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SDN helps velocity in Big Data

SDN helps velocity in Big Data

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Currently, improving the performance of Big Data in general and velocity in particular is challenging due to the inefficiency of current network management, and the lack of coordination between the application layer and the network layer to achieve better scheduling decisions, which can improve the Big Data velocity performance. In this chapter, we discuss the role of recently emerged software defined networking (SDN) technology in helping the velocity dimension of Big Data. We start the chapter by providing a brief introduction of Big Data velocity and its characteristics and different modes of Big Data processing, followed by a brief explanation of how SDN can overcome the challenges of Big Data velocity. In the second part of the chapter, we describe in detail some proposed solutions which have applied SDN to improve Big Data performance in term of shortened processing time in different Big Data processing frameworks ranging from batch-oriented, MapReduce-based frameworks to real-time and stream-processing frameworks such as Spark and Storm. Finally, we conclude the chapter with a discussion of some open issues.

Chapter Contents:

  • 10.1 Introduction
  • 10.1.1 Big Data velocity
  • 10.1.2 Type of processing
  • 10.1.2.1 Batch processing
  • 10.1.2.2 Near real-time and real-time processing
  • 10.1.2.3 Stream processing
  • 10.2 How SDN can help velocity?
  • 10.3 Improving batch processing performance with SDN
  • 10.3.1 FlowComb
  • 10.3.2 Pythia
  • 10.3.3 Bandwidth-aware scheduler
  • 10.3.4 Phurti
  • 10.3.5 Cormorant
  • 10.3.6 SDN-based Hadoop for social TV analytics
  • 10.4 Improving real-time and stream processing performance with SDN
  • 10.4.1 Firebird
  • 10.4.2 Storm-based NIDS
  • 10.4.3 Crosslayer scheduler
  • 10.5 Summary
  • 10.5.1 Comparison table
  • 10.5.2 Generic SDN-based Big Data processing framework
  • 10.6 Open issues and research directions
  • 10.7 Conclusion
  • References

Inspec keywords: Big Data; telecommunication scheduling; parallel processing; software defined networking

Other keywords: network management; application layer; network layer; Spark; Big Data velocity performance; Big Data processing frameworks; scheduling decisions; batch-oriented MapReduce-based frameworks; stream-processing frameworks; software defined networking technology; Storm

Subjects: Parallel software; Computer communications; Computer networks and techniques; Data handling techniques

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