Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

Güreş, Emre; Shayea, Ibraheem; Ergen, Mustafa; Azmi, Marwan Hadri; El‐Saleh, Ayman A.

doi:10.1109/access.2022.3161511

Cited by 58 publications

(18 citation statements)

References 125 publications

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“…Throughout the iterative learning and training process, the system computes and evaluates 'reward points' to continuously enhance the learning process [172], [173]. The training and re-training phases result in the periodic generation of a well-refined "trained machine learning model" [174]- [176]. This model serves as a crucial element in making informed decisions related to various resource management operations.…”

Section: Machine Learning-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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Section: Machine Learning-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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“…In this paper, we introduce the Load Balancing and Energy-Efficient Migration Model (LBEEMM), a novel approach that employs bioinspired algorithms, advanced security measures, and machine learning techniques to optimize VM migrations. By leveraging Genetic Algorithms (GAs) and Ant Colony Optimization (ACO) for resource scheduling, we efficiently solve the complex optimization problems inherent in VM migration operations [10,11,12]. Furthermore, our model employs a Deep Reinforcement Learning-Based Iterative-learning Contextual Side chaining Model to enhance security measures, ensuring a robust defense against potential threats.…”

Section: Introductionmentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

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As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the Load Balancing and Energy-Efficient Migration Model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic Algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a Deep Reinforcement Learning-based Iterative-learning Contextual Side chaining Model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means Clustering and Fuzzy Logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ Bidirectional Long Short-Term Memory networks with Recurrent Graph Neural Network, for accurate workload prediction and informed migration decision Making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved by 2.5%, and the delay of computation was significantly reduced by 9.5%. Importantly, the integration of the Iterative-learning Contextual Side chaining Model significantly enhanced the security and quality of service (QoS) under attack scenarios, resulting in a 10.4% improvement in response speed, a 2.5% reduction in energy consumption during block mining, a 3.9% improvement in throughput, and an 8.5% reduction in storage costs. This Load Balancing and Energy-Efficient Migration Model represents a significant advancement in addressing the challenges of load balancing, energy efficiency, and security in VM migrations. Through the meticulous integration of bioinspired algorithms, advanced security measures, and machine learning techniques, our model provides a comprehensive and innovative solution that markedly improves system performance, reduces energy consumption, and fortifies security, thereby paving the way for a more efficient and secure cloud computing ecosystem.

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“…The self-optimization falls down on RRM which also consists of two functions: mobility robustness optimization (MRO) and load balancing optimization (LBO) [14]. MRO manages HO issues according to UE movements, while LBO focuses on traffic load balancing [15]. The MRO basically auto-tunes HCPs based on the network status to control irregular HO triggering [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

et al. 2022

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Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors' knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.

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Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

Cited by 58 publications

References 125 publications

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

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