| 1. |
- Alizadeh Noghani, Kyoomars, et al.
(författare)
-
SDN helps volume in Big Data
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - London : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 185-206
-
Bokkapitel (refereegranskat)abstract
- Both Big Data and SDN are described in detail in previous chapters. This chapter investigates how SDN architecture can leverage its unique features to mitigate the challenges of Big Data volume. Accordingly, first, we provide an overview of Big Data volume, its effects on the underlying network, and mention some potential SDN solutions to address the corresponding challenges. Second, we elaborate more on the network-monitoring, traffic-engineering, and fault-tolerant mechanisms which we believe they may help to address the challenges of Big Data volume. Finally, this chapter is concluded with some open issues.
|
|
| 2. |
- Cho, Daewoong, et al.
(författare)
-
Big Data helps SDN to optimize its controllers
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - London : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 389-408
-
Bokkapitel (refereegranskat)abstract
- In this chapter, we first discuss the basic features and recent issues of the SDN control plane, notably the controller element. Then, we present feasible ideas to address the SDN controller-related problems using Big Data analytics techniques. Accordingly, we propose that Big Data can help various aspects of the SDN controller to address scalability issue and resiliency problem. Furthermore, we proposed six applicable scenarios for optimizing the SDN controller using the Big Data analytics: (i) controller scale-up/out against network traffic concentration, (ii) controller scale-in for reduced energy usage, (iii) backup controller placement for fault tolerance and high availability, (iv) creating backup paths to improve fault tolerance, (v) controller placement for low latency between controllers and switches, and (vi) flow rule aggregation to reduce the SDN controller's traffic. Although real-world practices on optimizing SDN controllers using Big Data are absent in the literature, we expect scenarios we highlighted in this chapter to be highly applicable to optimize the SDN controller in the future.
|
|
| 3. |
|
|
| 4. |
- Hernandez Benet, Cristian, et al.
(författare)
-
SDN implementations and protocols
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 27-48
-
Bokkapitel (refereegranskat)abstract
- This chapter begins by explaining the main SDN concepts with the focus on a SDN controller. It presents the most important aspects to consider when we desire to go from traditional network to a SDN networks. We present an in-depth analysis of the most commonly used and modern SDN controllers and analyse the main features, capabilities and requirements of one of the presented controllers. OpenFlow is the standard leading in the market allowing the management of the forwarding plane devices such as routers or switches. While there are other standards with the same aim, OpenFlow has secured a position in the market and has been expanded rapidly. Therefore, an analysis is presented on a different OpenFlow compatible device for the implementation of an SDN network. This study encompasses both software and hardware solutions along with the scope of implementation or use of these devices. This chapter ends up presenting a description of OpenFlow protocol alternatives, a more detailed description of OpenFlow and its components and other wellknown southbound protocols involved for the management and configuration of the devices.
|
|
| 5. |
- Li, Yanbiao, et al.
(författare)
-
SDN components and OpenFlow
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - London : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 49-68
-
Bokkapitel (refereegranskat)abstract
- Today's Internet suffers from ever-increasing challenges in scalability, mobility, and security, which calls for deep innovations on network protocols and infrastructures. However, the distributed controlling mechanism, especially the bundle of control plane and the data plane within network devices, sharply restricts such evolutions. In response, the software-defined networking (SDN), an emerging networking paradigm, proposes to decouple the control and data planes, producing logically centralized controllers, simple yet efficient forwarding devices, and potential abilities in functionalities programming. This chapter presents a short yet comprehensive overview of SDN components and the OpenFlow protocol on basis of both classic and latest literatures. The topics range from fundamental building blocks, layered architectures, novel controlling mechanisms, and design principles and efforts of OpenFlow switches.
|
|
| 6. |
- Nguyen, Van-Giang, 1989-, et al.
(författare)
-
SDN helps velocity in Big Data
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - London : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 207-228
-
Bokkapitel (refereegranskat)abstract
- 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.
|
|
| 7. |
- Rezgui, Abdelmounaam, et al.
(författare)
-
SDN helps Big Data to become fault tolerant
- 2018. - 1
-
Ingår i: Big Data and Software Defined Networks. - London : IET Digital Library. - 9781785613043 - 9781785613050 ; , s. 319-336
-
Bokkapitel (refereegranskat)abstract
- SDN networks would have many advantages to be used as fault-tolerant Big Data infrastructures such as programmability and global network view which help monitor and control the network behavior adaptively and efficiently. This chapter studied a number of requirements to provide fault tolerance in networks that Big Data applications perform upon. First, we studied the key requirements to be fault tolerant. The network topology design is crucial to provide resiliency against node or link failure. Second, we mentioned the principle concepts of fault tolerance and elaborated on reactive and proactive methods as two common approaches to deal with the failures in networks. Third, the fault-tolerant mechanisms in SDN architecture and their advantages were elucidated. Consequently, we investigated a number of studies that leverage SDN to provide fault tolerance. Finally, this chapter was concluded by introducing open issues and challenges in SDN architecture to provide a perfect fault-tolerant network.
|
|
