云环境中基于元启发式的微服务自动伸缩:一种新的容器感知应用程序调度

Subramonian Krishna Sarma
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引用次数: 3

摘要

云是一个服务器网络,用于共享计算资源,以运行应用程序和数据存储,提供各种形式的服务,即基础设施即服务、平台即服务和软件即服务。云中的容器被定义为“将软件及其依赖关系打包在一起的独立且自包含的单元”。与虚拟机类似,虚拟化方法简化了特定服务器上可供众多设备使用的资源。本研究引入了一种新的Dragon Levy更新的松鼠算法(DLU-SA),用于容器感知应用程序调度。进一步,通过定义考虑“网络总距离(TND)、系统故障(SF)、均衡集群使用(BC)和阈值距离(TD)”等标准的目标函数,得到资源最优分配的解。最后,本模型在成本和统计分析方面优于现有模型。结果表明,实验1所采用的模型总成本较低,在平均情况下分别比传统的速度更新灰狼(VU-GWO)、松鼠搜索算法(SSA)和蜻蜓算法(DA)模型高0.97%、10.45%和10.37%。特别是,在最佳情况下,所实现模型的成本值最小为761.95,而与之相比,鲸鱼随机更新辅助狮子算法、VU-GWO、SSA和DA模型的成本值更高,分别为761.98、779.46、766.62和766.51。因此,所开发的模型的增强已在现有工作上得到验证。提出了一种新的基于容器感知的应用程序调度方法。这是第一次使用DLU-SA模型通过考虑TND、SF、BC和TD等约束条件来优化容器资源分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling
Purpose The cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances. Design/methodology/approach This study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis. Findings On observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works. Originality/value This paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.
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