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Introduction de fonctionnalités d'auto-optimisation dans une architecture de selfbenchmarking

机译:defonctionnalitésd'auto-optimization dans une architecture de selfbenchmarking

摘要

Benchmarking client-server systems involves complex, distributed technical infrastructures, whose management deserves an autonomic approach. It also relies on observation, analysis and feedback steps that closely matches the autonomic control loop principle. While previous works in performance testing have shown how to introduce autonomic load testing features through self-regulated load injection, the goal of this thesis is to follow this approach of autonomic computing to introduce self-optimization features in this architecture to obtain reliable and comparable benchmark results, and to achieve the fully principle of Self-benchmarking.Our contribution is twofold. From the algorithmic point of view, we propose an original optimization algorithm in the context of performance testing. This algorithm is divided into two parts. The first one concerns the overall level, i.e. the control of the performance index evolution, based on global parameters setting of the system. The second part concerns the search for the optimum when only one parameter is modified. From the software architecture point of view, we complete the Fractal component-based architecture, containing several autonomic control loops (saturation, injection, optimization computing) and we implement the coordination principle between these loops by asynchronous messages according to the publish-subscribe communication paradigm. To apply a given parameters setting on the system under test, we introduced new components Configurators to support the setting of parameters before starting the test process. It may also be necessary to restart all or part of the system to optimize to ensure that the new setting is effectively taken into account. We introduced components Starters to cover this need in a specific way for each system.To validate our self-optimization framework, two types of campaigns have been conducted onto the servers of Orange Labs in Meylan and the servers of the LISTIC Laboratory of the University of Savoie in Polytech Annecy-Chambéry (Annecy le Vieux). The first one is a WEB online shopping application deployed on a Java EE application server JonAS. The second one is a three-tiers application (WEB, business (EJB JOnAS) and data base) clusterSample. The three tiers are in three separate machines.
机译:对客户/服务器系统进行基准测试涉及复杂的分布式技术基础架构,其管理应采用自动方法。它还依赖于观察,分析和反馈步骤,这些步骤与自主控制回路原理非常匹配。尽管以前的性能测试工作已经展示了如何通过自调节负载注入来引入自主负载测试功能,但本文的目标是遵循这种自主计算方法,在此体系结构中引入自优化功能,以获得可靠且可比的基准结果,并实现自我基准测试的全部原则。我们的贡献是双重的。从算法的角度来看,我们在性能测试的背景下提出了一种原始的优化算法。该算法分为两部分。第一个涉及总体水平,即基于系统的全局参数设置来控制性能指标的演变。第二部分涉及仅修改一个参数时的最佳搜索。从软件体系结构的角度来看,我们完成了基于分形组件的体系结构,其中包含多个自主控制循环(饱和,注入,优化计算),并根据发布-订阅通信范例通过异步消息在这些循环之间实现了协调原理。 。为了在测试的系统上应用给定的参数设置,我们引入了新的组件配置器以支持在开始测试过程之前进行参数设置。可能还需要重新启动系统的全部或部分以进行优化,以确保有效考虑新设置。我们引入了组件启动器以针对每个系统的特定方式满足此需求。为了验证我们的自我优化框架,已经在梅兰的Orange Labs的服务器和密西根大学LISTIC实验室的服务器上进行了两种类型的活动。 Savoie在PolytechAnnecy-Chambéry(Annecy le Vieux)。第一个是部署在Java EE应用程序服务器JonAS上的WEB在线购物应用程序。第二个是三层应用程序(WEB,业务(EJB JOnAS)和数据库)clusterSample。这三个层位于三个单独的计算机中。

著录项

  • 作者

    Bendahmane El Hachemi;

  • 作者单位
  • 年度 2012
  • 总页数
  • 原文格式 PDF
  • 正文语种 fr
  • 中图分类

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