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Performance modelling and comparisons of global shared buffer management policies in a cluster environment

机译:集群环境中全局共享缓冲区管理策略的性能建模和比较

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Clustering multiple computing nodes has become increasingly popular for reasons of capacity, availability and cost. One approach to clustering is the data sharing approach where a number of loosely coupled nodes share a common database. In this environment, a global shared buffer can be introduced to alleviate the multisystem invalidation effect either as a disk cache or shared intermediate memory. We develop an analytic model to evaluate different shared buffer management policies (SBMPs) which differ in their choice of data granules to be put into the shared buffer. The methodology analyzes all policies using a uniform framework by decomposing the input stream to the shared buffer into multiple (three) component streams based on their effects on the dependency between the private and shared buffer contents. This approach simplifies the problem of analyzing different SBMPs into 1) estimating the rate of each component stream, and 2) evaluating the impact of dependency on each type of component stream and hence the shared buffer hit probability. A detailed simulation model is also developed to validate the analytic model. We also illustrate how the analytic buffer model can be integrated with other system submodels to examine trade-offs between the SBMPs and to estimate optimal shared buffer allocations from a cost-performance point of view.
机译:由于容量,可用性和成本的原因,群集化多个计算节点已变得越来越流行。群集的一种方法是数据共享方法,其中许多松散耦合的节点共享一个公共数据库。在这种环境下,可以引入全局共享缓冲区来减轻多系统失效的影响,无论是磁盘高速缓存还是共享中间存储器。我们开发了一种分析模型来评估不同的共享缓冲区管理策略(SBMP),这些策略在选择要放入共享缓冲区的数据粒度方面有所不同。该方法根据统一策略对私有缓冲区和共享缓冲区内容之间的依赖关系的影响,通过将共享缓冲区的输入流分解为多个(三个)组件流,使用统一的框架分析所有策略。这种方法将分析不同的SBMP的问题简化为:1)估计每个分量流的速率,以及2)评估对每种类型的分量流的依存关系以及共享缓冲区命中率的影响。还开发了详细的仿真模型来验证分析模型。我们还说明了如何将分析缓冲区模型与其他系统子模型集成在一起,以检查SBMP之间的权衡并从性价比角度评估最佳共享缓冲区分配。

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