Dynamic resource allocation for database servers

拟议的项目将研究自主系统的适应性，以减少对大规模互联网服务器系统的人为管理的需要。自主系统监控并动态调整其行为，以满足预设的目标或行为要求。大型集群系统通常存在于提供动态内容服务的组织中，例如电子商务、在线银行、股票交易和游戏。这些服务基于存储在数据库中的持久状态动态生成内容。数据库系统传统上用于在给定时间在独立数据库引擎上运行单个应用程序。通常，在此配置中，专家管理员可以确定该应用程序的最佳数据库设置。大型数据中心的情况并非如此，在大型数据中心中，多个应用程序（如电子商务、拍卖和新闻）通常同时托管在同一硬件上，以降低成本。可能共享相同基础架构的应用程序的多样性使得配置和性能调优变得困难且耗时。此外，提供动态内容的大规模集群系统的规模不断增加，使得它们容易出现故障。从故障恢复，以及旨在减少故障的概率，如活动，系统备份占用了很大一部分人员的时间，并引入潜在的人为操作员errors.We建议开发自主数据库系统的机制，将不断监测和优化性能，并将自动故障恢复动态内容服务器，从而减少或消除目前所需的人力。具体来说，我们将研究动态分配资源的应用程序，以响应性能瓶颈或故障的水平：i）数据库服务器节点和ii）数据库服务器节点内的资源。我们提出的工作，预计将降低系统管理的总成本，同时提供更好的服务质量，从而有利于信息技术公司和他们的客户。

The proposed project will investigate autonomic system adaptations to reduce the need for human-directed management of large-scale Internet server systems. Autonomic systems monitor and dynamically adapt their behavior to meet preset goals or behavioral requirements. Large-scale cluster systems are often present in organizations that provide dynamic content services such as e-commerce, on-line banking, stock trading and gaming. Such services generate content on-the-fly based on persistent state stored in databases. Database systems have traditionally been used to run a single application at a given time on a stand-alone database engine. Typically, in this configuration, an expert administrator could determine the optimal database settings for that application. This is not the case with large data centers, where multiple applications, such as, e-commerce, auctions and news, are commonly hosted concurrently on the same hardware, in order to reduce costs. The diverse nature of applications that may share the same infrastructure makes configuration and performance tuning difficult and time consuming. Furthermore, the increasing size of the large-scale cluster systems serving dynamic content makes them prone to failures. Recovering from failures, as well as activities aimed at reducing the probability of failures, such as, system backup take up a significant portion of personnel time and introduce the potential for human operator errors.We propose to develop autonomic database system mechanisms that will continually monitor and optimize performance and will automate failure recovery in dynamic content servers, thus, reducing or eliminating the human effort currently required. Specifically, we will investigate dynamic allocation of resources to applications in response to either performance bottlenecks or faults at the level of: i) database server nodes and ii) resources inside a database server node. Our proposed work is expected to lower the total cost of system management, while at the same time provide improved quality of service, thus benefit information technology companies and their customers.