| The limitations of the traditional Database Management System to process continuous, unbounded and real time data streams for applications such as tickers, network monitoring, traffic management, and sensor monitoring have necessitated the development of MavStream. It is a Data Stream Management System (DSMS) developed for processing stream data with bursty arrival rates and need for real-time performance requirements.;This thesis investigates the design and implementation of scheduling strategies for stream processing. The scheduling strategies implemented in this thesis minimize the tuple latency enabling the system to satisfy quality of service (QoS) requirements. Several scheduling strategies have been integrated into the architecture of MavStream. We first introduce the path capacity strategy with the goal of minimization of the tuple latency. As an improvement, a segment scheduling strategy and its simplified version are implemented which aim at minimization of total memory requirement and throughput.;Extensive set of experiments have been designed and executed to evaluate the effectiveness of the proposed scheduling strategies. In addition, a feeder module was developed to simulate real time streams. Extensive experiments have been performed to measure tuple latency, memory usage and their effect on varying data rate and window widths on input streams with varying characteristics. |
