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000727552 019__ $$a914434591
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000727552 050_4 $$aQA76.53$$b.X8 2015eb
000727552 08204 $$a005.4/34$$223
000727552 1001_ $$aXu, Chen,$$eauthor.
000727552 24510 $$aQuality-aware scheduling for key-value data stores$$h[electronic resource] /$$cChen Xu, Aoying Zhou.
000727552 264_1 $$aHeidelberg :$$bSpringer,$$c2015.
000727552 300__ $$a1 online resource (xi, 97 pages) :$$billustrations.
000727552 336__ $$atext$$btxt$$2rdacontent
000727552 337__ $$acomputer$$bc$$2rdamedia
000727552 338__ $$aonline resource$$bcr$$2rdacarrier
000727552 4901_ $$aSpringerBriefs in computer science,$$x2191-5768
000727552 504__ $$aIncludes bibliographical references.
000727552 5050_ $$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
000727552 5058_ $$a3.1 Background Knowledge3.1.1 Data Organization; 3.1.2 Data Replication and Consistency; 3.1.3 User Queries; 3.1.4 System Updates: State-Transfer Versus Operation-Transfer; 3.2 Problem Statement; 3.2.1 QoS Penalty; 3.2.2 QoD Penalty; 3.2.3 Combined Penalty; 3.3 Summary; References; 4 Scheduling for State-Transfer Updates; 4.1 On-Demand (OD) Mechanism; 4.1.1 WSJF-OD; 4.2 Hybrid On-Demand (HOD) Mechanism; 4.2.1 WSJF-HOD; 4.3 Freshness/Tardiness (FIT) Mechanism; 4.3.1 WSJF-FIT; 4.4 Adaptive Freshness/Tardiness (AFIT) Mechanism; 4.4.1 Query Routing; 4.4.2 Query Selection; 4.4.3 WSJF-AFIT
000727552 5058_ $$a4.5 Popularity-Aware Mechanism4.5.1 Populairty-Aware WSJF-OD; 4.5.2 Populairty-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
000727552 5058_ $$a5.3.1 Popularity-Aware WSJF-HOD5.3.2 Popularity-Aware WSJF-FIT; 5.4 Experimental Study; 5.4.1 Parameter Setting; 5.4.2 Impact of Update Arrival Rate; 5.4.3 Impact of Popularity and Approximation; 5.5 Summary; References; 6 AQUAS: A Quality-Aware Scheduler; 6.1 System Overview; 6.1.1 System Goals; 6.1.2 System Design; 6.2 System Performance; 6.2.1 Benchmark; 6.2.2 Evaluation Result; 6.3 A Demonstration on MicroBlogging Application; 6.3.1 Timeline Queries in AQUAS; 6.3.2 A Case Study; 6.4 Summary; References; 7 Conclusion and Future Work; 7.1 Conclusion; 7.2 Future Work; References
000727552 506__ $$aAccess limited to authorized users.
000727552 520__ $$aKey-value stores, which are commonly used as data platform for various web applications, provide a distributed solution for cloud computing and big data management. In modern web applications, user experience satisfaction determines their success . In real application, different web queries or users produce different expectations in terms of query latency (i.e., Quality of Service (QoS)) and data freshness (i.e., Quality of Data (QoD)). Hence, the question of how to optimize QoS and QoD by scheduling queries and updates in key-value stores has become an essential research issue. This book comprehensively illustrates quality-ware scheduling in key-value stores. In addition, it provides scheduling strategies and a prototype framework for a quality-aware scheduler, as well as a demonstration of online applications. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in distributed systems, NoSQL key-value stores and scheduling.
000727552 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 10, 2015).
000727552 650_0 $$aComputer scheduling.
000727552 650_0 $$aDatabase management.
000727552 7001_ $$aZhou, Aoying,$$d1965-$$eauthor.
000727552 77608 $$iPrint version:$$aXu, Chen$$tQuality-aware Scheduling for Key-value Data Stores$$dBerlin, Heidelberg : Springer Berlin Heidelberg,c2015$$z9783662473054
000727552 830_0 $$aSpringerBriefs in computer science.
000727552 852__ $$bebk
000727552 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-662-47306-1$$zOnline Access$$91397441.1
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000727552 980__ $$aEBOOK
000727552 980__ $$aBIB
000727552 982__ $$aEbook
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