基于分层排队网络模型的自主DBMS负载自适应技术摘要随着计算机技术的不断发展和应用的普及，随着网络和分布式系统的增长及改变，计算机软件系统结构和计算机组织结构的复杂性不断增加。
计算机工业花费数10年的时间不断建造越来越复杂的计算机系统以期解决各种各样越来越复杂的商业问题。
然而，计算机系统的复杂性本身已经成为企业急需解决的问题。
管理人员的缺乏成为制约大规模应用技术发展的主要问题，软件复杂性危机严重阻碍了IT产业的向前发展。
在这种背景下，IBM公司在2001年提出了自主计算的概念，期望解决这一问题。
现代网络计算在从手工管理向自主计算演化的过程中，作为网络计算核心的数据库管理系统（DBMS）必须能够以自主计算的方式参与到满足网络应用性能要求的过程之中。
数据库管理系统的负载自适应是一个性能管理过程，在这一过程中自主计算数据库管理系统自动地控制和过滤负载、并有效地使用数据库系统资源来满足网络应用的性能要求。
本文将针对负载自适应技术开展研究。
在负载自适应框架中，性能预测部件是制定负载控制方案的基础，是该项目的核心所在。
这里我们应用分层排队网络建模技术建立DBMS的性能模型。
依据该框架初步搭建了一个测试平台并做出了测试，测试结果表明该框架起到了负载控制的作用，并有效的提高了用户满意率和网络应用性能的作用。
本文主要研究如何实现该性能预测部件。
描述了一种基于分层排队网络模型的分布式系统性能预测方法，提出了一种分析分层排队网络模型的方法——集合点平均值分析（Rendezvous Mean Value Analysis，RMVA）方法来计算平均响应时间、平均吞吐量等性能参数。
并以DBMS为例，建立了DBMS的分层排队网络模型，最后通过模拟实验验证了该模型及算法的合理性及有效性。
关键词：自主计算，负载自适应，分层排队网络，性能评价，平均值分析WORKLOAD ADAPTATION IN AUTONOMICDBMSs BASED ON LAYERED QUEUING NETMODELABSTRACTWith the constant development and the popularity application of computer technology, with the growth and change of Network and Distributed System, the complexity of computer software and computer system architecture are increasing. Computer industry spends several 10 years to build more and more complex computer systems with a view to resolving many kinds of complex business questions. However, the complexity of the computer system itself has become an urgent need to solve. Lack of managerial personnel has become the main constraint problem of the development of a large-scale application technology, the software complexity crisis has seriously hindered the development of IT industry. In a university setting, IBM in 2001 put forward the concept of Autonomic Computing, hoping to resolve this problem. During the evolution process of modern network computing from manual management to autonomic computing, as the core of network computing, the DataBase Management System (DBMS) must be able to participate in the process of meeting the network applications performance requirements. Workload Adaptation of DBMS is a performance management process, in the processAutonomic Computing Database Management System automatically controls and filters workload, and effectively makes use of database system resources to meet the performance requirements of network applications.This paper studies on workload adaptation technology. In the Framework for Workload Adaptation, the performance forecast components is base of developing programmer of workload control. Here we use layered queuing net modeling techniques to establish the performance model of DBMS. Sets up a test platform based on the architecture and been tested, Test result shows that the framework has played a role in the workload control, and improved the user satisfaction rate and the application performance of the network effectively. This paper studies how to achieve the performance prediction component. This paper discusses a model based on layered queuing net of distributed systems performance prediction method, and proposes an method to analyze LQN model—Rendezvous Mean Value Analysis（RMVA) to calculate the average response time, the average throughput and other performance parameters. Taking DBMS as example, it establishes the layered queuing net model of DBMS, finally verifies the rationality and validity of the model algorithm.KEY WORDS: autonomic computing, workload adaptation, layered queuing net, performance evaluation, mean value analysis第一章绪论随着计算机技术的不断发展和应用的普及，随着网络和分布式系统的增长及改变，计算机软件系统结构和计算机组织结构的复杂性不断增加。
