自适应组件副本选择模型及算法

提出了一个基于域的自适应副本选择模型DARSM(domain based adaptive replica selection model).该模型将组件副本划分为强一致性域和弱一致性域,域间通过一致性窗口机制进行状态同步.基于DARSM模型,给出了一种基于分区加权的自适应副本选择算法PWARS(partition-weighted based adaptive replica selection,).该算法利用动态性能度量信息来选择满足时间约束和一致性约束的组件副本集合.为了适应请求一致性约束的动态变化,还提出了一致性窗口自适应重配算法CWAR(consistency window adaptive reconfiguration).通过引入的一个一致性约束的可能性模型,该算法动态地对一致性窗口进行重配,从而实现了副本一致性的自适应控制.通过在OnceAS应用服务器集群中的原型实验及性能评价,表明该方法能够明显地提高副本选择的性能.

Adaptive Component Replica Selection Model and Algorithms

This paper proposes a domain based adaptive replica selecting model named DARSM (domain based adaptive replica selection model),in which component replicas are organized into strong consistency domain and weak consistency domain,and a schema based on consistency window is utilized to synchronize states between theses domains.Accordingly,a partition-balanced based adaptive replica selection algorithm PWARS (partition- weighted based adaptive replica selection) could be built on dynamic performance metric information to select the appropriate replica set that satisfies specific QoS constraints.A consistency window adaptive reconfiguration algorithm CWAR (consistency window adaptive reconfiguration) is presented to adapt to the dynamic change of consistency constraints.In this algorithm,a probability model built on the base of current consistency constraints distribution is used to dynamically adjust the configuration of consistency window.As a result,the inconsistency of each replica is controlled adaptively.This approach has been implemented in OnceAS,and experimental results demonstrate that it can effectively enhance the performance of replica selection.