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E-grāmata: Cloud Networking for Big Data

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  • Formāts: PDF+DRM
  • Sērija : Wireless Networks
  • Izdošanas datums: 09-Dec-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319247205
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Cloud Networking for Big DataPalielināt
  • Formāts: PDF+DRM
  • Sērija : Wireless Networks
  • Izdošanas datums: 09-Dec-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319247205
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This book introduces two basic big data processing paradigms for batch data and streaming data.  Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies.

The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing.

Cloud Networking for Big Data

targets professionals and researchers working in big data, networks, wireless communications and information technology.   Advanced-level students studying computer science and electrical engineering will also find this book valuable as a study guide. 

Part I Network Evolution Towards Cloud Networking
1 Background Introduction
3(14)
1.1 Networking Evolution
3(5)
1.2 Cloud Computing
8(6)
1.2.1 Infrastructure as a Service
9(1)
1.2.2 Platform as a Service
10(1)
1.2.3 Software as a Service
10(4)
1.3 Big Data
14(1)
1.3.1 Big Data Batch Processing
13(1)
1.3.2 Big Data Stream Processing
14(1)
1.4 Summary
15(2)
References
15(2)
2 Fundamental Concepts
17(16)
2.1 Software Defined Networking
17(4)
2.1.1 Architecture
17(2)
2.1.2 Floodlight
19(1)
2.1.3 Open Daylight
20(1)
2.1.4 Ryu SDN Framework
20(1)
2.2 Network Function Virtualization
21(2)
2.2.1 NFV in Data Centers
22(1)
2.2.2 NFV in Telecommunications
23(1)
2.3 Relationship Between SDN and NFV
23(1)
2.4 Big Data Batch Processing
24(4)
2.4.1 Hadoop
24(3)
2.4.2 DIYAD
27(1)
2.4.3 Spark
27(1)
2.5 Big Data Stream Processing
28(2)
2.5.1 Storm
29(1)
2.5.2 HAMR
30(1)
2.6 Summary
30(3)
References
31(2)
3 Cloud Networking
33(26)
3.1 Motivation: Fill the Gap Between Application and Network
33(1)
3.2 Cloud Networking Architecture
34(3)
3.2.1 Parser and Scheduler
34(2)
3.2.2 Network Manager
36(1)
3.2.3 Cloud Manager
36(1)
3.2.4 Monitor
37(1)
3.3 Design Issues
37(4)
3.3.1 Language Abstractions
37(1)
3.3.2 Performance Optimization
38(1)
3.3.3 Energy and Cost Optimization
39(1)
3.3.4 Flexible Data Management
40(1)
3.3.5 Stream Processing Aware Network Resource Management...
40(1)
3.3.6 Security
41(1)
3.4 Cloud Networking and Big Data Related Work Review
41(10)
3.4.1 Energy and Cost Reduction
41(1)
3.4.2 VM Placement
42(1)
3.4.3 Big Data Placement
43(1)
3.4.4 Big Data Stream Processing
44(1)
3.4.5 Big Data Aware Traffic Cost Optimization
45(1)
3.4.6 SDN Aware Optimization
46(5)
3.4.7 Network Function Virtualization
51(1)
3.5 Summary
51(8)
References
52(7)
Part II Cost Efficient Big Data Processing in Cloud Networking Enabled Data Centers
4 Cost Minimization for Big Data Processing in Geo-Distributed Data Centers
59(20)
4.1 Motivation and Problem Statement
59(2)
4.2 System Model
61(2)
4.2.1 Network Model
61(1)
4.2.2 Task Model
62(1)
4.3 Problem Formulation
63(5)
4.3.1 Constraints of Data and Task Placement
63(1)
4.3.2 Constraints of Data Loading
64(1)
4.3.3 Constraints of QoS Satisfaction
65(3)
4.3.4 An MINLP Formulation
68(1)
4.4 Linearization
68(2)
4.5 Performance Evaluation
70(5)
4.6 Summary
75(4)
References
77(2)
5 A General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-Distributed Data Centers
79(22)
5.1 Motivation and Problem Statement
79(4)
5.2 System Model
83(2)
5.2.1 Geo-Distributed DCs
83(1)
5.2.2 BDSP Task
83(2)
5.3 Problem Formulation
85(8)
5.3.1 VM Placement Constraints
85(3)
5.3.2 Flow Constraints
88(3)
5.3.3 A Joint MILP Formulation
91(2)
5.4 Algorithm Design
93(2)
5.5 Performance Evaluation
95(3)
5.6 Summary
98(3)
References
99(2)
6 Conclusion
101
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