Balancing Latency and Quality in Web Search
Liang Zhou, Kadangode K. Ramakrishnan · 2021
Selecting the right time budget for a search query is challenging because a proper balance between the search latency, quality and efficiency has to be maintained. State-of-the-art approaches leverage a centralized sample index at the aggregator to select the Index Serving Nodes (ISNs) to maintain quality and responsiveness. In this paper, we propose Cottage, a coordinated framework between the aggregator and ISNs for latency and quality optimization in web search. Cottage has two separate neural network models at each ISN to predict the quality contribution and latency, respectively. Then, these prediction results are sent back to the aggregator for latency and quality optimizations. The key task is integration of the predictions at the aggregator in determining an optimal dynamic time budget for identifying slow and low quality ISNs to improve latency and search efficiency. Our experiments on the Solr search engine prove that Cottage can reduce the average query latency by 54% and achieve a good P@10 search quality of 0.947.