Answering Multiple-Item Queries in Data Broadcast Systems

A lot of research has been done on answering single-item queries, only a few have looked at answering multiple-item queries in data broadcast systems. The few that did, have proposed approaches that are less responsive to changes in the query queue. It is not immediately clear how single-item scheduling algorithms will perform when used in answering pull-based multiple-item queries. This paper investigates the performance of existing single-item scheduling algorithms in answering multiple-item queries in pull-based data broadcast systems. We observed that Longest Wait First, a near-optimal single-item data scheduling algorithm, has been used in environments where users' data access pattern is skewed. This paper also investigates the performance of Longest Wait First under various user access patterns. We propose $\mathcal{Q}$LWF: an online data broadcast scheduling algorithm for answering multiple-item queries in pull-based data broadcast systems. For the purpose of comparison with $\mathcal{Q}$LWF, we adapted existing pull single-item algorithm, push single-item algorithm, and push multiple-item algorithm to answer multiple-item queries in pull environments. Results from extensive sets of experiments show that $\mathcal{Q}$LWF has a superior performance compared with the adapted algorithms.