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Data Management in the Cloud: Challenges and Opportunities [Mīkstie vāki]

  • Formāts: Paperback / softback, 138 pages, height x width: 235x191 mm, weight: 333 g
  • Sērija : Synthesis Lectures on Data Management
  • Izdošanas datums: 01-Dec-2012
  • Izdevniecība: Morgan & Claypool Publishers
  • ISBN-10: 1608459241
  • ISBN-13: 9781608459247
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 59,85 €
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  • Formāts: Paperback / softback, 138 pages, height x width: 235x191 mm, weight: 333 g
  • Sērija : Synthesis Lectures on Data Management
  • Izdošanas datums: 01-Dec-2012
  • Izdevniecība: Morgan & Claypool Publishers
  • ISBN-10: 1608459241
  • ISBN-13: 9781608459247
Citas grāmatas par šo tēmu:
Cloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an increase in the scale of the data generated as well as consumed by such applications. Scalable database management systems form a critical part of the cloud infrastructure. The attempt to address the challenges posed by the management of big data has led to a plethora of systems.This book aims to clarify some of the important concepts in the design space of scalable data management in cloud computing infrastructures. Some of the questions that this book aims to answer are: the appropriate systems for a specific set of application requirements, the research challenges in data management for the cloud, and what is novel in the cloud for database researchers? We also aim to address one basic question: whether cloud computing poses new challenges in scalable data management or it is just a reincarnation of old problems? We provide a comprehensive background study of state-of-the-art systems for scalable data management and analysis. We also identify important aspects in the design of different systems and the applicability and scope of these systems. A thorough understanding of current solutions and a precise characterization of the design space are essential for clearing the "cloudy skies of data management" and ensuring the success of DBMSs in the cloud, thus emulating the success enjoyed by relational databases in traditional enterprise settings.
Preface xv
Acknowledgments xvii
1 Introduction
1(6)
2 Distributed Data Management
7(18)
2.1 Distributed Systems
7(10)
2.1.1 Logical Time and Lamport Clocks
8(1)
2.1.2 Vector Clocks
9(1)
2.1.3 Mutual Exclusion and Quorums
10(2)
2.1.4 Leader Election
12(1)
2.1.5 Group Communication through Broadcast and Multicast
12(3)
2.1.6 The Consensus Problem
15(1)
2.1.7 CAP Theorem
16(1)
2.2 Peer to Peer Systems
17(1)
2.3 Database Systems
18(7)
2.3.1 Preliminaries
19(1)
2.3.2 Concurrency Control
20(1)
2.3.3 Recovery and Commitment
21(4)
3 Cloud Data Management: Early Trends
25(14)
3.1 Overview of Key-value Stores
25(1)
3.2 Design Choices and their Implications
26(4)
3.2.1 Data Model
27(1)
3.2.2 Data Distribution and Request Routing
27(1)
3.2.3 Cluster Management
28(1)
3.2.4 Fault-tolerance and Data Replication
29(1)
3.3 Key-Value Store System Examples
30(6)
3.3.1 Bigtable
31(2)
3.3.2 PNUTS
33(2)
3.3.3 Dynamo
35(1)
3.4 Discussion
36(3)
4 Transactions on Co-located Data
39(32)
4.1 Data or Ownership Co-location
40(8)
4.1.1 Leveraging Schema Patterns
40(4)
4.1.2 Access-driven Database Partitioning
44(1)
4.1.3 Application-specified Dynamic Partitioning
45(3)
4.2 Transaction Execution
48(1)
4.3 Data Storage
48(2)
4.3.1 Coupled Storage
48(1)
4.3.2 Decoupled Storage
49(1)
4.4 Replication
50(2)
4.4.1 Explicit Replication
51(1)
4.4.2 Implicit Replication
51(1)
4.5 A Survey of the Systems
52(19)
4.5.1 G-Store
52(4)
4.5.2 Elas TraS
56(2)
4.5.3 Cloud SQL Server
58(2)
4.5.4 Megastore
60(4)
4.5.5 Relational Cloud
64(1)
4.5.6 Hyder
65(3)
4.5.7 Deuteronomy
68(3)
5 Transactions on Distributed Data
71(12)
5.1 Database-like Functionality on Cloud Storage
71(4)
5.2 Transactional support for Geo-replicated Data
75(2)
5.3 Incremental Update Processing using Distributed Transactions
77(2)
5.4 Scalable Distributed Synchronization using Minitransactions
79(2)
5.5 Discussion
81(2)
6 Multi-tenant Database Systems
83(22)
6.1 Multi-tenancy Models
84(4)
6.1.1 Shared Hardware
84(1)
6.1.2 Shared Process
85(1)
6.1.3 Shared Table
86(1)
6.1.4 Analyzing the Models
86(2)
6.2 Database Elasticity in the Cloud
88(12)
6.2.1 Albatross: Live Migration for Shared Storage Data Stores
89(3)
6.2.2 Zephyr: Live Migration For Shared Nothing Data Stores
92(6)
6.2.3 Slacker: Live DBMS Instance Migration in Shared-nothing Model
98(2)
6.3 Autonomic Control for Database Workloads in the Cloud
100(4)
6.4 Discussion
104(1)
7 Concluding Remarks
105(2)
Bibliography 107(11)
Authors' Biographies 118