Adaptive query scheduling in key-value data stores

Xu, Chen, Sharaf, Mohamed, Zhou, Minqi, Zhou, Aoying and Zhou, Xiaofang (2013). Adaptive query scheduling in key-value data stores. In: Bonghee Hong, Xiaofeng Meng, Lei Chen, Werner Winiwarter and Wei Song, Database Systems for Advanced Applications - 18th International Conference, DASFAA 2013, Proceedings. 18th International Conference on Database Systems for Advanced Applications, DASFAA 2013, Wuhan, China, (86-100). 22-25 April 2013. doi:10.1007/978-3-642-37487-6_9

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Author Xu, Chen
Sharaf, Mohamed
Zhou, Minqi
Zhou, Aoying
Zhou, Xiaofang
Title of paper Adaptive query scheduling in key-value data stores
Conference name 18th International Conference on Database Systems for Advanced Applications, DASFAA 2013
Conference location Wuhan, China
Conference dates 22-25 April 2013
Proceedings title Database Systems for Advanced Applications - 18th International Conference, DASFAA 2013, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2013
Sub-type Fully published paper
DOI 10.1007/978-3-642-37487-6_9
Open Access Status Not yet assessed
ISBN 9783642374869
ISSN 0302-9743
Editor Bonghee Hong
Xiaofeng Meng
Lei Chen
Werner Winiwarter
Wei Song
Volume 7825 LNCS
Issue PART 1
Start page 86
End page 100
Total pages 15
Abstract/Summary Large-scale distributed systems such as Dynamo at Amazon, PNUTS at Yahoo!, and Cassandra at Facebook, are rapidly becoming the data management platform of choice for most web applications. Those key-value data stores rely on data partitioning and replication to achieve higher levels of availability and scalability. Such design choices typically exhibit a trade-off in which data freshness is sacrificed in favor of reduced access latencies. Hence, it is indispensable to optimize resource allocation in order to minimize: 1) query tardiness, i.e., maximize Quality of Service (QoS), and 2) data staleness, i.e., maximize Quality of Data (QoD). That trade-off between QoS and QoD is further manifested at the local-level (i.e., replica-level) and is primarily shaped by the resource allocation strategies deployed for managing the processing of foreground user queries and background system updates. To this end, we propose the AFIT scheduling strategy, which allows for selective data refreshing and integrates the benefits of SJF-based scheduling with an EDF-like policy. Our experiments demonstrate the effectiveness of our method, which does not only strike a fine trade-off between QoS and QoD but also automatically adapts to workload settings.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
 
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