Algorithms and metrics for processing multiple heterogeneous continuous queries

Abstract

The emergence of monitoring applications has precipitated the need for Data Stream Management Systems (DSMSs), which constantly monitor incoming data feeds (through registered continuous queries), in order to detect events of interest. In this article, we examine the problem of how to schedule multiple Continuous Queries (CQs) in a DSMS to optimize different Quality of Service (QoS) metrics. We show that, unlike traditional online systems, scheduling policies in DSMSs that optimize for average response time will be different from policies that optimize for average slowdown, which is a more appropriate metric to use in the presence of a heterogeneous workload. Towards this, we propose policies to optimize for the average-case performance for both metrics. Additionally, we propose a hybrid scheduling policy that strikes a fine balance between performance and fairness, by looking at both the average- and worst-case performance, for both metrics. We also show how our policies can be adaptive enough to handle the inherent dynamic nature of monitoring applications. Furthermore, we discuss how our policies can be efficiently implemented and extended to exploit sharing in optimized multi-query plans and multi-stream CQs. Finally, we experimentally show using real data that our policies consistently outperform currently used ones.