云计算环境下的资源负载均衡算法研究
摘 要
随着云计算技术的迅猛发展，云环境中资源负载不均问题日益凸显，严重影响了服务质量与用户体验。为此，本研究聚焦于云计算环境下的资源负载均衡算法，旨在通过优化资源分配策略提高系统整体性能。研究基于现有负载均衡理论，提出了一种融合多维评价体系的自适应资源调度算法，该算法综合考虑计算资源、网络带宽及存储空间等多方面因素，采用动态权重调整机制实现对不同任务需求的精准匹配。通过引入机器学习模型预测未来负载趋势，提前进行资源预分配，有效避免了传统算法中因突发流量导致的过载现象。实验结果表明，所提算法在响应时间、吞吐量以及资源利用率等方面较现有方法均有显著提升，特别是在高并发场景下优势明显。
关键词：资源负载均衡 自适应资源调度 多维评价体系
Abstract
With the rapid development of cloud computing technology, the problem of uneven resource load in the cloud environment has become increasingly prominent, which seriously affects the service quality and user experience. To this end, this study focuses on the resource load balancing algorithm in the cloud computing environment, aiming to improve the overall performance of the system by optimizing the resource allocation strategy. Based on the existing load balancing theory, an adaptive resource scheduling algorithm integrating multi-dimensional evaluation system is proposed. The algorithm comprehensively considers various factors such as computing resources, network bandwidth and storage space, and adopts dynamic weight adjustment mechanism to realize accurate matching of different task requirements. By introducing the machine learning model to predict the future load trend, we can pre-allocate resources in advance, and effectively avoid the overload phenomenon caused by sudden traffic in the traditional algorithm. The experimental results show that the proposed algorithm is significantly improved than the existing methods in terms of response time, throughput and resource utilization, especially in high concurrency scenarios.
Keyword: Resource load balancing adaptive resource scheduling multi-dimensional evaluation system
目 录
1引言 1
2云计算资源负载均衡概述 1
2.1 云计算环境特点分析 1
2.2 资源负载均衡概念界定 2
3负载均衡算法分类研究 2
3.1 静态负载均衡算法 2
3.2 动态负载均衡算法 3
3.3 混合型负载均衡算法 3
4关键技术与实现机制 4
4.1 资源监控与预测方法 4
4.2 负载分配决策模型 4
4.3 算法性能评估指标 5
5实验设计与结果分析 6
5.1 实验环境搭建过程 6
5.2 算法对比实验方案 6
5.3 实验结果与讨论分析 7
6结论 7
参考文献 9
致谢 10
摘 要
随着云计算技术的迅猛发展，云环境中资源负载不均问题日益凸显，严重影响了服务质量与用户体验。为此，本研究聚焦于云计算环境下的资源负载均衡算法，旨在通过优化资源分配策略提高系统整体性能。研究基于现有负载均衡理论，提出了一种融合多维评价体系的自适应资源调度算法，该算法综合考虑计算资源、网络带宽及存储空间等多方面因素，采用动态权重调整机制实现对不同任务需求的精准匹配。通过引入机器学习模型预测未来负载趋势，提前进行资源预分配，有效避免了传统算法中因突发流量导致的过载现象。实验结果表明，所提算法在响应时间、吞吐量以及资源利用率等方面较现有方法均有显著提升，特别是在高并发场景下优势明显。
关键词：资源负载均衡 自适应资源调度 多维评价体系
Abstract
With the rapid development of cloud computing technology, the problem of uneven resource load in the cloud environment has become increasingly prominent, which seriously affects the service quality and user experience. To this end, this study focuses on the resource load balancing algorithm in the cloud computing environment, aiming to improve the overall performance of the system by optimizing the resource allocation strategy. Based on the existing load balancing theory, an adaptive resource scheduling algorithm integrating multi-dimensional evaluation system is proposed. The algorithm comprehensively considers various factors such as computing resources, network bandwidth and storage space, and adopts dynamic weight adjustment mechanism to realize accurate matching of different task requirements. By introducing the machine learning model to predict the future load trend, we can pre-allocate resources in advance, and effectively avoid the overload phenomenon caused by sudden traffic in the traditional algorithm. The experimental results show that the proposed algorithm is significantly improved than the existing methods in terms of response time, throughput and resource utilization, especially in high concurrency scenarios.
Keyword: Resource load balancing adaptive resource scheduling multi-dimensional evaluation system
目 录
1引言 1
2云计算资源负载均衡概述 1
2.1 云计算环境特点分析 1
2.2 资源负载均衡概念界定 2
3负载均衡算法分类研究 2
3.1 静态负载均衡算法 2
3.2 动态负载均衡算法 3
3.3 混合型负载均衡算法 3
4关键技术与实现机制 4
4.1 资源监控与预测方法 4
4.2 负载分配决策模型 4
4.3 算法性能评估指标 5
5实验设计与结果分析 6
5.1 实验环境搭建过程 6
5.2 算法对比实验方案 6
5.3 实验结果与讨论分析 7
6结论 7
参考文献 9
致谢 10
