Managing Web server performance with AutoTune agents
Managing Web server performance with AutoTune agents
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DOI：
10.1147/sj.421.0136
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Y. Diao;J. Hellerstein;S. Parekh;Joseph P. Bigus
中科院分区：
文献类型：
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作者：
Y. Diao;J. Hellerstein;S. Parekh;Joseph P. Bigus
管理电子商务网站的性能具有挑战性。网站内容经常变化，客户兴趣和业务计划也是如此，这导致了动态变化的工作负载。为了保持良好的性能，系统管理员必须不断调整其信息技术环境。不幸的是，这样做需要大量的专业知识，并增加了系统拥有的总成本。在本文中，我们提出了一个基于代理的解决方案，不仅自动化正在进行的系统调整，但也自动设计一个适当的调整机制的目标系统。我们在管理Web服务器的上下文中说明了这一点。在那里，我们研究了使用服务器公开的应用程序级调优参数MaxClients和KeepAlive来控制Apache ® Web服务器的CPU和内存利用率的问题。使用AutoTune代理框架下的代理建设和学习环境（ABLE），我们构造代理完全自动化的控制理论方法，包括模型的建立，控制器的设计，和运行时的反馈控制。具体来说，我们设计（1）一个建模代理，建立一个动态系统模型，从受控的服务器运行数据，（2）控制器设计代理，使用最优控制理论，以获得定制的反馈控制算法，该服务器，和（3）运行时控制代理，部署在一个在线的真实的时间环境中的反馈控制算法，自动管理Web服务器。所设计的自主反馈控制系统能够处理动态和相互关联的1调谐参数和性能指标之间的依赖关系，从控制理论保证稳定性。通过涉及工作负载、服务器容量和业务目标变化的实验，证明了AutoTune代理的有效性。结果也作为一个ABLE工具包和AutoTune代理框架的验证。
Managing the performance of e-commerce sites is challenging. Site content changes frequently, as do customer interests and business plans, contributing to dynamically varying workloads. To maintain good performance, system administrators must tune their information technology environment on an ongoing basis. Unfortunately, doing so requires considerable expertise and increases the total cost of system ownership. In this paper, we propose an agent-based solution that not only automates the ongoing system tuning but also automatically designs an appropriate tuning mechanism for the target system. We illustrate this in the context of managing a Web server. There we study the problem of controlling CPU and memory utilization of an Apache® Web server using the application-level tuning parameters MaxClients and KeepAlive, which are exposed by the server. Using the AutoTune agent framework under the Agent Building and Learning Environment (ABLE), we construct agents to fully automate a control-theoretic methodology that involves model building, controller design, and run-time feedback control. Specifically, we design (1) a modeling agent that builds a dynamic system model from the controlled server run data, (2) a controller design agent that uses optimal control theory to derive a feedback control algorithm customized to that server, and (3) a run-time control agent that deploys the feedback control algorithm in an on-line real- time environment to automatically manage the Web server. The designed autonomic feedback control system is able to handle the dynamic and interrelated dependencies between the1 tuning parameters and the performance metrics with guaranteed stability from control theory. The effectiveness of the AutoTune agents is demonstrated through experiments involving variations in workload, server capacity, and business objectives. The results also serve as a validation of the ABLE toolkit and the AutoTune agent framework.