A comprehensive survey of load balancing techniques in software-defined network

Hamdan, Mosab; Hassan, Entisar; Abdelaziz, Ahmed; Elhigazi, Abdallah; Mohammed, Bushra; Khan, Suleman; Vasilakos, Athanasios V.; Marsono, Muhammad Nadzir

doi:10.1016/j.jnca.2020.102856

Cited by 104 publications

(53 citation statements)

References 89 publications

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“…Intra-RAT HO can be either intra-or inter-frequency HO. Intra-RAT HO aims to preserve the connectivity of the UE with the existing network and the primary reason for this kind of HO can be attributed to load balancing or measurement trigger conditions [88]. Once UE HO occurs, it prefers to camp on the cell which provides the strongest received signal.…”

Section: ) Intra-/inter-rat Handovermentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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Section: ) Intra-/inter-rat Handovermentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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“…In the work of [ 14 ], the architecture of SDN layers considers load balancer as one block of SDN application tier. In the survey of [ 15 ], the authors provided a taxonomy of load balancing in SDN with discussing the various objectives such as response time, resources optimization throughout, and bottlenecks. Their taxonomy has classified load balancing in control plane and data plane.…”

Section: Literature Reviewmentioning

confidence: 99%

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Albowarab

Zakaria

Abidin

2021

Sensors

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Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

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“…Machine learning algorithms have been used to avoid or detect attacks and security problems, including cloud vulnerabilities, in a variety of ways [56]. The use of machine learning and its applications in cloud computing and related environments has been discussed in some of the most recent related works [57][58][59][60][61].…”

Section: Cloud Computing Replication Strategiesmentioning

confidence: 99%

Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

Waseem¹,

Din

Alshamrani

et al. 2021

Electronics

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Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. In this paper, we will try to find out which target objective is most addressed, which are average addressed, and which are least addressed in target-oriented replication strategies. The paper also includes a detailed discussion about the challenges, issues, and future research directions. This comprehensive analysis and performance evaluation based-work will open a new door for researchers in the field of cloud computing and will be helpful for further development of cloud-based dynamic data replication strategies to develop a technique that will address all attributes (Target Objectives) effectively in one replication strategy.

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A comprehensive survey of load balancing techniques in software-defined network

Cited by 104 publications

References 89 publications

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

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