Effective Techniques and Tools for Resource Management in Clustered Web Servers

万维网的爆炸性增长、给定站点承载的信息的多样性、用户需求的快速波动以及首选架构趋势的复杂性，都是系统管理员有效管理当今复杂Web服务器系统的日益严峻的挑战。本提案通过寻求开发智能算法来解决这个问题，通过持续监控和适应工作负载和系统的变化，帮助执行诸如web服务器集群配置、容量规划、故障管理和负载平衡等困难任务。工作负载监控将为分析模型提供输入，分析模型的解决方案将被离线和在线算法所使用，这些算法可以提出改进的系统配置。为此目的，拟议的研究目标将包括：(a)获得web服务器的详细统计工作负载特征的方法，(b)从重尾概率分布中提取任务的系统分析模型的新的有效解决方案，(c)专门针对集群web服务器的负载平衡的软件工具，供包括系统管理员在内的非专家建模者使用，以及(d)工作负载感知和系统感知算法，可显着提高性能和易于web系统管理。

The explosive growth of the World Wide Web, the variety of information hosted by a given site, the rapid fluctuations in user demand, and the complexity of the preferred architectural trend are among the reasons that make increasingly challenging for system administrators to effectively mange today's complex web server systems. This proposal addresses this problem by seeking to develop smart algorithms that help performing difficult tasks such as web server cluster configuration, capacity planning, fault management and load balancing, by continuously monitoring and adapting to changes in the workload and system. Workload mointoring will provide input to analytic models whose solution will in turn be used by both off-line and on-line algorithms that can propose improved system configurations. To this end, the proposed research objectives will provide: (a) methods to derive detailed statistical workload characterizations of web servers, (b) new and efficient solutions for analytic models of systems that serve tasks drawn from heavy tail probability distributions, (c) a software tool particularly targeted to load balancing in clustered web servers, to be used by non expert modelers, including system administrators, and (d) workload-aware and system-aware algorithms that significantly improve performance and ease of web system management.