虚拟计算环境下基于聚类的资源匹配优化模型

Resource Matching Optimization Model Based on Clustering under VCE

虚拟计算环境中任务具有数量庞大、需求模糊、种类多样等特征,使得资源匹配面临巨大挑战.依据虚拟计算实验床平台公布数据,提出了一种融合虚拟资源与任务聚类的资源匹配优化模型.该模型通过分析任务需求、消耗等特征,基于改进二分K均值进行任务聚类,并结合虚拟资源类型生成优化的资源匹配列表.经实验分析验证,该模型有效缩小资源匹配范围,提高任务运行成功率,为精准匹配提供基础.

Under virtual computing environment( VCE),tasks have features of large quantity,ambiguous requirements,and various types. This makes resource matching face enormous challenges. According to the data published by VCE platform,a resource matching optimization model combined with resource and task clustering was proposed. By analyzing task requirements and consumption characteristics,the model clustered tasks based improved bised K-means,and combined with the virtual resource types to generate optimized matching resource list. The experimental analysis verified that the model effectively reduced resource matching range and improved the successful rate of tasks to provide the foundation for precision matching.