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Scheduling Multiple Data Visualization Query Workloads on a Shared Memory Machine

dc.contributor.authorAndrade, Henriqueen_US
dc.contributor.authorKurc, Tahsinen_US
dc.contributor.authorSussman, Alanen_US
dc.contributor.authorSaltz, Joelen_US
dc.date.accessioned2004-05-31T23:12:57Z
dc.date.available2004-05-31T23:12:57Z
dc.date.created2001-01en_US
dc.date.issued2001-10-10en_US
dc.identifier.urihttp://hdl.handle.net/1903/1152
dc.description.abstractQuery 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)en_US
dc.format.extent1199625 bytes
dc.format.mimetypeapplication/postscript
dc.language.isoen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4290en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2001-68en_US
dc.titleScheduling Multiple Data Visualization Query Workloads on a Shared Memory Machineen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US


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