云环境下面向数据密集型应用的容错性资源配置方法

Fault-tolerant resource provisioning for data-intensive applications in cloud environment

为了在云计算平台发生宕机时进行有效的资源配置,提出一种面向数据密集型应用的容错资源配置方法(FRPM)。首先,将数据密集型应用建模为工作流模型,并且基于虚拟层2(VL2)网络拓扑结构建立了宕机任务时间恢复时间模型和负载均衡模型;然后,利用非支配排序遗传算法(NSGA-Ⅲ)实现对数据密集型应用的完成时间和云平台所有计算节点负载均衡进行联合优化,从而为部署在宕机节点上应用找到有效的资源配置策略;最后,根据简单加权(SAW)法和多层次决策准则(MCDM)选择出最优的资源配置策略。实验结果表明,FRPM方法能够在云平台在发生宕机时保证数据密集型应用的完成时间最短,与此同时也能保证云平台所有计算节点的负载均衡。

To address the problem of resource provisioning when the computing node of the cloud fails,a Fault-tolerant Resource Provisioning Method(FRPM)for data-intensive applications was proposed in this paper. Initially,the dataintensive application is modeled as a workflow model,and the faulty task recovery time model and the load balance model were established based on the Virtual Layer 2(VL2)network topology. Subsequently,the Non-dominated Sorting Genetic Algorithm(NSGA-Ⅲ)was adopted to jointly optimize the makespan of data-intensive applications and the load balance of all computing nodes in the cloud to find effective resource provisioning strategies. Finally,the optimal resource provisioning strategy was obtained based on Simple Additive Weighting(SAW)and the Multiple Criteria Decision Making(MCDM). The experimental results show that FRPM ensures the shortest makespan of data-intensive applications when the cloud platform fails,and at the same time,it can ensure the load balance of all computing nodes of the cloud.