A Review on Utilizing Big Data Techniques for Performance Improvement in Software Defined Networking | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 6, Volume 23, Issue 3, September 2023, Pages 68-79 PDF (1005.41 K) | ||
| Document Type: Review Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.23.3.6 | ||
| Authors | ||
| Sama Salam Samaan* ; Hassan Awheed Jeiad | ||
| Computer Engineering Department, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Traditional network abilities have a drastic shortage in the current networking world. Software-Defined Networking (SDN) is a revival development in the networking domain that provides separation of control and data planes, enlarges the data plane granularity, and simplifies the network devices. All these factors accelerate and automate the evolution of new services. However, when the SDN network topology becomes large, it poses new challenges in security, traffic management, and scalability due to the vast amounts of traffic data generated and the need for additional controllers to manage the significant number of networking devices. On the other hand, big data has become an attractive trend that can enhance network performance in general, specifically SDN. Both SDN and big data have gained great attraction from industry and academia. Traditionally, these two subjects have been studied separately in most of the preceding works. However, big data can impact the design and implementation of SDN thoroughly. This paper presents how big data can support SDN in various aspects, including intrusion detection, traffic monitoring, and controller scalability and resiliency. We suggest several approaches toward deeper cooperation between big data and SDN. | ||
| Keywords | ||
| SDN; big data; Spark; graph database; traffic engineering; intrusion detection | ||
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