© 2015

Quality-aware Scheduling for Key-value Data Stores


  • Provides a comprehensive overview on the related literature and research

  • Describes the strategies of quality-aware scheduling for state-transfer updates and operation-transfer updates

  • Presents a prototype of quality-aware scheduler and demonstrates the timeline query on a Microblogging application


Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Chen Xu, Aoying Zhou
    Pages 1-9
  3. Chen Xu, Aoying Zhou
    Pages 11-23
  4. Chen Xu, Aoying Zhou
    Pages 25-35
  5. Chen Xu, Aoying Zhou
    Pages 37-63
  6. Chen Xu, Aoying Zhou
    Pages 65-81
  7. Chen Xu, Aoying Zhou
    Pages 83-94
  8. Chen Xu, Aoying Zhou
    Pages 95-97

About this book


Key-value stores, which are commonly used as data platform for various web applications, provide a distributed solution for cloud computing and big data management.  In modern web applications, user experience satisfaction determines their success​. In real application, different web queries or users produce different expectations in terms of query latency (i.e., Quality of Service (QoS)) and data freshness (i.e., Quality of Data (QoD)).  Hence, the question of how to optimize QoS and QoD by scheduling queries and updates in key-value stores has become an essential research issue. This book comprehensively illustrates quality-ware scheduling in key-value stores. In addition, it provides scheduling strategies and a prototype framework for a quality-aware scheduler, as well as a demonstration of online applications. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in distributed systems, NoSQL key-value stores and scheduling.​


Data Consistency Key-Value Stores Quality of Data Quality of Service Scheduling

Authors and affiliations

  1. 1.Institute for Data Science and EngineeringEast China Normal UniversityShanghaiChina
  2. 2.Institute for Data Science and EngineeringEast China Normal UniversityShanghaiChina

About the authors

Chen Xu received his PhD degree from East China Normal University (ECNU) in 2014 and Bachelor degree from Hefei University of Technology (HFUT) in 2009. In 2011, Chen studied as visiting student at The University of Queensland (UQ) supported by a research fellowship from UQ. He held the honors of outstanding graduates from ECNU and HFUT as well as Anhui provincial government of P.R. China. He was the winner of the National Scholarship from Ministry of Education of P.R. China in 2008. Chen has publications in academic journal such as Distributed and Parallel Databases (DAPD), and conferences including ICDE, DASFAA, etc. He is serving as a reviewer of Frontier of Computer Science (FCS). His research interest includes data management for data-intensive computing, large-scale data analysis, etc. Aoying Zhou is a professor on Computer Science at East China Normal University (ECNU), where he is heading the Institute for Data Science and Engineering. He got his master and bachelor degree in Computer Science from Sichuan University, in 1988 and 1985 respectively, and he won his Ph.D. degree from Fudan University in 1993. Before joining ECNU in 2008, Aoying worked for Fudan University at the Computer Science Department for 15 years. He is the winner of the National Science Fund for Distinguished Young Scholars supported by NSFC and the professorship appointment under Changjiang Scholars Program of Ministry of Education in China. He is now acting as a vice-director of ACM SIGMOD China and Database Technology Committee of China Computer Federation. He is serving as a member of the editorial boards VLDB Journal, WWW Journal, and etc. His research interests include Web data management, data management for data-intensive computing, memory cluster computing, benchmarking for big data and performance.

Bibliographic information

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