Client Side-Server Side Load Balancing with Grasshopper optimization Mapreduce Enhancing Accuracy in Cloud Environment

Jeyakarthic, M.; Subalakshmi, N.

doi:10.1109/icisc47916.2020.9171130

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The outcomes of experiments validated that the established approach was more effective to balance the workload in comparison with the traditional algorithms. 2020) designed a novel hybrid approach named FIMPSO in which FFA (Firefly algorithm) was integrated with IMPSO in order to balance the load [12]. The search space was diminished using FFA in this approach.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Yadav¹,

Kumar²,

Saini

2022

IJRITCC

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Cloud computing is frequently alluded to as a model that furnishes boundless information handling conveniences with a compensation for each utilization framework. Present day cloud foundations resources as virtual machines (VMs) to actual machines utilizing virtualization innovation. All VMs works their involved structure and exhaust resources from their actual machine which behaves like a host. For load adjusting, Cloud moves VMs from exceptionally troubled real machines to low troubled actual machines. The delay of this calculation expansions in the organization as virtual machines are relocated. This work puts forward a new algorithm, namely Butterfly optimization for VM migration. The proposed optimization algorithm has been implemented in the MATLAB software. A comparative analysis is performed between the outcomes of the preceding and the new algorithm. The proposed algorithm has been evaluated over three performance parameters including delay, bandwidth used, and space used.

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Section: Literature Surveymentioning

confidence: 99%

A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Yadav¹,

Kumar²,

Saini

2022

IJRITCC

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“…The future work would focus on more QoS components concerning waiting time, migration time cost etc. M Jeyakarthic, et.al (2020) introduced CS-SS and GO along with Man Reduce for dealing with the issues of offloading and to cut down the exploitation of assets [9]. The server was selected by the client and a request was sent for enhancing the redundancy, reliability and availability.…”

Section: Dmentioning

confidence: 99%

Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Yadav

Kumar

Saini

2022

IJRITCC

View full text Add to dashboard Cite

Cloud computing is frequently alluded to as a model that furnishes boundless information handling conveniences with compensation for each utilization framework. Present-day cloud foundations resources as virtual machines (VMs) to actual machines utilizing virtualization innovation. All VMs works their involved structure and exhaust resources from their actual machine which behaves like a host. For load adjusting, Cloud moves VMs from exceptionally troubled real machines to low troubled actual machines. The delay of this calculation expansions in the organization as virtual machines are relocated. This work puts forward a new algorithm, namely o for VM migration. The proposed optimization algorithm has been implemented in MATLAB software. A comparative analysis is performed between the outcomes of the preceding and the new algorithm. The proposed algorithm has been evaluated over three performance parameters including delay, bandwidth used and space used.

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“…The process involves moving the virtual machines to a different host. Performance deterioration is the overhead of migration [1] . Virtual resources are allocated dynamically in the cloud based on user demand.…”

Section: Introductionmentioning

confidence: 99%

Optimizing load balancing for green cloud via efficient scheduling

Yenugula

2022

Int. J. Adv. Acad. Stud.

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The primary objective of cloud service providers is to maximize profits via the optimal use of cloud computing resources. Cloud technology is a business-oriented concept that aims at offering online IT resources as well as IT services on demand through a pay-as-you-go approach. Optimization of cloud servers on a regular basis is one of the trickiest parts of cloud computing. In order to promote the idea of green computing and increase host machine efficiency, load balancing in cloud datacenters is the major focus. In order to restore equilibrium to the data center's workload, it is necessary to use migration methods to move the virtual machines from the overloaded hosts to the less demanding host. We provide a Bacterial Foraging Optimization Algorithms (BFOA) for optimizing cloud servers, which is based on thresholds, in this study. For cloud service cost reduction, BFOA decreases the number of host systems that need to be switched on, in contrast to conventional server optimization algorithms that just take CPU, RAM, and BW use into account when scheduling resources. Through efficient use of resources, our method may help the cloud sector reduce service costs.

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Client Side-Server Side Load Balancing with Grasshopper optimization Mapreduce Enhancing Accuracy in Cloud Environment

Cited by 3 publications

References 8 publications

A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Optimizing load balancing for green cloud via efficient scheduling

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