网格性能预测:需求、框架和模型

F. Nadeem, M. Yousaf, M. Ali
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引用次数: 5

摘要

网格性能预测(GPP)对于网格中间件组件服务和网格(中间件)用户做出优化的网格资源使用决策以满足sla中提交的QoS需求至关重要。网格中的其他复杂概念,如多标准调度、网格容量规划和网格提前预约,也依赖于GPP。GPP在多个维度上覆盖所有级别的网格资源。我们在不同的粗粒度水平上进行了探索,以发现不同网格和/或网格服务的人类和服务客户最需要的网格性能预测需求,并提出了一个全面的网格性能预测框架,以支持细粒度水平。我们还为我们的网格中间件提供了广泛的ALP(应用程序级预测)和NLP(网络级预测)架构
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Grid Performance Prediction: Requirements, Framework, and Models
Grid performance prediction (GPP) is critical for grid middleware component services and grid (middleware) users to make optimized grid resource usage decisions to meet QoS requirements committed in SLAs. Other sophisticated concepts in grid like multi-criteria scheduling, grid capacity planning, and grid advance reservation are also dependant on GPP. GPP spreads over all levels of grid resources in multiple dimensions. We explore it at different coarse grain levels to discover the most wanted grid performance prediction needs of different human and service clients of grid and/or grid services, and present a comprehensive grid performance prediction framework to support at fine grain levels. We also provide a broad architecture of ALP (application level prediction) and NLP (network level prediction) for our grid middleware
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