Self-adapting Service Level in Java Enterprise Edition
Self-adapting Service Level in Java Enterprise Edition
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DOI：
10.1007/978-3-642-10445-9_8
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发表时间：
2009-11
期刊：
影响因子：
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通讯作者：
J. Philippe;N. D. Palma;F. Boyer;O. Gruber
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文献类型：
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作者：
J. Philippe;N. D. Palma;F. Boyer;O. Gruber
应用程序服务器会受到不同工作负载的影响，这表明需要进行自主管理以保持最佳性能。我们建议在当前应用服务器中经常使用的基于组件的编程模型中集成服务水平自适应的概念，允许某些组件动态地降低或升级其服务水平。我们的目标是能够在繁重的工作负载下，以较低的资源密集型组件服务水平换取服务器整体的稳定性能。组件的升级或降级是通过运行时分析自动执行的，运行时分析用于估计应用程序的热点和目标适配。除了找到最佳的适应，这个性能配置文件允许我们的系统来表征过去的适应的影响，特别是考虑到当前的工作负载，它是可能的估计，如果服务水平升级可能会导致过载。因此，通过组件适配将服务器稳定在峰值性能，我们能够大幅改善整体延迟和吞吐量。例如，在RUBiS和TPC-W基准测试中，我们能够在重负载情况下保持峰值性能，远远超过系统的初始容量。
Application servers are subject to varying workloads, which suggests an autonomic management to maintain optimal performance. We propose to integrate in the component-based programming model often used in current application servers the concept ofservice level adaptation, allowing some components to dynamically degrade or upgrade their level of service. Our goal is to be able, under heavy workloads, to trade a lower service level of the most resource-intensive components for a stable performance of the server as a whole. Upgrading or degrading components is autonomously performed through runtime profiling, which is used to estimate the application’s hot spots and target adaptations. In addition to finding the best adaptations, this performance profile allows our system to characterize the effects of past adaptations; in particular given the current workload, it is possible to estimate if a service level upgrade might result in an overload. As a result, by stabilizing the server at peak performance via component adaptations, we are able to drastically improve both overall latency and throughput. For instance, on both the RUBiS and TPC-W benchmarks, we are able to maintain peak performance in heavy load scenarios, far exceeding the initial capacity of the system.