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Assessing operational impact in enterprise systems with dependency discovery and usage mining.

机译：通过依赖性发现和使用情况挖掘来评估企业系统中的操作影响。

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摘要

Enterprise systems are growing larger, more distributed, and increasingly complex. They can be composed of hundreds or thousands of heterogeneous workstations and servers, connected via various networking devices, which allow business users to access critical data via multi-tier applications and web services. They can vary in architecture, available bandwidth, computing power, and the amount of black-box resources employed. System administrators are often required to assess the impact on business operations when an enterprise system component fails, which we refer to as assessing the operational impact. Operational impacts can also be caused inadvertently when enterprise system components are reconfigured. Assessing operational impacts accurately is critical to providing business executives with information needed to allocate limited Information Technology resources optimally---for example, maintenance personnel, time, and dollars.;We claim that assessing operational impact requires that administrators relate the component failure to the affected users in a manner that is clear and understandable by business executives. A number of approaches have been presented to calculate these kinds of impact, but many of these approaches have focused on the calculating the dependencies at the application & infrastructure levels. The applications are important only in that they provide a means for the business users to access their critical business data stored in files, databases and other (possibly remote) repositories, or to contact other users directly in a timely manner. Furthermore, the importance of different sets data will vary over time. For example, a certain set of financial data, and the ability to access and modify this data, might be significantly more critical to the business operations near the end of the fiscal year as opposed to other times. Consequently, to more accurately determine the operational impact, an impact assessment system must also monitor the various data sources accessed, the various applications used to access them, and the periods of time for which accessing these files are truly critical to the business users.;This paper presents a framework for monitoring the dependencies between users, applications, and other system components, combined with the actual access times and frequencies. We use operating system commands to extract information from the end-user workstations about the dependencies between system components. We also record the times that system components are accessed, and use data mining tools to detect usage patterns. This information can then be used to predict whether or not the failure of a component will cause an impact during certain time periods. Furthermore, we designed this framework to require minimal installation and management overhead, and to consume minimal system resources, so that it can be employed on a variety of enterprise systems, including those with low-bandwidth and partial-connectivity characteristics. Finally, we implemented this framework in a test environment to demonstrate the feasibility of this approach. This combination of understanding how and when users access various system components allows us to better assess current and future operational impacts.
机译：企业系统正在变得越来越大，越来越分散并且越来越复杂。它们可以由数百个或数千个异构工作站和服务器组成，它们通过各种网络设备连接，使业务用户可以通过多层应用程序和Web服务访问关键数据。它们的体系结构，可用带宽，计算能力和使用的黑匣子资源可能会有所不同。当企业系统组件发生故障时，通常需要系统管理员评估对业务运营的影响，我们称其为评估运营影响。重新配置企业系统组件时，也会无意中引起操作影响。准确评估运营影响对于为业务主管提供必要的信息以最佳地分配有限的信息技术资源（例如维护人员，时间和金钱）至关重要。我们声称，评估运营影响需要管理员将组件故障与解决方案相关联。以业务主管清楚易懂的方式影响用户。已经提出了许多方法来计算这些类型的影响，但是其中许多方法都集中在计算应用程序和基础结构级别的依赖关系上。这些应用程序之所以重要，仅在于它们为业务用户提供了一种访问其存储在文件，数据库和其他（可能是远程）存储库中的关键业务数据或及时与其他用户直接联系的方式。此外，不同集合数据的重要性会随着时间而变化。例如，一组财务数据以及访问和修改此数据的能力可能对会计年度结束时的业务运营比其他时间更为重要。因此，为了更准确地确定运营影响，影响评估系统还必须监视所访问的各种数据源，用于访问它们的各种应用程序以及访问这些文件对于业务用户而言真正至关重要的时间段。本文提出了一个框架，用于监视用户，应用程序和其他系统组件之间的依赖性，并结合实际的访问时间和频率。我们使用操作系统命令从最终用户工作站中提取有关系统组件之间依赖性的信息。我们还将记录访问系统组件的时间，并使用数据挖掘工具检测使用模式。然后，可以使用此信息来预测组件的故障在某些时间段内是否会造成影响。此外，我们设计了此框架，以要求最小的安装和管理开销，并消耗最少的系统资源，以便可以在各种企业系统中使用它，包括具有低带宽和部分连接性的系统。最后，我们在测试环境中实现了该框架，以证明此方法的可行性。了解用户如何以及何时访问各种系统组件的这种结合，使我们能够更好地评估当前和将来的运营影响。

著录项

  • 作者

    Moss, Mark Bomi.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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