Scheduling Multiple Data Visualization Query Workloads on a Shared Memory Machine

Query scheduling plays an important role when systems are faced with limited resources and high workloads. It becomes even more relevant for servers applying multiple query optimization techniques to batches of queries, in which portions of datasets as well as intermediate results are maintained in memory to speed up query evaluation. In this work, we present a dynamic query scheduling model based on a priority queue implementation using a directed graph and a strategy for ranking queries. We examine the relative performance of four ranking strategies and compare them against a first-in first-out (FIFO) scheduling strategy. We describe experimental results on a shared-memory machine using two different versions of an application, called the Virtual Microscope, for browsing digitized microscopy images. (Also cross-referenced UMIACS-TR-2001-68