Implementation and Research of Load Balancing Service on Cloud Computing Platform in IPv6 Network Environment

Chen, Zhidong; Zhang, Hailong; Yan, Jinyao; Zhang, Yuan

doi:10.1145/3291842.3291871

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…In the bid to improve existing work on dynamic loadbalancing for cloud applications, authors [8,9,18,20,21] identified specific server metrics that commonly affect the performance of both cloud applications and the servers they are running on. These identified metrics included the CPU, RAM, bandwidth utilisation, and network buffer, among others.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, evaluation of the above researches including these [14,15,27] were all done using simulation tools and environment. Also, there is little research [18] on evaluating cloud computing models by completely using real cloud infrastructures. In general, a simplified real infrastructure experiment is performed to complement the simulated experiments as recorded in [8,16,17].…”

Section: Related Workmentioning

confidence: 99%

“…The proposed VM weighting technique for our novel load balancing algorithm is an improvement on the research work by [9] and [18]. The weighting technique combined four server metrics to calculate the weight of a VM and an additional server metric as a key determining factor in the loadbalancing algorithm.…”

Section: Proposed Weighting Techniquementioning

confidence: 99%

“…Most studies conducted on the evaluation and development of load balancing techniques have focused on the use of simulation tools to evaluate these cloud models [8,10,[12][13][14][15]. This is in contrast to the few evaluations done on real cloud infrastructure [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Weight-Assignment Load Balancing Algorithm for Cloud Applications

Adewojo

Bass

2023

SN COMPUT. SCI.

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Web applications commonly suffer from flash crowds and resource failure, resulting in performance degradation. Flash crowds are large, sudden, yet legitimate influxes of user requests that constitute a critical problem because of their potential economic damage. For cloud providers, resource estimation is challenging, while distributing workload and sustaining performance. To alleviate flash crowds and resource failure problems, we propose a novel weight assignment load balancing algorithm that combines five carefully selected server metrics to efficiently distribute the workload of three-tier web applications among virtual machines. We experimentally characterised, using a private cloud running OpenStack, the load distribution ability of our proposed novel algorithm, as well as a baseline algorithm and round-robin algorithm. We compared the performance of the three algorithms by simulating resource failures and flash crowds, while carefully measuring response times. Our experimental results show that our approach improves average response times by 12.5% when compared to the baseline algorithm and 22.3% when compared to the round-robin algorithm in the flash crowds’ situation. In addition, average response time was improved by 20.7% when compared to the baseline algorithm and 21.4% when compared to the round-robin algorithm in resource failure situations. These experiments show that our novel algorithm is more resilient to fluctuating loads and resource failures than baseline algorithms.

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