An Adaptive Multi-Objective Scheduling Selection Framework for Continuous Query Processing

Adaptive operator scheduling algorithms for continuous query processing are usually designed to serve a single performance objective, such as minimizing memory usage or maximizing query throughput. We observe that different performance objectives may sometimes conflict with each other. Also due to the dynamic nature of streaming environments, the performance objective may need to change dynamically. Furthermore, the performance specification defined by users may itself be multi-dimensional. Therefore, utilizing a single scheduling algorithm optimized for a single objective is no longer sufficient. In this paper, we propose a novel adaptive scheduling algorithm selection framework named AMoS. It is able to leverage the strengths of existing scheduling algorithms to meet multiple performance objectives. AMoS employs a lightweight learning mechanism to assess the effectiveness of each algorithm. The learned knowledge can be used to select the algorithm that probabilistically has the ...

• Type: Conference
• Year: 2005
• Where: IDEAS
• Authors: Timothy M. Sutherland, Yali Zhu, Luping Ding, Elke A. Rundensteiner
• Keywords: Database, IDEAS 2005, Operator Scheduling Algorithms, Performance Objectives, Scheduling Algorithms