Hybrid meta-heuristic VM load balancing optimization approach

Yadav, Mala; Gupta, Sachin Kumar

doi:10.1080/02522667.2020.1733190

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…A hybrid GA and PSO (HGA‐PSO)‐based load balancing approach was proposed by Yadav and Gupta 20 for discovering optimal candidate solution that aids in reactive balancing of incoming loads in cloud environment. This HGA‐PSO concentrated on the process of searching approximate solution for the objective of VM load balancing process.…”

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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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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“…Mala Yadav et al [10] have developed a multi-criterion scheduling technique for multiprocessor computer systems. This application combines quantum computing and the gravitational search approach, both of which were inspired by nature.…”

Section: Literature Surveymentioning

confidence: 99%

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Alghamdi

2022

Sustainability

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As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.

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“…Advanced outcomes achieved in terms of execution when proposed approach assessed on Cloud Analyst Simulation tool. M. Yadav & S. Gupta [17] proposed aalgorithm that wipes out the downsides of executing a basic cooperative design in distributed computing by presenting an idea of assigning various time slices to individual processes relying upon their needs.…”

Section: Gs Singh and Tmentioning

confidence: 99%

A New Load Balancing Algorithm for Better Resource Utilization in Cloud Computing Environment

Grewal¹

2020

IJATCSE

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Cloud computing is a developing concept that has been used in every sector either government or private sector. Powerful data centres are used on clouds to handles the large number of requests. Dynamic pool of requests and virtualization has been provided by the cloud. To handle large number of requests, a proper load balancing technique is required that can equally share the loads among data centres. In this paper, a novel load balancing algorithm is proposed that will help in efficient utilization of resources. Set the priority of each request and Virtual Machines (VM) based on some predefined parameters. Experimental result shows that proposed technique is more efficient and less time consuming.

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Hybrid meta-heuristic VM load balancing optimization approach

Cited by 13 publications

References 13 publications

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

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

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

A New Load Balancing Algorithm for Better Resource Utilization in Cloud Computing Environment

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