Scheduling Strategies and Their Evaluation in a Data Stream Management System

  • Sharma Chakravarthy
  • Vamshi Pajjuri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4042)


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.


Schedule Strategy Query Execution Operator Path Query Plan Continuous Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sharma Chakravarthy
    • 1
  • Vamshi Pajjuri
    • 1
  1. 1.Information Technology Laboratory and Department of Computer Science and EngineeringThe University of Texas at Arlington 

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