Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Rate-based query optimization for streaming information sources
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
MavHome: An Agent-Based Smart Home
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Chain: operator scheduling for memory minimization in data stream systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Operator scheduling in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Load Shedding in MavStream: Analysis, Implementation, and Evaluation
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
Parallel processing of continuous queries over data streams
Distributed and Parallel Databases
Extensions to stream processing architecture for supporting event processing
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Dynamic routing of data stream tuples among parallel query plan running on multi-core processors
Distributed and Parallel Databases
Multilevel secure data stream processing: Architecture and implementation
Journal of Computer Security - DBSec 2011
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MavStream, a Data Stream Management System (DSMS), has been developed for processing stream data from applications such as network monitoring, sensor monitoring and traffic management systems that require near-real time results and have to process unbounded streams of data. In order to be useful, a result produced by MavStream has to meet certain Quality of Service (QoS) requirements on tuple latency, memory usage, and throughput. Strategies used for scheduling the operators of continuous query (CQ) significantly affect the QoS metrics and hence are of interest. This paper discusses scheduling strategies used in MavStream, their design, implementation, and evaluation. Scheduling is done in MavStream at the operator level. The scheduler maintains a ready queue of operators and decides on the operators to be scheduled based on the scheduling strategy. We first introduce the path capacity scheduling strategy with the goal of minimizing tuple latency by scheduling operator paths with maximum processing capacity. Later we discuss segment-scheduling strategy that aims at minimization of total memory requirement by scheduling operator segments with maximum memory release capacity. We then discuss simplified segment strategy, which splits operator path into just two segments providing better tuple latency performance than segment scheduling strategy and lower memory utilization than path capacity scheduling strategy. Extensive set of experiments have been designed and performed to evaluate the proposed scheduling strategies by simulating real time streams. The performance metrics of average tuple latency, memory utilization and throughput are compared with each other for different strategies and with round robin strategy to validate the analytical conclusions.