Autonomic database management systems

自主计算是一种以最少的人为干扰实现自我管理计算系统的方法。自主系统旨在通过在安装时（或添加新硬件时）配置自己，管理自己的资源以达到峰值性能，在软件崩溃时（或检测损坏的硬件并请求维修）自我修复，以及保护自己免受内部故障和外部攻击，从而在很少的人为交互下执行。自动化诊断和资源管理将带来更好的性能和更低的运营成本。自主计算影响重大的一个领域是嵌入式系统和应用程序。嵌入式系统和应用程序通常只有有限的资源可供应用程序运行，因此，性能取决于对资源的有效和智能使用。由于缺乏可以调整资源的常规系统接口，资源有限的情况进一步恶化。对于典型的嵌入式应用程序，例如存储在电话中的地址簿，不可能为了提高系统的性能而调整资源。在这种情况下，嵌入式应用程序必须负责管理自己的资源，并在给定可用资源的情况下实现最佳性能。自治原则在嵌入式应用程序和系统中的应用是必要的，因为我们期望从资源受限的设备中获得更多的功能。在过去几年中，自主计算在数据库管理系统（dbms）中特别流行。随着数据库的大小和复杂性的增加，手动控制性能的能力变得不切实际。随着dbms被嵌入到日常设备中，并且需要处理大量的信息并且性能良好，这个问题进一步加剧。DBMS在这些嵌入式应用程序上的性能变得更加明显，数据量也在增加。这一领域的进步将适用于DBMS、嵌入式系统和自治系统社区。

Autonomic computing is an approach to self-managed computing systems with a minimum of human interference. Autonomic systems are intended to perform with little human interaction by configuring themselves when installed (or when new hardware is added), manage their own resources for peak performance, heal themselves when software breaks (or detect broken hardware and ask for repairs) and protect themselves from both internal failures and outside attacks. Automated diagnosis and resource management will result in better performance and lower operating costs. One area where the impact of autonomic computing is significant is that of embedded systems and applications. Embedded systems and applications usually have a limited amount of resources available for application to run, and as such, performance is based on the efficient and intelligent use of resources. Limited resources are further exacerbated by the lack of regular system interfaces in which resources can be adjusted. For typical embedded applications such as an address book stored in a telephone, it is not possible to adjust resources in order to improve the performance of the system. In such cases, the embedded application must be responsible for managing its own resources and achieving the best possible performance given the resources available. The application of autonomic principles to embedded applications and systems is necessary as we expect more functionality from resource-constrained devices. Autonomic computing has been especially popular in database management systems (DBMSs) in the past several years. As databases increase in size and complexity, the ability to control performance by hand becomes impractical. This problem is further intensified as DBMSs are embedded into everyday devices and expected to handle significant amounts of information and perform well. DBMS performance on these embedded applications becomes more apparent and data volume increases. Advancements in this area will be applicable to the DBMS, embedded systems and autonomic systems community.