MARC record from Internet Archive

LEADER: 04809cam 2200469Ii 4500
001 9925212987301661
005 20160414072430.0
008 160226s2015 gw a b 000 0 eng d
019 $a908374697
020 $a9783662473061 (electronic bk.)
020 $a3662473062 (electronic bk.)
020 $a9783662473054 (paperback bk.)
020 $a3662473054 (print)
020 $a9783662473054 (print)
035 $a(OCoLC)941209895$z(OCoLC)908374697
035 $a(OCoLC)ocn941209895
040 $aUHC$cUHC$dBTCTA$dYDXCP$dOCLCF$dCDX$dCNU
049 $aCNUM
050 4 $aQA76.53$b.X8 2015
082 04 $a005.4/34$223
100 1 $aXu, Chen,$eauthor.
245 10 $aQuality-aware scheduling for key-value data stores /$cChen Xu, Aoying Zhou.
264 1 $aHeidelberg :$bSpringer,$c2015.
300 $axi, 97 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
490 1 $aSpringerBriefs in computer science,$x2191-5768
504 $aIncludes bibliographical references.
520 $aKey-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.
505 0 $aPreface; Acknowledgments; Contents; 1 Introduction; 1.1 Application Scenarios; 1.2 The Research Significance and Challenges; 1.3 Implementation Framework; 1.4 Overview of the Book; References; 2 Literature and Research Review; 2.1 Metrics for Quality-Aware Scheduling; 2.1.1 QoS Metrics; 2.1.2 QoD Metrics; 2.2 Quality-Aware Scheduling in Data Management System; 2.2.1 Quality-Aware Scheduling in RTDBMS; 2.2.2 Quality-Aware Scheduling in DSMS; 2.2.3 Quality-Aware Scheduling in RDBMS; 2.2.4 Quality-Aware Scheduling in Key-Value Stores; 2.3 Summary; References; 3 Problem Overview
505 8 $a3.1 Background Knowledge 3.1.1 Data Organization; 3.1.2 Data Replication and Consistency; 3.1.3 User Queries; 3.1.4 System Updates: State-Transfer Versus Operation-Transfer; 3.2 Problem Statement; 3.2.1 QoS Penalty; 3.2.2 QoD Penalty; 3.2.3 Combined Penalty; 3.3 Summary; References; 4 Scheduling for State-Transfer Updates; 4.1 On-Demand (OD) Mechanism; 4.1.1 WSJF-OD; 4.2 Hybrid On-Demand (HOD) Mechanism; 4.2.1 WSJF-HOD; 4.3 Freshness/Tardiness (FIT) Mechanism; 4.3.1 WSJF-FIT; 4.4 Adaptive Freshness/Tardiness (AFIT) Mechanism; 4.4.1 Query Routing; 4.4.2 Query Selection; 4.4.3 WSJF-AFIT
505 8 $a4.5 Popularity-Aware Mechanism 4.5.1 Popularity-Aware WSJF-OD; 4.5.2 Popularity-Aware WSJF-HOD; 4.5.3 Popularity-Aware WSJF-FIT; 4.5.4 Popularity-Aware WSJF-AFIT; 4.6 Experimental Study; 4.6.1 Baseline Policies; 4.6.2 Parameter Setting; 4.6.3 Impact of Query Arrival Rate; 4.6.4 Impact of Update Cost; 4.6.5 Impact of Different QoS and QoD Preferences; 4.6.6 Impact of Popularity; 4.7 Summary; References; 5 Scheduling for Operation-Transfer Updates; 5.1 Hybrid On-Demand (HOD) Mechanism; 5.1.1 WSJF-HOD; 5.2 Freshness/Tardiness (FIT) Mechanism; 5.2.1 WSJF-FIT; 5.3 Popularity-Aware Mechanism
505 8 $a5.3.1 Popularity-Aware WSJF-HOD 5.3.2 Popularity-Aware WSJF-FIT; 5.4 Experimental Study; 5.4.1 Parameter Setting; 5.4.2 Impact of Update Arrival Rate; 5.4.3 Impact of Popularity and Approximation; 5.5 Summary; References; 6 AQUAS: A Quality-Aware Scheduler; 6.1 System Overview; 6.1.1 System Goals; 6.1.2 System Design; 6.2 System Performance; 6.2.1 Benchmark; 6.2.2 Evaluation Result; 6.3 A Demonstration on MicroBlogging Application; 6.3.1 Timeline Queries in AQUAS; 6.3.2 A Case Study; 6.4 Summary; References; 7 Conclusion and Future Work; 7.1 Conclusion; 7.2 Future Work; References
650 0 $aComputer scheduling.
650 0 $aDatabase management.
720 $aXu, Chen
720 $aZhou, Aoying
700 1 $aZhou, Aoying,$d1965-$eauthor.
830 0 $aSpringerBriefs in computer science.
947 $hCIRCSTACKS$r31786103018039
980 $a99964955382