Table-of-Content

Chapter 1. Introduction to SDN (Ruslan L. Smelyanskiy et al., Moscow State University, Russia)
  1. Introduction 
    1. Crisis of traditional network architecture
  2. Data Centre (DC) anatomy
  3. Network routers and switches in traditional networks (notion of Control Plane vs Data Plane)
  4. Services in traditional networks
  5. Introduction to networks management systems
  6. Network infrastructure virtualisation in traditional networks: Basics (VLAN, overlays, protocols)
  7. Software-Defined Networks: How we can split Control Plane and Data Plane?
  8. Open Flow protocol and switching: Basics
  9. Programmable switching (SDN through overlays and virtual switching)
  10. SDN Controller, Northbound API, controller applications
  11. Open Issued and Challenges
  12. Summary and Conclusion


Chapter 2. SDN Implementations and Protocols (Cristian H. Benet, Karlstad University, Sweden)
  1. Introduction
  2. How SDN is implemented
    1. Implementation concept
    2. Existing SDN controllers
    3. Current SDN implementation using OpenDaylight and NOX controller
  3. Overview of OpenFlow Devices
    1. Software Switches
    2. Hardware Switches
  4. SDN Protocols
    1. ForCES
    2. OpenFlow
    3. Open vSwitch Database Management (OVSDB)
    4. Netconf
    5. MPLS/BGP
  5. Open Issues and Challenges
  6. Summary and Conclusion


Chapter 3: SDN Components and OpenFlow (Yanbiao Li et. al, Hunan University, China)
  1. Introduction Overview of the OpenFlow protocol
    1. Basic Concepts and Fundamental Abstraction of OpenFlow
    2. Top-Down view of SDN’s Layered Architectures
  2. Functionality Programming
    1. Functionality Abstraction
    2. Automatic Dependence Derivation
    3. Function Computation and Virtualization
    4. Northbound Interfaces of SDN
  3. Centralized Controllers
    1. Management Mechanisms
    2. Rule Placement and Optimization
    3. Multi-Controller Groups
    4. OpenFlow’s secure channels
  4. The OpenFlow Switch
    1. Southbound Interfaces of SDN
    2. Flow and Rule Definition
    3. The Matching Pipeline
    4. Design and Optimization of Tables
    5. Hardware Switch Development
    6. Software Switch Development
  5. Open Issues and Challenges
    1. Research Progress and Industrial Applications
    2. Future Work Directions


Chapter 4. SDN for Cloud Data Centres (Dimitrios P. Pezaros et al., University of Glasgow, UK)
  1. Overview
  2. Cloud Data Centre Topologies
    1. Clos/Fat-tree architectures
    2. Server-centric architectures
    3. Management topologies
  3. Software-Defined Networks for Cloud Data Centres
    1. Benefits of using SDN in Cloud DCs
    2. Current SDN deployments in Cloud DC
    3. SDN as a backbone of a converged (server/network) resource control plane
  4. Open research issues
    1. performance-based resource provisioning
    2. Network Function Virtualisation and SDN in DCs
    3. The future of network programmability
  5. Summary
  6. Open Issues and Challenges


Chapter 5. Introduction to Big-Data (Amir H. Payberah et al., Swedish ICT (SICS), Sweden)
  1. Definition of big data
  2. 2. Processing big data
    1. Programming models in big data processing platforms
    2. Processing data-at-rest (batch processing)
    3. Processing data-in-motion (stream processing)
    4. Processing linked-data (graph processing)
    5. High level interfaces
  3. Storing big data
    1. Distributed file systems
    2. NoSQL databases
    3. NewSQL databases
    4. Logging systems
  4. Resource management
  5. Open Issues and Challenges


Chapter 6. Big-Data Processing using Apache Spark and Hadoop (Koichi Shirahata et. al., Fujitsu Laboratories LTD, Japan)
  1. Introduction to Big-Data processing and Apache Spark and Hadoop
  2. Big-Data processing
    1. Big-Data processing models
    2. MapReduce-based Big-Data processing implementations
    3. Computing environments for Big-Data processing
  3. Apache Hadoop
    1. Overview of Hadoop
    2. Hadoop MapReduce
    3. Hadoop Distributed File System
    4. YARN
    5. Research activities on Hadoop
  4. Apache Spark
    1. Overview of Spark
    2. Resilient Distributed Datasets
    3. Spark DSL
    4. Using both Spark and Hadoop cooperatively
    5. Research activities on Spark
  5. Open Issues and Challenges
    1. Programming model choice for specific applications
    2. Implementation choice and utilization for specific computing environments
    3. Managing real time data and growing data
    4. Optimizations for emerging machine learning applications


Chapter 7: Big-Data stream data processing using Apache Storm (MReza Hoseiny et. al., University of Sydney, Australia) 
  1. Introduction to data stream processing
  2. Apache Storm components
  3. State of the Art Scheduling and Resource Allocation in Apache Storm
  4. MPC-based QoS-Aware Scheduling for Apache Storm
  5. Experimental Performance Analysis
  6. Open Issues and Challenges
  7. Conclusion


Chapter 8. Big-Data in Cloud Data Centres (Gunasekaran Manogaran et. al.,Vellore Institute of Technology, India)
  1. Introduction to Cloud Data Centres 
  2. Cloud and Big Data Integration
    1. Virtualization with respect of Cloud and Big data
    2. Cloud Computing Storage Solutions for Big Data
    3. Different Data Centres in Different Cloud Deployment Models
    4. Tools and Technologies of Cloud Computing and Big Data 
    5. Applications in Different Cloud Deployment Models
  3. Proposed Methodologies for Storing and Processing Big Data in Cloud Computing Environment
    1. Attributes of Stored Data in Cloud Environment
    2. Meta Cloud Cluster Platform with Proposed Load Balancing Algorithm
    3. Data Access from Different Cloud Data Centers 
    4. Performance Comparison of Proposed Load Balancing Algorithm 
    5. Data Security Integration Issues of Data Centers 
  4. Open Issues and Challenges
  5. Conclusion


Chapter 9. SDN helps Volume in Big-Data (Kyoomars N. Alizadeh et. al., Karlstad University, Sweden)
  1. Introduction to Volume in Big Data
  2. Volume of Big data (some examples) 
  3. Traffic Engineering using SDN
    1. Load balancing
    2. Traffic Monitoring
    3. Fault Tolerant
  4. 4. Data Sources
    1. Data source classification
    2. How to access data sources
  5. Applications and huge data volume
  6. Open issues in Volume in Big Data


Chapter 10. SDN helps Velocity in Big-Data (Giang Nguyen et al., Karlstad University, Sweden)
  1. Overview
    1. Big Data Velocity
    2. Challenges and Requirements
    3. Why SDN can help velocity?
  2. MapReduce and Hadoop
    1. Framework Overview
    2. How it works?
    3. Networking Bottlenecks
  3. Generic SDN-based MapReduce/Hadoop Architecture
    1. Architecture Overview
    2. Component Descriptions
  4. Job Acceleration with SDN in MapReduce/Hadoop
    1. FlowComb
    2. Pythia
    3. OFScheduler
    4. Phurti
    5. Summary
  5. Open Issues and Challenges


Chapter 11. SDN helps Value in Big-Data (Harald Gjermundrod et. al., University of Nicosia, Cyprus)
  1. Introduction (motivate how SDN could help to increase value in Big-Data)
  2. Value of Adaptable Network Platform 
  3. Value of Dark Data
  4. Value of Adaptable Data Flows
  5. Open Issues and Challenges


Chapter 12. SDN helps other Vs in Big-Data (Pradeeban Kathiravelu, et. al., University of Lisbon, Portugal)
  1. Introduction to other Vs in Big Data
  2. SDN for other Vs of Big Data
    1. SDN for Variety of Data
    2. SDN for Validity and Quality of Data
    3. SDN for Volatility of Data
    4. SDN for Veracity of Data
    5. SDN for Visibility of Data
  3. SDN for Big Data Diversity
    1. Use cases for SDN in Heterogeneous Data
    2. Architectures for Variety and Quality of Data
    3. QoS-Aware Big Data Applications
    4. Multi-Tenant SDN and Data Isolation 
  4. Open Issues and Challenges
  5. Summary and Conclusion


Chapter 13, SDN helps Big-Data to Scale (Kurt Tutschku et. al., Blekinge Institute of Technology, Sweden)


Chapter 14, SDN helps Big-Data to optimize storage (Ali R. Butt et. al., Virginia Tech, USA)
  1. Introduction to key-value stores and their architectures
  2. Related work, features and shortcomings
  3. Key-value store systems for datacenter applications
  4. Interactions between storage and software defined datacenters
  5. SDN-based efficient data management
  6. Rules of thumb of storage deployment in software-defined datacenters
  7. Results
  8. Open Issues and Challenges
  9. Summary and Conclusion


Chapter 15: SDN helps Big-Data to optimize access to data (Fengguang Song et. al., Indiana University-Purdue University Indianapolis, USA)
  1. Introduction 
    1. About Integration of High Performance Computing with Big Data Analytics
  2. State of the art and related work
  3. Performance analysis of message passing and parallel file system I/O
  4. SDN-based data transfer optimization between HPC and Big Data
  5. Analytical modelling based end-to-end time optimization
  6. Synthetic and real-world computational fluid dynamics (CFD) applications
  7. Experimental results
  8. Open issues and challenges
  9. Summary and Conclusion


Chapter 16, SDN helps Big-Data to become energy efficiency (Laurent Lefevre et. al., The French Institute for Research in Computer Science (INRIA), France)
  1. Introduction
  2. State 
    of 
    the 
    Art
    1. Distributed 
      Data
      Stream 
      Processing 
      for 
      Big 
      Data
    2. Network 
      Architectures 
      for 
      Stream 
      Processing
    3. SDN 
      and 
      Network 
      Provisioning
  3. Energy-Efficient 
    Routing 
    for 
    Big-Data 
    Services
    1. STREETE 
      -­ 
      SDN-Based 
      Energy-Efficient 
      Segment 
      Routing
    2. Energy-­Aware 
      Network 
      Provisioning 
      for 
      Big 
      Data 
      Stream 
      Processing
    3. Experimental 
      Results
  4. Future 
    Directions
  5. Conclusion


Chapter 17: SDN helps Big-Data to become fault tolerant (Abdelmounaam Rezgui et. al., New Mexico Tech, USA)
  1. Introduction
    1. Rationale and benefits of running big data workloads in cloud data centers
    2. Fault tolerance in cloud data centers
  2. Related work
  3. Failure scenarios for big data workloads in cloud data centers
  4. SDN-based fault tolerance for big data workloads in cloud data centers
  5. Open Issues and Challenges
  6. Summary and Conclusion


Chapter 18: Big-Data helps SDN with data protection and privacy (Lothar Fritsch et al., Karlstad University, Sweden)
  1. Big Data and SDN – collection and processing of data to improve performance
    1. The promise of Big Data in SND: Data collection, analysis, configuration change
  2. Data Protection Requirements and their implications for Big Data in SDN
    1. Data protection philosophy and requirements in Europe
      1. European data protection philosophy
      2. Essentials of lawful personal information processing
    2. Personal data in networking information 
      1. MACs, IPs, and other person-related information
      2. Traffic patterns
      3. Location information
      4. Routing and other management information
    3. Issues with Big Data processing and privacy in SDN
      1. Accumulation, Profiling, Aggregation
      2. Wrongful assumptions
      3. Transparency
      4. Liability and accountability for algorithms and data
  3. Recommendations for Privacy Design in SDN Big Data projects
    1. Privacy by Design
    2. Storage concepts
    3. Filtration, anonymization and data minimization
    4. Privacy-friendly data mining
    5. Purpose –binding and obligations management
    6. Data subject content management techniques
    7. Transparency, audibility and interveneability  preparedness
    8. Algorithmic accountability concepts
  4. Open Issues and Challenges
  5. Summary and Conclusion


Chapter 19. Big-Data helps SDN to detect intrusions and secure data flows (Li-Chun Wang et al., National Chiao Tung University, Taiwan)
  1. Security Issues of SDN
  2. Big Data Techniques for Security Threats in SDN
  3. QoS Consideration in SDN with Security Services
  4. Big Data Applications for Securing SDN
  5. Open Issues and Challenges


Chapter 20: Big-Data helps SDN forensics (Suleman Khan et al., University of Malaya, Malaysia)
  1. Introduction (General Overview about chapter)
  2. Motivation of SDN Forensics
  3. Requirements of SDN Forensics
  4. Big Data as a SDN Forensics Challenge
  5. Big data Analytics as a solution for SDN Forensics
  6. Open Issues and Challenges
  7. Conclusion


Chapter 21. Big-Data helps SDN to detect traffic patterns (Jianwu Wang et. al., University of Maryland, Baltimore County, USA)
  1. Introduction
  2. State of art of traffic pattern detection and adaptation in SDN
  3. Applicable Big Data techniques for traffic pattern detection and adaptation in SDN
  4. Proposed approach and architecture
  5. Experiments and evaluation
  6. Open Issues and Challenges
  7. Conclusion


Chapter 22: Big-Data helps SDN to optimize its controllers (allocated to Saeed Bastani et. al., Lund University, Sweden)


Chapter 23. Big-Data helps SDN to verify integrity of control/data planes (allocated to Yinglong Xia et. al., Huawei Research America, USA)
  1. Introduction
  2. Constructing graphs using SDN data
  3. Analyze SDN from graph perspective
  4. Experiments
  5. Open Issues and Challenges
  6. Conclusion


Chapter 24. Big-Data helps SDN to improve application specific quality of service (Thomas Zinner et al, University of Würzburg, Germany)
  1. Introduction to the Chapter 
  2. Background / Related Work
  3. Big Data Analytics and Context Information
  4. Classification of SDN-based context-aware networking approaches 
  5. Discussion of selected approaches including a quantification on the benefit in terms of QoE
  6. Discussion / Open Issues
  7. Conclusion


Chapter 25. Big-Data helps SDN to optimize routing tables (Felipe Estrada-Solano et. al., University of Cauca, Colombia)