基于局部择优随机爬坡算法的云计算负载策略研究
Load Balancing in Cloud Computing Using Stochastic Hill Climbing-A Approach
李霞 1彭浩1
作者信息
- 1. 郑州轻工业学院计算机与通信工程学院，郑州 450001
- 折叠
摘要
以云计算的方式来执行任务的过程中负载节点的选择是非常关键的环节，从挖掘资源有效性的角度出发，负载节点必须根据任务属性来合理选择，提出一种负载均衡算法———局部择优的随机爬坡算法，该算法用来为虚拟机或服务器分配即将运行的调度作业，用云分析软件对算法的性能进行定量和定性的分析。将局部择优随机爬坡算法与轮训调度算法和先进先服务算法进行对比分析来反映局部择优随机爬坡算法在选择负载节点优化配置计算资源的优越性。
Abstract
Utilizes the computing resources on the network to facilitate the execution of complicated tasks that requires large-scale computation. Se-lects nodes (load balancing) is crucial for executing a task in the cloud computing, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. Proposes a soft computing based load balancing approach, uses a lo-cal optimization approach Stochastic Hill climbing for allocation of incoming jobs to the servers or virtual machines (VMs). Analyzes per-formance of the algorithm both qualitatively and quantitatively using CloudAnalyst. Makes a comparison with Round Robin and First Come First Serve (FCFS) algorithms. The comparison reflect the advantage of local optimization approach Stochastic Hill climbing in se-lecting load balance node.
关键词
云计算/负载均衡/软计算/随机爬坡算法/云分析Key words
Cloud Computing/Loding Balance/Soft Computing/Stochastic Hill Climbing/CloudAnalyst引用本文复制引用
基金项目
河南省科技攻关项目(豫财政【2014】124号)
出版年
2015
NETL
NSTL
万方数据