An Improved Approach for Load Balancing among Virtual Machines in Cloud Environment

Sansanwal, Suman; Jain, Naresh C.

doi:10.1016/j.procs.2022.12.058

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Load balancing [3] redistributes the all-out load of the dispersed framework into separate hubs to confirm that no * Corresponding author's e-mail: ssuman15@gmail.com machine is overloaded or inactive [4]. Further load balancing optimizes the setup energy consumption and execution time, thereby increasing the strength of a particular system [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Sansanwal,

Jain

2023

Applied Computer Systems

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Cloud remains an active and dominant player in the field of information technology. Hence, to meet the rapidly growing requirement of computational processes and storage resources, the cloud provider deploys efficient data centres globally that comprise thousands of IT servers. Because of tremendous energy and resource utilization, a reliable cloud platform has to be necessarily optimized. Effective load balancing is a great option to overcome these issues. However, loading balancing difficulties, such as increased computational complexity, the chance of losing the client data during task rescheduling, and consuming huge memory of the host, and new VM (Virtual Machine), need appropriate optimization. Hence, the study aims to create a newly developed IG-WA (Inquisitive Genetic–Wolf Optimization) framework that meritoriously detects the optimized virtual machine in an environment. For this purpose, the system utilises the GWO (Grey Wolf Optimization) method with an evolutionary mechanism for achieving a proper compromise between exploitation and exploration, thereby accelerating the convergence and achieving optimized accuracy. Furthermore, the fitness function evaluated with an inquisitive genetic algorithm adds value to the overall efficacy. Performance evaluation brings forward the outperformance of the proposed IGWO system in terms of energy consumption, execution time and cost, makespan, CPU utilization, and memory utilization. Further, the system attains more comprehensive and better results when compared to the state of art methods.

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Section: Introductionmentioning

confidence: 99%

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Sansanwal,

Jain

2023

Applied Computer Systems

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Multi-objective optimization of virtual machine migration among cloud data centers

Maldonado Carrascosa,

Seddiki,

Jiménez Sánchez

et al. 2024

Soft Comput

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Workload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%.

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An Improved Approach for Load Balancing among Virtual Machines in Cloud Environment

Cited by 2 publications

References 25 publications

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Multi-objective optimization of virtual machine migration among cloud data centers

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