Optimal Container Resource Allocation Using Hybrid SA-MFO Algorithm in Cloud Architecture

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引用次数: 26

Abstract

: Owing to the merits of container practice such as easier and more rapid consumption, superior portability, and limited overheads, it can be extensively installed over the cloud architecture. Then, a suitable architecture solution is proposed to develop the applications, which are produced using the microservice expansion model. Thus far, numerous research works have determined on resolving the open problems in container management and automation. In reality, for cloud providers, container resource allocation is considered as the main knothole as it directly influences the system performance and resource utilization. In this way, this work initiates a novel optimized container resource allocation framework by developing a novel optimization theory. Here, a novel hybrid approach is proposed such as, SA and MFO that is the hybridization of Simulated Annealing (SA) and Moth Flame Optimization Algorithm (MFOA) to create the prospect of optimal container resource allocation. In addition, the solution of optimized resource allocation is inclined with the modeling of a novel objective model which contemplates system failure, threshold distance, total network distance, and balanced cluster use, correspondingly. At last, the performance of the proposed approach is evaluated over other existing approaches and exhibits the performance of the proposed model.
云架构下基于混合SA-MFO算法的容器资源优化分配
由于容器实践的优点,例如更容易和更快速的使用、优越的可移植性和有限的开销,它可以广泛地安装在云架构上。然后,提出了一种合适的体系结构方案来开发应用程序,并使用微服务扩展模型生成了应用程序。迄今为止,大量的研究工作都致力于解决集装箱管理和自动化中的开放性问题。实际上,对于云提供商来说,容器资源分配被认为是一个主要的问题,因为它直接影响到系统的性能和资源利用率。通过这种方式,本工作通过发展一种新的优化理论,启动了一种新的优化容器资源分配框架。本文将模拟退火算法(SA)和蛾焰优化算法(MFOA)相结合,提出了一种新的混合方法,即SA和MFO,为集装箱资源的最优分配创造了前景。此外,优化资源分配的解决方案倾向于建立一个新的目标模型,该模型相应考虑了系统故障、阈值距离、总网络距离和均衡集群使用。最后,对所提方法的性能进行了比较,并展示了所提模型的性能。
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