Adaptive scheduling strategy for data stream management system

More and more applications involve processing continuous data streams, and the data stream management system (DSMS) is designed to deal with such data streams. Due to features of large volume and stochastic arrival, DSMS must process data stream efficiently in order to avoid system memory exhaustion and reduce the data access latency, to satisfy requirements of the application requirement. One of the key factors, which significantly impact the system performance significantly, is the scheduling strategy adopted by the DSMS. Chain scheduling is an operator-based scheduling strategy for DSMS, which has near-optimal in terms of run-time memory usage. FIFO strategy achieves optimal performance in terms of data access latency. Inspired by the two important scheduling strategies, Chain and FIFO, we propose two novel adaptive strategies for DSMS, ASCF and CSS, which efficiently deal with the varying input load in terms of both memory usage and data access latency. To give a fair comparison performance with other competing strategies, we design thorough simulation experiment and run different strategies under the same system environment. The outcomes of simulation experiment demonstrate the potential benefits and advantages of ASCF and CSC.