引用本文:范国闯,朱寰,黄涛,冯玉琳.Web应用服务器自适应负载平衡服务.软件学报,2003,14(6):1134-1141
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Web应用服务器自适应负载平衡服务
范国闯1, 朱寰1, 黄涛1, 冯玉琳1
中国科学院,软件研究所,软件工程技术研究开发中心,北京,100080
摘要:
Web应用服务器是为事务性Web应用提供一系列运行时服务的分布式系统.基于中间件的自适应负载平衡服务是为Web应用服务器提供高可信赖性和高伸缩性的一种有效方法,但目前还存在许多不足,如缺乏服务端透明性、负载策略不可替换等,不能满足Web应用服务器特有的需求.分析了Web应用服务器负载平衡服务的关键需要,设计了一种自适应负载平衡服务,阐述了在J2EE应用服务器WebFrame2.0上实现该服务的若干关键技术及其解决办法,包括可热插拔、负载策略可替换、负载反馈与自适应控制、状态迁移以及容错技术等,最后是相关工作介绍及其比较.该负载平衡服务已在Web应用服务器WebFrame2.0中得以实现.
关键词:  Web应用服务器  自适应负载平衡服务  中间件  可信赖性  可伸缩性
DOI:
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基金项目:Supported by the National High-Tech Research and Development Plan of China under Grant Nos.2001AA113010, 2001AA414020, 2001AA414310 (国家高技术研究发展计划(863)); the National Grand Fundamental Research 973 Program of China under Grant No.2002CB312005 (国家重点基础研究发展计划(973))
Towards Adaptive Load Balancing Services for Web Application Servers
FAN Guo-Chuang,ZHU Huan,HUANG Tao,FENG Yu-Lin
Abstract:
WAS (Web application server) is a distributed system which provides runtime integrated services for transactional Web applications. To improve the dependability and scalability of WASs, one effective way is to balance loads via adaptive load balancing service based on the middleware. The existing adaptive load balancing services, however, have inadequacies such as lack of server transparency and extensible load balancing strategy etc., and provide insufficient functionality to satisfy WASs. In this paper, the key requirements of adaptive load balancing services for WASs are addressed first, and then the design of a novel adaptive load balancing service is presented. The key design challenges including hot plug-in, customizable load balancing strategy, load feedback and adaptive control, state migration and fault tolerance etc. are described, and the technical solutions are outlined. Lastly, a comparative study with other related works is given The adaptive load balancing service is developed and implemented in WebFrame2.0 application server by using Java RMI.
Key words:  Web application server  adaptive load balancing service  middleware  dependability  scalability