Load balancing mechanism in the cloud environment using preference alignments and an optimisation algorithm

Li, Daming; Su, Qinglang; Deng, Lizhen; Cai, Kaiming; Cai, Zhiming; Mohammed, Bayan Omar

doi:10.1049/iet-com.2019.0800

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In Meeting the demands of cloud users and providers, balancing of load and scheduling of tasks are two major problems in the management of cloud resources (Kumar et al, 2018;Ruan et al, 2019). A load balancer allocates tasks to various machines in such a way that job execution times are reduced and virtual machine performance is monitored (Daming et al, 2020;Polepally et al,2019). Load balancing's ultimate objective is to lower the highest execution time (maximum Makespan time) whereas raising cloud resource utilization (Kumar & Sharma, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud

Kassanuk

Phasinam

2023

International Journal of Cloud Applications and Computing

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The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud

Kassanuk

Phasinam

2023

International Journal of Cloud Applications and Computing

View full text Add to dashboard Cite

show abstract

“…It can distribute modules or tasks to resources and increase the stability and reliability of the system [7]. The quality‐of‐service (QoS) factors such as process time, scalability, throughput, the durability of the system, and energy consumption are improved by the load balancing [8]. Therefore, in this study, the Bayesian classification [9] algorithm as a machine learning method is presented to optimise load balancing in FC.…”

Section: Introductionmentioning

confidence: 99%