Preference-Aware Query and Update Scheduling in Web-databases


Typical Web-database systems receive read-only queries, that generate dynamic Web pages as a response, and write-only updates, that keep information up-to-date. Users expect short response times and low staleness. However, it may be extremely hard to apply all updates on time, i.e., keep zero staleness, and also get fast response times, especially in periods of bursty traffic. In this paper, we present the concept of quality contracts (QCs) which combines the two incomparable performance metrics: response time or quality of service (QoS), and staleness or quality of data (QoD). QCs allows individual users to express their preferences for the expected QoS and QoD of their queries by assigning "profit" values. To maximize the total profit from submitted QCs, we propose an adaptive algorithm, called QUTS. QUTS addresses the problem of prioritizing the scheduling of updates over queries using a two-level scheduling scheme that dynamically allocates CPU resources to updates and queries according to user preferences. We present the results of an extensive experimental study using real data (taken from a stock information Web site), where we show that QUTS performs better than baseline algorithms under the entire spectrum of QCs; QUTS also adapts fast to changing workloads.

DOI: 10.1109/ICDE.2007.367881

Extracted Key Phrases

14 Figures and Tables

Showing 1-10 of 40 extracted citations


Citations per Year

63 Citations

Semantic Scholar estimates that this publication has received between 41 and 105 citations based on the available data.

See our FAQ for additional information.