Abstract
Web search engines are optimized to reduce the high-percentile response time to consistently provide fast responses to almost all user queries. This is a challenging task because the query workload exhibits large variability, consisting of many short-running queries and a few long-running queries that significantly impact the high-percentile response time. With modern multicore servers, parallelizing the processing of an individual query is a promising solution to reduce query execution time, but it gives limited benefits compared to sequential execution since most queries see little or no speedup when parallelized. The root of this problem is that short-running queries, which dominate the workload, do not benefit from parallelization. They incur a large parallelization overhead, taking scarce resources from long-running queries. On the other hand, parallelization substantially reduces the execution time of long-running queries with low overhead and high parallelization efficiency. Motivated by these observations, we propose a predictive parallelization framework with two parts: (1) predicting long-running queries, and (2) selectively parallelizing them. For the first part, prediction should be accurate and efficient. For accuracy, we study a comprehensive feature set covering both term features (reflecting dynamic pruning efficiency) and query features (reflecting query complexity). For efficiency, to keep overhead low, we avoid expensive features that have excessive requirements such as large memory footprints. For the second part, we use the predicted query execution time to parallelize long-running queries and process short-running queries sequentially. We implement and evaluate the predictive parallelization frame-work in Microsoft Bing search. Our measurements show that under moderate to heavy load, the predictive strategy reduces the 99th-percentile response time by 50% (from 200 ms to 100 ms) compared with prior approaches that parallelize all queries.
| Original language | English |
|---|---|
| Title of host publication | SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Publisher | Association for Computing Machinery |
| Pages | 253-262 |
| Number of pages | 10 |
| ISBN (Print) | 9781450322591 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia Duration: 2014 Jul 6 → 2014 Jul 11 |
Publication series
| Name | SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval |
|---|
Other
| Other | 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 |
|---|---|
| Country | Australia |
| City | Gold Coast, QLD |
| Period | 14/7/6 → 14/7/11 |
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All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design
- Information Systems
Cite this
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Predictive parallelization : Taming tail latencies in web search. / Jeon, Myeongjae; Kim, Saehoon; Hwang, Seung Won; He, Yuxiong; Elnikety, Sameh; Cox, Alan L.; Rixner, Scott.
SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2014. p. 253-262 (SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Predictive parallelization
T2 - Taming tail latencies in web search
AU - Jeon, Myeongjae
AU - Kim, Saehoon
AU - Hwang, Seung Won
AU - He, Yuxiong
AU - Elnikety, Sameh
AU - Cox, Alan L.
AU - Rixner, Scott
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Web search engines are optimized to reduce the high-percentile response time to consistently provide fast responses to almost all user queries. This is a challenging task because the query workload exhibits large variability, consisting of many short-running queries and a few long-running queries that significantly impact the high-percentile response time. With modern multicore servers, parallelizing the processing of an individual query is a promising solution to reduce query execution time, but it gives limited benefits compared to sequential execution since most queries see little or no speedup when parallelized. The root of this problem is that short-running queries, which dominate the workload, do not benefit from parallelization. They incur a large parallelization overhead, taking scarce resources from long-running queries. On the other hand, parallelization substantially reduces the execution time of long-running queries with low overhead and high parallelization efficiency. Motivated by these observations, we propose a predictive parallelization framework with two parts: (1) predicting long-running queries, and (2) selectively parallelizing them. For the first part, prediction should be accurate and efficient. For accuracy, we study a comprehensive feature set covering both term features (reflecting dynamic pruning efficiency) and query features (reflecting query complexity). For efficiency, to keep overhead low, we avoid expensive features that have excessive requirements such as large memory footprints. For the second part, we use the predicted query execution time to parallelize long-running queries and process short-running queries sequentially. We implement and evaluate the predictive parallelization frame-work in Microsoft Bing search. Our measurements show that under moderate to heavy load, the predictive strategy reduces the 99th-percentile response time by 50% (from 200 ms to 100 ms) compared with prior approaches that parallelize all queries.
AB - Web search engines are optimized to reduce the high-percentile response time to consistently provide fast responses to almost all user queries. This is a challenging task because the query workload exhibits large variability, consisting of many short-running queries and a few long-running queries that significantly impact the high-percentile response time. With modern multicore servers, parallelizing the processing of an individual query is a promising solution to reduce query execution time, but it gives limited benefits compared to sequential execution since most queries see little or no speedup when parallelized. The root of this problem is that short-running queries, which dominate the workload, do not benefit from parallelization. They incur a large parallelization overhead, taking scarce resources from long-running queries. On the other hand, parallelization substantially reduces the execution time of long-running queries with low overhead and high parallelization efficiency. Motivated by these observations, we propose a predictive parallelization framework with two parts: (1) predicting long-running queries, and (2) selectively parallelizing them. For the first part, prediction should be accurate and efficient. For accuracy, we study a comprehensive feature set covering both term features (reflecting dynamic pruning efficiency) and query features (reflecting query complexity). For efficiency, to keep overhead low, we avoid expensive features that have excessive requirements such as large memory footprints. For the second part, we use the predicted query execution time to parallelize long-running queries and process short-running queries sequentially. We implement and evaluate the predictive parallelization frame-work in Microsoft Bing search. Our measurements show that under moderate to heavy load, the predictive strategy reduces the 99th-percentile response time by 50% (from 200 ms to 100 ms) compared with prior approaches that parallelize all queries.
UR - http://www.scopus.com/inward/record.url?scp=84904568618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904568618&partnerID=8YFLogxK
U2 - 10.1145/2600428.2609572
DO - 10.1145/2600428.2609572
M3 - Conference contribution
AN - SCOPUS:84904568618
SN - 9781450322591
T3 - SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 253
EP - 262
BT - SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
ER -