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Applications Monitoring for Self-Optimization in GridGain

机译:在格里格此的自我优化的应用监测

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摘要

Monitoring process offer a quantitative and qualitative measurement of performance by collecting information relevant to environment and applications. Monitoring allows the obtaining of valuable parameters about performance, resource usage and availability, the efficiency of scheduling and used algorithms and represents a mechanism for analyzing and adapting an application's behavior, particularly useful for optimization of complex applications. Self-* properties of different applications are the answer to the complexity and large scale of distributed systems. The purpose of this paper is to analyze the requirements and to build such a tool destined for computational grids using the Grid Gain middleware platform (an Enterprise middleware for Grids, dedicated both to researcher environments and to industry). The optimization process is very important for QoS assurance, so multi-criteria approach could be adopted. The self-* behavior consider bio-inspired techniques for optimization (genetic algorithms, immune algorithms, swarm intelligence).
机译:监测过程通过收集与环境和应用相关的信息提供了对性能的定量和定性测量。监控允许获得有关性能,资源使用和可用性的有价值的参数,调度和使用算法的效率,并表示用于分析和调整应用程序行为的机制,特别适用于优化复杂应用程序。不同应用的自我属性是分布式系统复杂性和大规模的答案。本文的目的是使用网格增益中间件平台(专用于研究人员环境和行业,为工商网格的企业中间件构建指向计算网格的要求和建立该工具。优化过程对于QoS保证非常重要,因此可以采用多标准方法。自我*的行为考虑生物启发技术用于优化(遗传算法,免疫算法,群体智能)。

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