Tuning adaptive computations for the performance improvement of applications in JEE server

Tuning adaptive computations for the performance improvement of applications in JEE server
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调整自适应计算以提高 JEE 服务器中应用程序的性能

DOI:
10.1007/s13174-012-0060-4
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发表时间:
2012-05
影响因子:
3.5
通讯作者:
Hong Mei
Hong Mei
中科院分区:
--
文献类型:
--
作者:
Ying Zhang;Gang Huang;Xuanzhe Liu;Hong Mei

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随着自主计算技术的使用越来越多,Java企业版(JEE)应用服务器被实现了越来越多的自适应计算,以自我管理中间件及其托管的应用程序。然而,这些自适应计算会消耗CPU和内存等资源,并且会在运行时由于资源竞争而干扰应用程序的正常业务处理,特别是在整个系统负载较重的情况下。从资源管理的角度调整这些自适应计算是必要的。在本文中,我们提出了一种用于自适应计算的调优模型。在该模型的基础上,通过升级或降低自适应计算的自主级别来动态地进行调整,以控制其资源消耗。我们实现了RSpring调谐器,并使用它来优化自主JEE服务器,如PkuAS和Jonas。RSpring在ECperf和Rubis基准应用程序上进行了评估。结果表明,在相同的资源量下,PkuAS能有效提升13.6%的应用性能,Jonas能有效提升19.2%的应用性能。
With the increasing use of autonomic computing technologies, a Java Enterprise Edition (JEE) application server is implemented with more and more adaptive computations for self-managing the Middleware as well as its hosted applications. However, these adaptive computations consume resources such as CPU and memory, and can interfere with the normal business processing of applications at runtime due to resource competition, especially when the whole system is under heavy load. Tuning these adaptive computations from the perspective of resource management becomes necessary. In this article, we propose a tuning model for adaptive computations. Based on the model, tuning is carried out dynamically by upgrading or degrading the autonomic level of an adaptive computation so as to control its resource consumption. We implement the RSpring tuner and use it to optimize autonomic JEE servers such as PkuAS and JOnAS. RSpring is evaluated on ECperf and RUBiS benchmark applications. The results show that it can effectively improve the application performance by 13.6 % in PkuAS and 19.2 % in JOnAS with the same amount of resources.
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