为了改善数据重均衡的效果及减小数据迁移对系统性能的影响,提出一种上下文感知的数据重均衡方法。构建迁移时间预测模型,以刻画虚拟机环境上下文对数据迁移的影响,据此提出基于细粒度资源监测的上下文感知的数据重均衡算法CADR。实验结果表明,该迁移时间预测模型具有较低的错误率;CADR算法与传统数据重均衡算法相比,能够提供更好的均衡效果及更短的迁移时间。国家973重点基础研究发展计划基金项目(2009CB320704)|国家自然科学基金项目(61173003)|国家科技重大专项“核高基”基金项目(2011ZX03002-002-01)In order to improve the effect of data rebalancing and reduce the impact of data migration, a context-aware data reba-lancing approach is proposed. First, a predictive model of migration time is presented to depict the impact of virtualization context on data migration. Then a context-aware data rebalancing algorithm (CADR) is provided based on fine-grained resource monitoring. The experiments show that our prediction model of migration time has a low error rate, and CADR can improve perfor-mance compared with a typical data rebalancing algorithm
