Real-time scheduling for continuous queries with deadlines
Proceedings of the 2009 ACM symposium on Applied Computing
Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments
Journal of Parallel and Distributed Computing
Cost-Based Vectorization of Instance-Based Integration Processes
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
Adaptive scheduling strategy for data stream management system
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
A QoS-guaranteeing scheduling algorithm for continuous queries over streams
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Cost-based vectorization of instance-based integration processes
Information Systems
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Quality-aware management of data streams is gaining moreand more importance with the amount of data produced by streams growing continuously. The resources required for data stream processing depend on different factors and are limited by the environment of the Data Stream Management System (DSMS). Thus, with a potentially unbounded amount of stream data and limited processing resources, some of the data stream processing tasks (originating from different users) may not be satisfyingly answered, and therefore, users should be enabled to negotiate a certain quality for the execution of their stream processing tasks.After the negotiation process, it is the responsibility of the DataStream Management System to meet the quality constraints by using adequate resource reservation and scheduling techniques.Within this paper, we consider different aspects of real-timescheduling for operations within a DSMS.We propose a schedulingconcept which enables us to meet certain time-dependentQuality-of-Service requirements for user-given processing tasks.Furthermore, we describe the implementation of our schedulingconcept within a real-time-capable Data Stream ManagementSystem, and we give experimental results on that.