Mining Computing Configurations

挖矿计算配置

• 批准号: 203897-2011
• 负责人: Holt, Richard
• 金额: $ 2.11万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570011
• 关键词: Mining; Computing; Configurations

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.

大规模计算系统，特别是IT（信息技术）系统，由通过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等复杂的配置组成。 这些配置随着时间的推移而发展，必须不断地进行监控和管理，以纠正故障并适应新的需求。 对于分析师来说，管理和跟踪这些变化以最大限度地提高这些系统的可靠性和性能是一项挑战。 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记录了计算系统现在和过去的架构。 它用于帮助分析人员控制和维护系统。 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，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规划系统变更。 此类更改需要更新系统中的一组项目。 至关重要的是，变更计划必须包括完成整个变更必须更新的所有项目。 如果无意中忽略了一些应该更新的项目，则尝试的更改可能会失败，从而导致停机、销售损失、客户不满意——甚至商业企业的失败。 拟议的研究将使用挖掘技术根据 CMDB 中记录的过去的变更和活动模式提供建议和意见，以帮助分析师避免诸如不完整变更之类的问题。 更一般地说，拟议的研究将搜索计算配置内的历史活动模式，以便提出决策支持工具和信息来帮助分析师。