LEADER 05897nam 22007095 450 001 9910299240803321 005 20200629224838.0 010 $a3-662-47306-2 024 7 $a10.1007/978-3-662-47306-1 035 $a(CKB)3710000000422116 035 $a(EBL)2095397 035 $a(SSID)ssj0001525074 035 $a(PQKBManifestationID)11909418 035 $a(PQKBTitleCode)TC0001525074 035 $a(PQKBWorkID)11496896 035 $a(PQKB)11610396 035 $a(DE-He213)978-3-662-47306-1 035 $a(MiAaPQ)EBC2095397 035 $a(PPN)186397011 035 $a(EXLCZ)993710000000422116 100 $a20150605d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aQuality-aware Scheduling for Key-value Data Stores /$fby Chen Xu, Aoying Zhou 205 $a1st ed. 2015. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015. 215 $a1 online resource (102 p.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 300 $aDescription based upon print version of record. 311 $a3-662-47305-4 320 $aIncludes bibliographical references at the end of each chapters. 327 $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 327 $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 327 $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 327 $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 330 $aKey-value stores, which are commonly used as data platform for various web applications, provide a distributed solution for cloud computing and big data management.  In modern web applications, user experience satisfaction determines their success . In real application, different web queries or users produce different expectations in terms of query latency (i.e., Quality of Service (QoS)) and data freshness (i.e., Quality of Data (QoD)).  Hence, the question of how to optimize QoS and QoD by scheduling queries and updates in key-value stores has become an essential research issue. This book comprehensively illustrates quality-ware scheduling in key-value stores. In addition, it provides scheduling strategies and a prototype framework for a quality-aware scheduler, as well as a demonstration of online applications. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in distributed systems, NoSQL key-value stores and scheduling. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aDatabase management 606 $aApplication software 606 $aOperating systems (Computers) 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aOperating Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I14045 615 0$aDatabase management. 615 0$aApplication software. 615 0$aOperating systems (Computers). 615 14$aDatabase Management. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aOperating Systems. 676 $a005.7565 700 $aXu$b Chen$4aut$4http://id.loc.gov/vocabulary/relators/aut$0926016 702 $aZhou$b Aoying$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299240803321 996 $aQuality-aware Scheduling for Key-value Data Stores$92520016 997 $aUNINA