Load balancing in cloud using improved gray wolf optimizer

Gohil, Bhavesh N.; Patel, Dhiren

doi:10.1002/cpe.6888

Cited by 6 publications

(9 citation statements)

References 24 publications

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“…These algorithms are used successfully in most real-world problems. The PSO and the ABC algorithms are state-of-art metaheuristic algorithms that were compared with GWO [38][39][40][41][42][43]. Therefore, these algorithms were used for comparison in this study.…”

Section: Discussionmentioning

confidence: 99%

A Hyperparameter Optimization for Galaxy Classification

Şenel¹

2023

Computers, Materials &Amp; Continua

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In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe's formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature, the classification using Convolutional Neural Network (CNN) is better. Three metaheuristic algorithms are used to obtain the optimum architecture of CNN. These are Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. A CNN architecture with nine hidden layers and two full connected layers was used. The number of neurons in the hidden layers and the fully connected layers, the learning coefficient and the batch size values were optimized. The classification accuracy of my model was 85%. The best results were obtained using GWO. Manual optimization of CNN is difficult. It was carried out with the help of the GWO meta-heuristic algorithm.

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Section: Discussionmentioning

confidence: 99%

A Hyperparameter Optimization for Galaxy Classification

Şenel¹

2023

Computers, Materials &Amp; Continua

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“…This HBB‐MBFOA strategy minimized the rate of tasks migration by 26.78%, compared to the classical methods of load balancing considered for investigation. Gohil and Patel 29 proposed an improved gray wolf optimization algorithm‐based load balancing (IGWOALB) scheme for distributing the load in a fair manner in the cloud computing environment. It handled the workload by distributing it between the multiple number of servers for preventing the issue of servers' underloading and overloading conditions.…”

Section: Related Workmentioning

confidence: 99%

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Ramya

Ayothi

2023

Trans Emerging Tel Tech

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Cloud computing is the most rapidly growing technology that facilitates virtualized computer resources to the users through its service providers. Load balancing and task scheduling is the important concern in the cloud computing scenario from the service provider point of view. The process of load balancing is responsible for allocating the dynamic workload among the cloud systems, such that equal sharing of the resources without the virtual machines (VMs) being overloaded or underloaded. This load balancing process not only needs to allocate the tasks to suitable VMs but also need to maintain trade‐off among VMs. Thus, a potential dynamic and optimized load balancing strategy is essential for improving resource utilization, services, reliability, and throughput by maintaining equilibrium between VMs with minimized makespan. In this article, hybrid dingo and whale optimization algorithm‐based load balancing mechanism (HDWOA‐LBM) is proposed for effective load balancing those aids in maximized throughput, reliability, and resource utilization in the clouds. This HDWOA‐LBM is proposed by mimicking the hunting characteristics of dingo (equivalent to tasks) and VMs as their prey to be hunted. It inherited the process of exploration and exploitation through the chasing and approaching, encircling, and harassing inherent with dingo optimization algorithm (DOA) to determine the optimal allocation of incoming tasks to the suitable VM. In specific, it utilized the merits of whale optimization for improving the exploitation phase of DOA to balance the trade‐off between local and global search. The simulation experiments of the proposed HDWOA‐LBM conducted using CloudSim confirmed better throughput of 21.28%, maximized reliability of 25.42%, minimized makespan of 22.98%, and improved resource allocation of 20.86%, on par with the competitive intelligent load balancing schemes used for investigation.

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“…Meanwhile, cloud infrastructure is recognized as a necessity in the IoT by providing a variety of Iaas, Paas, and Saas services. Upon increasing traffic flows, the demand for computing services grows, followed by unbalanced workload distribution and the processing time/completion of tasks [19], [20], [21].…”

Section: Figure 1 Resource Allocation Model To Workflow Tasks By the ...mentioning

confidence: 99%

“…Beta and delta wolves act as alternative solutions to guide the decision-making activities of the alpha wolf. Omega wolves follow the instructions of wolves with a higher rank under the title of remaining candidate solutions in the role of follower [19], [21]. Indeed, among all the obtained solutions, alpha, beta, and delta are considered the three best solutions, while omega wolves should change their location according to the location of the best global optimal search agents [19], [31], [36].…”

Section: Grey Wolf Optimizer Overviewmentioning

confidence: 99%

“…SDN controller can be effective in the optimal allocation of resources to tasks dynamically by obtaining infrastructure information to control cloud resources and manage traffic load fluctuations, as a decision-maker for processing load distribution. This method prevents congestion in the network and enhances the performance of the network significantly by meeting the requirements of the network entities [18], [19]. Figure 1 displays the process of mapping resources to workflow tasks by the controller.…”

Section: Introductionmentioning

confidence: 99%

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TMaLB: A Tolerable Many-Objective Load Balancing Technique for IoT Workflows Allocation

Rostami,

Goli-Bidgoli

2023

IEEE Access

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Recently, the load imbalance of network resources in the Internet of Things (IoT) due to the diverse and heterogeneous workflow of data, has attracted the attention of many researchers. To make appropriate load-balancing decisions, the influential Quality of Service (QoS) parameters should be identified. Most load-balancing techniques have focused on optimizing the multi-objective QoS while, the QoS optimization from the user, service provider and infrastructure provider directions simultaneously is an important many-objective problem. Extracting the QoS parameters from various directions, this paper proposes a tolerable many-objective load-balancing technique to efficiently allocate computing resources to workflows. Cost, response time, energy consumption, and CPU utilization have been investigated as the user, service provider, and infrastructure's QoS parameters solved by Grey Wolf Optimization (GWO) algorithm. Also, a load-tolerance concept has been introduced to improve the QoS-based load balancing and task acceptance rate together. The experiment results show that TMaLB has about 10% improvement compared to MaPSO and NSGA-III algorithms.INDEX TERMS Load-balancing, Quality of service, Many-objective, Multi-direction, Allocation of tasks.

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Load balancing in cloud using improved gray wolf optimizer

Cited by 6 publications

References 24 publications

A Hyperparameter Optimization for Galaxy Classification

A Hyperparameter Optimization for Galaxy Classification

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

TMaLB: A Tolerable Many-Objective Load Balancing Technique for IoT Workflows Allocation

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