Mining Computing Configurations

大型计算系统，特别是IT（信息技术）系统，由网络连接的硬件服务器、操作系统和数据库、客户服务管理等服务软件组成的复杂配置，这些配置随着时间的推移而不断发展，必须不断进行监控和管理，以纠正故障，适应新的需求。 分析人员很难管理和跟踪这些变化，以便最大限度地提高这些系统的可靠性和性能。 这些配置越来越多地存储在专用数据库中，例如配置管理数据库（CMDB）。 本质上，CMDB记录了计算系统的当前和过去的体系结构。 它用于帮助分析师控制和维护系统。 这里提出的研究将使用数据挖掘技术来探索和监控类似CMDB的数据库，其长期目标是帮助分析师更好地运行这些系统。 分析师的工作之一是计划对系统的更改。 这种更改需要更新系统中的一组项。 重要的是，变更计划必须包括完成整个变更必须更新的所有项目。 如果一些应该更新的项目被无意中忽略，尝试的更改可能会失败，导致停机，销售损失，客户不满-甚至商业企业的失败。 拟议的研究将使用挖掘技术，根据CMDB中记录的过去的更改和活动模式提供建议和意见，以帮助分析师避免不完整更改等问题。 更一般地说，拟议的研究将搜索计算配置内的活动的历史模式，以建议决策支持的工具和信息，以帮助分析师。

Large scale computing systems, notably IT (Information Technology) systems, consist of elaborate configurations made up of hardware servers connected by networks, operating systems and databases, service software such as Customer Service Management and so on. These configurations evolve with time, and must be constantly monitored and managed to correct faults and to adapt to new demands. It is challenging for analysts to manage and track these changes in order to maximize the reliability and performance of these systems. Increasingly, these configurations are stored in special purpose databases such as Configuration Management Databases (CMDBs). In essence, the CMDB records the present and past architectures of the computing system. It is used to help the analysts control and maintain the system. The research proposed here will use data mining techniques to explore and monitor CMDB-like databases with the long-term goal of assisting analysts to do a better job of running these systems. One of the jobs of the analysts is to plan changes to the system. Such a change requires updating a set of items in the system. It is essential that the plan for the change must include all the items that must be updated to accomplish the whole change. If some items that should be updated are inadvertently overlooked, the attempted change may fail, causing outage, loss of sales, customer dissatisfaction --- even failure of the commercial enterprise. The proposed research will use mining techniques to provide suggestions and advice, based on past patterns of change and activity as recorded in the CMDB to help the analysts avoid problems such as incomplete changes. More generally, the proposed research will search for historic patterns of activity within computing configurations in order to suggests tools and information for decision support to aid analysts.