An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment

Ramasamy, V.; Pillai, SudalaiMuthu Thalavai

doi:10.1007/s10586-020-03118-x

Cited by 19 publications

(12 citation statements)

References 13 publications

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“…Hence, Chen et al 20 Several methods cannot provide powerful performance evaluation between the artificial demand analysis machines and VMs since it troubles by interferences. Thus, Ramasamy et al 21 proposed a powerful algorithm to attain dynamic resource allocation by VMs. In this approach, the demand of tasks from the number of users is fed to the feature extraction process.…”

Section: Literature Surveymentioning

confidence: 99%

Multi‐objective hybrid capuchin search with genetic algorithm based hierarchical resource allocation scheme with clustering model in cloud computing environment

Gola

Singh

Gupta

et al. 2023

Concurrency and Computation

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Internet of recent decades considered cloud computing as the most effective and distributed platform. It is a comfortable and quick way to access shared resources over the Internet anytime. The major problem cloud customers face while choosing the resources for a particular application is QoS. In the cloud computing environment, various resources need to be effectively allocated on VMs by reducing makespan and synchronously increasing resource utilization. For that, a novel multi-objective hybrid capuchin search with genetic algorithm (MHCSGA) based hierarchical resource allocation is established in this work. MHCSGA optimizes the multi-objective functions like resource utilization, response time, makespan, execution time and throughput. Initially, partitioning around the K-medoids clustering method is utilized to allocate the resources optimally. During clustering, the tasks are divided into two cluster groups then, the optimization is performed to attain an optimal resource allocation process.The experimental setup is executed using the JAVA tool. For the simulation process, the proposed work uses the GWA-T-12 Bitbrains dataset. The makespan achieved by proposed algorithm for 50, 100, 150, and 200 tasks are found to be 10. 45, 17.6, 25.67, and 31.34, respectively. The comparison analysis proves that the developed model attains improved performance than the state-of-the-art works.

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Section: Literature Surveymentioning

confidence: 99%

Multi‐objective hybrid capuchin search with genetic algorithm based hierarchical resource allocation scheme with clustering model in cloud computing environment

Gola

Singh

Gupta

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In order to choose the optimal resource, a survey improvement approach in [12] suggested combining the fake bumble bee region (ABC) model with the replicated treating (SA) methodology. In response to growing need for dynamic asset distribution in virtual machines, the designers have developed a robust computation to overcome impedance difficulties in evaluating the display of asset part methodologies [13].In [14], the authors promoted a different levelled multi-specialist streamlining (HMAO) method to handle dispersed computing assets, with an eye on maximising asset use and minimising transmission capacity costs. This method combines multi-specialist streamlining with hereditary computations (GA) to identify administrative hubs with the best resource utilisation for job delivery.…”

Section: Literature Surveymentioning

confidence: 99%

Capuchin Search Particle Swarm Optimization (CS-PSO) based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment

Gupta,

Singh

2023

IJRITCC

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This review introduces the methods for further enhancing resource assignment in distributed computing situations taking into account QoS restrictions. While resource distribution typically affects the quality of service (QoS) of cloud organizations, QoS constraints such as response time, throughput, hold-up time, and makespan are key factors to take into account. The approach makes use of a methodology from the Capuchin Search Particle Large Number Improvement (CS-PSO) apparatus to smooth out resource designation while taking QoS constraints into account. Throughput, reaction time, makespan, holding time, and resource use are just a few of the objectives the approach works on. The method divides the resources in an optimum way using the K-medoids batching scheme. During batching, projects are divided into two-pack assembles, and the resource segment method is enhanced to obtain the optimal configuration. The exploratory association makes use of the JAVA device and the GWA-T-12 Bitbrains dataset for replication. The outrageous worth advancement problem of the multivariable capacity is addressed using the superior calculation. The simulation findings demonstrate that the core (Cloud Molecule Multitude Improvement, CPSO) computation during 500 ages has not reached assembly repeatedly, repeatedly, repeatedly, and repeatedly, respectively.The connection analysis reveals that the developed model outperforms the state-of-the-art approaches. Generally speaking, this approach provides significant areas of strength for a successful procedure for improving resource designation in distributed processing conditions and can be applied to address a variety of resource segment challenges, such as virtual machine setup, work arranging, and resource allocation. Because of this, the capuchin search molecule enhancement algorithm (CSPSO) ensures the success of the improvement measures, such as minimal streamlined polynomial math, rapid consolidation speed, high productivity, and a wide variety of people.

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“…Based on the HPSO-MGA algorithm, Ramasamy and Pillai [24] offer a unique dynamic resource allocation strategy. e suggested method is divided into two parts, the first of which involves feature extraction and feature reduction.…”

Section: Related Workmentioning

confidence: 99%

Optimized Production of Biodiesel Using Internet of Things Sensed Temperature with Hybrid Particle Swarm Optimization

Loganathan¹,

Kannan²

2022

Journal of Chemistry

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Biofuel production offers a non-fossil fuel that can be utilized in modern engines without any redesign. Regardless of receiving rising attention, many researchers have explored microalgae-based biofuel production and found biodiesel production is cost-effective compared to petroleum-centered conventional fuels. The primary reason is that the lipid accumulation of microalgae is possible. An efficient technique is proposed for optimized biodiesel manufacturing with microalgae through an IoT device with the hybrid particle swarm optimization (HPSO) algorithm for elapsing such drawbacks. First, the component of biodiesel is determined. Then, from the components, the temperature value is sensed through the IoT device. Based on the obtained temperature, the reaction parameters are optimized with HPSO to increase productivity and reduce cost. Finally, we observed performance and comparative analysis. The experimental results contrasted with the existent particle swarm optimization (PSO) and genetic algorithm (GA) concerning iteration’s temperature, concentration, production, and fitness. The present HPSO algorithm has differed from the existing PSO and GA concerning IoT sensed temperature and production function. Fitness value and instance concentration are the performance parameters. It varies based on the iteration values. Thus, the proposed optimized biodiesel production is advanced when weighed down with the top-notch methods.

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An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment

Cited by 19 publications

References 13 publications

Multi‐objective hybrid capuchin search with genetic algorithm based hierarchical resource allocation scheme with clustering model in cloud computing environment

Multi‐objective hybrid capuchin search with genetic algorithm based hierarchical resource allocation scheme with clustering model in cloud computing environment

Capuchin Search Particle Swarm Optimization (CS-PSO) based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment

Optimized Production of Biodiesel Using Internet of Things Sensed Temperature with Hybrid Particle Swarm Optimization

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