Is an improvement of traditional query processing, in order to addresses the problems of missing statistics, unexpected correlations, unpredictable costs, and dynamic data by using feedback to tune execution. It is one of the cornerstones of so-called autonomic database management systems, although it also generalizes to many other contexts, particularly at the intersection of database query processing and the Web.
Published in Chapter:
Adaptive Query Processing in Data Grids
Chunjiang Zhao (National Engineering and Research Center for Information Technology for Agriculture, China), Junwei Cao (Tsinghua University, China), Huarui Wu (Tsinghua National Laboratory for Information Science and Technology, China), and Weiwei Chen (Tsinghua University, China)
Copyright: © 2010
|Pages: 14
DOI: 10.4018/978-1-61520-686-5.ch016
Abstract
The data grid integrates wide-area autonomous data sources and provides users with a unified data query and processing infrastructure. Adaptive data query and processing is required by data grids to provide better quality of services (QoS) to users and applications in spite of dynamically changing resources and environments. Existing AQP techniques can only meet partially data grid requirements. Some existing work is either addressing domain-specific or single-node query processing problems. Data grids provide new mechanisms for monitoring and discovering data and resources in a cross-domain wide area. Data query in grids can benefit from this information and provide better adaptability to the dynamic nature of the grid environment. In this work, an adaptive controller is proposed that dynamically adjusts resource shares to multiple data query requests in order to meet a specified level of service differentiation. The controller parameters are automatically tuned at runtime based on a predefined cost function and an online learning method. Simulation results show that our controller can meet given QoS differentiation targets and adapt to dynamic system resources among multiple data query processing requests while total demand from users and applications exceeds system capability.