• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Towards Adaptive Policy-Based Autonomic Management.
【24h】

Towards Adaptive Policy-Based Autonomic Management.

机译：迈向基于自适应策略的自主管理。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The combination of applications integrated within a single or multi-computer environment has become a key component in the way many organizations deliver their services and provide support. The increased diversity of applications compounded by rising expectations from users regarding the quality of service performance of these systems means that more and more systems administrators are turning to automated solutions. Policy-based management offers significant benefit to this effect since the use of policies can make it more straightforward to define and modify systems behavior at run-time, through policy manipulation, rather than through re-engineering. Equally important, however, is the need for systems to continuously monitor their own behavior from the use of policies, evaluate that behavior, and adapt, when necessary, to cope with not only the inherent human error but also changes in the configuration of the managed environment.;Keywords: Policy-based Management, autonomic management, reinforcement learning, model adaptation, quality of service management.;This thesis proposes an adaptive policy-driven autonomic management approach to quality of service provisioning, utilizing Reinforcement Learning methodologies to determine how best to use a set of policies to meet performance objectives. We believe that "learning" could offer significant benefits to this effect since it enables systems to learn from past experience, predict future actions, and make appropriate trade-offs when selecting policy actions for resolving quality of service violations and for optimizing resources usage. Contrary to most approaches utilizing some form of learning to guide performance management where changes to the environment (in this case the active set of policies) often mean discarding the old knowledge, this work presents an approach for "re-using" the experience - by transforming a model learned from the use of one set of active policies to a new model when those policies change. Since the strategies for learning and adaptation are dependent only on the structure of the policies, our approach can be utilized in a variety of domains. As such, our work is both unique and essential in developing flexible, adaptive, and portable management solutions.
机译：集成在单计算机或多计算机环境中的应用程序组合已成为许多组织交付服务和提供支持的关键组成部分。随着用户对这些系统的服务质量的期望越来越高，应用程序的多样性也随之增加，这意味着越来越多的系统管理员正在转向自动化解决方案。基于策略的管理为此效果提供了显着的好处，因为策略的使用可使通过策略操纵（而不是通过重新设计）在运行时更轻松地定义和修改系统行为。但是，同样重要的是，系统需要不断地通过使用策略来监视自身的行为，评估该行为，并在必要时进行调整，以应对固有的人为错误以及被管理者配置的更改关键字：基于策略的管理，自主性管理，强化学习，模型适应，服务质量管理。；本文提出了一种自适应策略驱动的自主性管理方法来提供服务质量，利用强化学习方法来确定最佳方法使用一套策略来达到绩效目标。我们认为，“学习”可以为这种效果带来显着的好处，因为它使系统可以从过去的经验中学习，预测未来的行为，并在选择用于解决服务质量违规和优化资源使用的策略行为时做出适当的权衡。与大多数利用某种形式的学习来指导绩效管理的方法相反，在这种情况下，环境的变化（在这种情况下是一套有效的政策）通常意味着丢弃旧知识，这项工作提出了一种“重用”经验的方法-通过当这些策略发生更改时，将从使用一组活动策略中学到的模型转换为新模型。由于学习和适应的策略仅取决于政策的结构，因此我们的方法可用于多种领域。因此，在开发灵活，自适应和便携式管理解决方案方面，我们的工作既独特又必不可少。

著录项

  • 作者

    Bahati, Raphael M.;

  • 作者单位

    The University of Western Ontario (Canada).;

  • 授予单位 The University of Western Ontario (Canada).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 320 p.
  • 总页数 320
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号