale networks. It offers programmable and centralized interfaces for making complex network decisions dynamically and seamlessly. However, SDN provides opportunities for businesses and individuals to build network applications based on their demands and improve their services. In contrast, it started to face a new array of security and privacy challenges and simultaneously introduced the threats of a single point of failure. Usually, attackers launch malicious attacks such as botnets and Distributed Denial of Service (DDoS) to the controller through OpenFlow switches. Deep learning (DL)-based security applications are trending, effectively detecting and mitigating potential threats with fast response. In this article, we analyze and show the performance of the DL methods to detect botnet-based DDoS attacks in an SDN-supported environment. A newly self-generated dataset is used for the evaluation. We also used feature weighting and tuning methods to select the best subset of features. We verify the measurements and simulation outcomes over a self-generated dataset and real testbed settings. The main aim of this study is to find a lightweight DL method with baseline hyper-parameters to detect botnet-based DDoS attacks with features and data that can be easily acquired. We observed that the best subset of features influences the performance of the DL method, and the prediction accuracy of the same method could be variated with a different set of features. Finally, based on empirical results, we found that the CNN method outperforms the dataset and real testbed settings. The detection rate of CNN reaches 99% for normal flows and 97% for attack flows.

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“…Most of the authors focused on minimizing the load of the overloaded controller. In contrast the authors in [24] tried the solve the problem by distributing the load equally in long run using artificial bee colony (ABC) optimization and reduced the number of switch migrations thereby improving the performance. The authors in [25] used the ACO technique for traffic load balancing in SDN.…”

Section: Related Workmentioning

confidence: 99%

Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking

Belgaum,

Musa,

Ali

et al. 2023

IEEE Access

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The ever-increasing heterogeneous connections and the demands of the users pose many new challenges to the network service providers to sustain by providing improved quality of service (QoS). Software-defined networking (SDN) is a game changer in networking by allowing user customization to enhance performance. With the advent of 5G and the increasing user requests, a massive volume of heterogeneous traffic is generated in the network, increasing load. Currently, the existing load balancing techniques lack efficiency in handling the load under unicontroller deployment. In addition, the network paths selected must also be reliable and optimal. We proposed the self-socio adaptive, reliable particle swarm optimization (SSAR-PSO) load balancing technique to address the issue of load balancing in the unicontroller deployment of SDN. In the proposed technique, the performance of the node itself, known as direct information, and the performance of the neighbouring nodes, known as indirect information, were considered to identify the reliable node to form an optimal path. Simulation results showed that the proposed technique outperforms the existing state of art techniques under both TCP and UDP load in the following network performance metrics: latency, packet loss ratio, throughput, average round trip time, and bandwidth utilization ratio.INDEX TERMS Load balancing, particle swarm optimization, quality of service, reliability, softwaredefined networking.

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LBABC: Distributed controller load balancing using artificial bee colony optimization in an SDN

Cited by 17 publications

References 27 publications

Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

Detecting and Mitigating Botnet Attacks in Software-Defined Networks Using Deep Learning Techniques

Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking

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