云计算下的自适应资源配置方法研究

Research on Adaptive Resource Allocation Method in Cloud Computing

随着国家电网规模不断扩大和电网复杂性日益增加,云数据中心大规模应用云计算、虚拟化、微服务等技术,其运维工作面临诸多新挑战。尽管云用户可以访问大量的资源,但云计算环境中大多低效的资源配置方法导致了不合理的成本和资源的浪费。为了解决这一问题,本研究基于云计算环境下的系统运行状态,提出了一种可靠的性能感知云弹性框架(PACE)进行自适应资源配置。该框架围绕状态检测、资源配置和系统优化三项关键服务,旨在实现可靠的决策制定、高效的工作负载执行以及最佳配置的识别,从而最大程度地提升应用程序性能和资源利用效率。最后,利用行业标准应用程序和最先进的基准来证明了所提出框架的有效性。

With the continuous expansion of the national power grid and the increasing complexity of the power grid,cloud data centers are widely adopting technologies such as cloud computing,virtualization,and microservices.However,their operational tasks are facing numerous new challenges.Despite the abundant resources accessible to cloud users,inefficient resource allocation methods in cloud computing environments lead to unreasonable costs and wastage of resources.To address this issue,this paper proposes a reliable Performance-Aware Cloud Elasticity(PACE)framework for adaptive resource allocation based on the operational status of systems in cloud computing environments.This framework revolves around three key services:state detection,resource configuration,and system optimization.Its objective is to achieve reliable decision-making,efficient workload execution,and optimal configuration recognition,thus maximizing application performance and resource utilization efficiency.Finally,the effectiveness of the proposed framework is demonstrated using industry-standard applications and state-of-the-art benchmarks.